Table of Contents
A low-level client representing Amazon SageMaker Service
Provides APIs for creating and managing Amazon SageMaker resources.
Other Resources:
import boto3
client = boto3.client('sagemaker')
These are the available methods:
Creates an association between the source and the destination. A source can be associated with multiple destinations, and a destination can be associated with multiple sources. An association is a lineage tracking entity. For more information, see Amazon SageMaker ML Lineage Tracking .
See also: AWS API Documentation
Request Syntax
response = client.add_association(
SourceArn='string',
DestinationArn='string',
AssociationType='ContributedTo'|'AssociatedWith'|'DerivedFrom'|'Produced'
)
[REQUIRED]
The ARN of the source.
[REQUIRED]
The Amazon Resource Name (ARN) of the destination.
The type of association. The following are suggested uses for each type. Amazon SageMaker places no restrictions on their use.
dict
Response Syntax
{
'SourceArn': 'string',
'DestinationArn': 'string'
}
Response Structure
(dict) --
SourceArn (string) --
The ARN of the source.
DestinationArn (string) --
The Amazon Resource Name (ARN) of the destination.
Exceptions
Adds or overwrites one or more tags for the specified Amazon SageMaker resource. You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints.
Each tag consists of a key and an optional value. Tag keys must be unique per resource. For more information about tags, see For more information, see Amazon Web Services Tagging Strategies .
Note
Tags that you add to a hyperparameter tuning job by calling this API are also added to any training jobs that the hyperparameter tuning job launches after you call this API, but not to training jobs that the hyperparameter tuning job launched before you called this API. To make sure that the tags associated with a hyperparameter tuning job are also added to all training jobs that the hyperparameter tuning job launches, add the tags when you first create the tuning job by specifying them in the Tags parameter of CreateHyperParameterTuningJob
Note
Tags that you add to a SageMaker Studio Domain or User Profile by calling this API are also added to any Apps that the Domain or User Profile launches after you call this API, but not to Apps that the Domain or User Profile launched before you called this API. To make sure that the tags associated with a Domain or User Profile are also added to all Apps that the Domain or User Profile launches, add the tags when you first create the Domain or User Profile by specifying them in the Tags parameter of CreateDomain or CreateUserProfile .
See also: AWS API Documentation
Request Syntax
response = client.add_tags(
ResourceArn='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
[REQUIRED]
The Amazon Resource Name (ARN) of the resource that you want to tag.
[REQUIRED]
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources .
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
dict
Response Syntax
{
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
}
Response Structure
(dict) --
Tags (list) --
A list of tags associated with the Amazon SageMaker resource.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
Associates a trial component with a trial. A trial component can be associated with multiple trials. To disassociate a trial component from a trial, call the DisassociateTrialComponent API.
See also: AWS API Documentation
Request Syntax
response = client.associate_trial_component(
TrialComponentName='string',
TrialName='string'
)
[REQUIRED]
The name of the component to associated with the trial.
[REQUIRED]
The name of the trial to associate with.
dict
Response Syntax
{
'TrialComponentArn': 'string',
'TrialArn': 'string'
}
Response Structure
(dict) --
TrialComponentArn (string) --
The ARN of the trial component.
TrialArn (string) --
The Amazon Resource Name (ARN) of the trial.
Exceptions
Check if an operation can be paginated.
Creates an action . An action is a lineage tracking entity that represents an action or activity. For example, a model deployment or an HPO job. Generally, an action involves at least one input or output artifact. For more information, see Amazon SageMaker ML Lineage Tracking .
Note
CreateAction can only be invoked from within an SageMaker managed environment. This includes SageMaker training jobs, processing jobs, transform jobs, and SageMaker notebooks. A call to CreateAction from outside one of these environments results in an error.
See also: AWS API Documentation
Request Syntax
response = client.create_action(
ActionName='string',
Source={
'SourceUri': 'string',
'SourceType': 'string',
'SourceId': 'string'
},
ActionType='string',
Description='string',
Status='Unknown'|'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
Properties={
'string': 'string'
},
MetadataProperties={
'CommitId': 'string',
'Repository': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string'
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
[REQUIRED]
The name of the action. Must be unique to your account in an Amazon Web Services Region.
[REQUIRED]
The source type, ID, and URI.
The URI of the source.
The type of the source.
The ID of the source.
[REQUIRED]
The action type.
A list of properties to add to the action.
Metadata properties of the tracking entity, trial, or trial component.
The commit ID.
The repository.
The entity this entity was generated by.
The project ID.
A list of tags to apply to the action.
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
dict
Response Syntax
{
'ActionArn': 'string'
}
Response Structure
(dict) --
ActionArn (string) --
The Amazon Resource Name (ARN) of the action.
Exceptions
Create a machine learning algorithm that you can use in Amazon SageMaker and list in the Amazon Web Services Marketplace.
See also: AWS API Documentation
Request Syntax
response = client.create_algorithm(
AlgorithmName='string',
AlgorithmDescription='string',
TrainingSpecification={
'TrainingImage': 'string',
'TrainingImageDigest': 'string',
'SupportedHyperParameters': [
{
'Name': 'string',
'Description': 'string',
'Type': 'Integer'|'Continuous'|'Categorical'|'FreeText',
'Range': {
'IntegerParameterRangeSpecification': {
'MinValue': 'string',
'MaxValue': 'string'
},
'ContinuousParameterRangeSpecification': {
'MinValue': 'string',
'MaxValue': 'string'
},
'CategoricalParameterRangeSpecification': {
'Values': [
'string',
]
}
},
'IsTunable': True|False,
'IsRequired': True|False,
'DefaultValue': 'string'
},
],
'SupportedTrainingInstanceTypes': [
'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge',
],
'SupportsDistributedTraining': True|False,
'MetricDefinitions': [
{
'Name': 'string',
'Regex': 'string'
},
],
'TrainingChannels': [
{
'Name': 'string',
'Description': 'string',
'IsRequired': True|False,
'SupportedContentTypes': [
'string',
],
'SupportedCompressionTypes': [
'None'|'Gzip',
],
'SupportedInputModes': [
'Pipe'|'File',
]
},
],
'SupportedTuningJobObjectiveMetrics': [
{
'Type': 'Maximize'|'Minimize',
'MetricName': 'string'
},
]
},
InferenceSpecification={
'Containers': [
{
'ContainerHostname': 'string',
'Image': 'string',
'ImageDigest': 'string',
'ModelDataUrl': 'string',
'ProductId': 'string',
'Environment': {
'string': 'string'
}
},
],
'SupportedTransformInstanceTypes': [
'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
],
'SupportedRealtimeInferenceInstanceTypes': [
'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge',
],
'SupportedContentTypes': [
'string',
],
'SupportedResponseMIMETypes': [
'string',
]
},
ValidationSpecification={
'ValidationRole': 'string',
'ValidationProfiles': [
{
'ProfileName': 'string',
'TrainingJobDefinition': {
'TrainingInputMode': 'Pipe'|'File',
'HyperParameters': {
'string': 'string'
},
'InputDataConfig': [
{
'ChannelName': 'string',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'AttributeNames': [
'string',
]
},
'FileSystemDataSource': {
'FileSystemId': 'string',
'FileSystemAccessMode': 'rw'|'ro',
'FileSystemType': 'EFS'|'FSxLustre',
'DirectoryPath': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File',
'ShuffleConfig': {
'Seed': 123
}
},
],
'OutputDataConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string'
},
'ResourceConfig': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123
}
},
'TransformJobDefinition': {
'MaxConcurrentTransforms': 123,
'MaxPayloadInMB': 123,
'BatchStrategy': 'MultiRecord'|'SingleRecord',
'Environment': {
'string': 'string'
},
'TransformInput': {
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'SplitType': 'None'|'Line'|'RecordIO'|'TFRecord'
},
'TransformOutput': {
'S3OutputPath': 'string',
'Accept': 'string',
'AssembleWith': 'None'|'Line',
'KmsKeyId': 'string'
},
'TransformResources': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'InstanceCount': 123,
'VolumeKmsKeyId': 'string'
}
}
},
]
},
CertifyForMarketplace=True|False,
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
[REQUIRED]
The name of the algorithm.
[REQUIRED]
Specifies details about training jobs run by this algorithm, including the following:
The Amazon ECR registry path of the Docker image that contains the training algorithm.
An MD5 hash of the training algorithm that identifies the Docker image used for training.
A list of the HyperParameterSpecification objects, that define the supported hyperparameters. This is required if the algorithm supports automatic model tuning.>
Defines a hyperparameter to be used by an algorithm.
The name of this hyperparameter. The name must be unique.
A brief description of the hyperparameter.
The type of this hyperparameter. The valid types are Integer , Continuous , Categorical , and FreeText .
The allowed range for this hyperparameter.
A IntegerParameterRangeSpecification object that defines the possible values for an integer hyperparameter.
The minimum integer value allowed.
The maximum integer value allowed.
A ContinuousParameterRangeSpecification object that defines the possible values for a continuous hyperparameter.
The minimum floating-point value allowed.
The maximum floating-point value allowed.
A CategoricalParameterRangeSpecification object that defines the possible values for a categorical hyperparameter.
The allowed categories for the hyperparameter.
Indicates whether this hyperparameter is tunable in a hyperparameter tuning job.
Indicates whether this hyperparameter is required.
The default value for this hyperparameter. If a default value is specified, a hyperparameter cannot be required.
A list of the instance types that this algorithm can use for training.
Indicates whether the algorithm supports distributed training. If set to false, buyers can't request more than one instance during training.
A list of MetricDefinition objects, which are used for parsing metrics generated by the algorithm.
Specifies a metric that the training algorithm writes to stderr or stdout . Amazon SageMakerhyperparameter tuning captures all defined metrics. You specify one metric that a hyperparameter tuning job uses as its objective metric to choose the best training job.
The name of the metric.
A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining Objective Metrics .
A list of ChannelSpecification objects, which specify the input sources to be used by the algorithm.
Defines a named input source, called a channel, to be used by an algorithm.
The name of the channel.
A brief description of the channel.
Indicates whether the channel is required by the algorithm.
The supported MIME types for the data.
The allowed compression types, if data compression is used.
The allowed input mode, either FILE or PIPE.
In FILE mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode.
In PIPE mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume.
A list of the metrics that the algorithm emits that can be used as the objective metric in a hyperparameter tuning job.
Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the Type parameter.
Whether to minimize or maximize the objective metric.
The name of the metric to use for the objective metric.
Specifies details about inference jobs that the algorithm runs, including the following:
The Amazon ECR registry path of the Docker image that contains the inference code.
Describes the Docker container for the model package.
The DNS host name for the Docker container.
The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by Amazon SageMaker, the inference code must meet Amazon SageMaker requirements. Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
An MD5 hash of the training algorithm that identifies the Docker image used for training.
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
Note
The model artifacts must be in an S3 bucket that is in the same region as the model package.
The Amazon Web Services Marketplace product ID of the model package.
The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
This parameter is required for unversioned models, and optional for versioned models.
A list of the instance types that are used to generate inferences in real-time.
This parameter is required for unversioned models, and optional for versioned models.
The supported MIME types for the input data.
The supported MIME types for the output data.
Specifies configurations for one or more training jobs and that Amazon SageMaker runs to test the algorithm's training code and, optionally, one or more batch transform jobs that Amazon SageMaker runs to test the algorithm's inference code.
The IAM roles that Amazon SageMaker uses to run the training jobs.
An array of AlgorithmValidationProfile objects, each of which specifies a training job and batch transform job that Amazon SageMaker runs to validate your algorithm.
Defines a training job and a batch transform job that Amazon SageMaker runs to validate your algorithm.
The data provided in the validation profile is made available to your buyers on Amazon Web Services Marketplace.
The name of the profile for the algorithm. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
The TrainingJobDefinition object that describes the training job that Amazon SageMaker runs to validate your algorithm.
The input mode used by the algorithm for the training job. For the input modes that Amazon SageMaker algorithms support, see Algorithms .
If an algorithm supports the File input mode, Amazon SageMaker downloads the training data from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an algorithm supports the Pipe input mode, Amazon SageMaker streams data directly from S3 to the container.
The hyperparameters used for the training job.
An array of Channel objects, each of which specifies an input source.
A channel is a named input source that training algorithms can consume.
The name of the channel.
The location of the channel data.
The S3 location of the data source that is associated with a channel.
If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects that match the specified key name prefix for model training.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for model training.
If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
If you want Amazon SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .
If you want Amazon SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.
A list of one or more attribute names to use that are found in a specified augmented manifest file.
The file system that is associated with a channel.
The file system id.
The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.
The file system type.
The full path to the directory to associate with the channel.
The MIME type of the data.
If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, Amazon SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO .
In File mode, leave this field unset or set it to None.
(Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , Amazon SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
To use a model for incremental training, choose File input model.
A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
Determines the shuffling order in ShuffleConfig value.
the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
If you use a KMS key ID or an alias of your master key, the Amazon SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. Amazon SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig . If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
Identifies the S3 path where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
The resources, including the ML compute instances and ML storage volumes, to use for model training.
The ML compute instance type.
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.
You must specify sufficient ML storage for your scenario.
Note
Amazon SageMaker supports only the General Purpose SSD (gp2) ML storage volume type.
Note
Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for training, Amazon SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can't request a VolumeSizeInGB greater than the total size of the local instance storage.
For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes .
The Amazon Web Services KMS key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be in any of the following formats:
Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts.
The maximum length of time, in seconds, that a training or compilation job can run. If the job does not complete during this time, Amazon SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
The default value is 1 day. The maximum value is 28 days.
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, Amazon SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
The TransformJobDefinition object that describes the transform job that Amazon SageMaker runs to validate your algorithm.
The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1.
The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata).
A string that determines the number of records included in a single mini-batch.
SingleRecord means only one record is used per mini-batch. MultiRecord means a mini-batch is set to contain as many records that can fit within the MaxPayloadInMB limit.
The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
A description of the input source and the way the transform job consumes it.
Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
The S3 location of the data source that is associated with a channel.
If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.
The following values are compatible: ManifestFile , S3Prefix
The following value is not compatible: AugmentedManifestFile
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None .
The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None , which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:
When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord , Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord , Amazon SageMaker sends individual records in each request.
Note
Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord . Padding is not removed if the value of BatchStrategy is set to MultiRecord .
For more information about RecordIO , see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord , see Consuming TFRecord data in the TensorFlow documentation.
Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, s3://bucket-name/key-name-prefix .
For every S3 object used as input for the transform job, batch transform stores the transformed data with an .``out`` suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at s3://bucket-name/input-name-prefix/dataset01/data.csv , batch transform stores the transformed data at s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out . Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .``out`` file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.
The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify None . To add a newline character at the end of every transformed record, specify Line .
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
Identifies the ML compute instances for the transform job.
The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or ml.m5.large instance types.
The number of ML compute instances to use in the transform job. For distributed transform jobs, specify a value greater than 1. The default value is 1 .
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be any of the following formats:
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources .
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
dict
Response Syntax
{
'AlgorithmArn': 'string'
}
Response Structure
(dict) --
AlgorithmArn (string) --
The Amazon Resource Name (ARN) of the new algorithm.
Creates a running app for the specified UserProfile. Supported apps are JupyterServer and KernelGateway . This operation is automatically invoked by Amazon SageMaker Studio upon access to the associated Domain, and when new kernel configurations are selected by the user. A user may have multiple Apps active simultaneously.
See also: AWS API Documentation
Request Syntax
response = client.create_app(
DomainId='string',
UserProfileName='string',
AppType='JupyterServer'|'KernelGateway'|'TensorBoard',
AppName='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
ResourceSpec={
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'
}
)
[REQUIRED]
The domain ID.
[REQUIRED]
The user profile name.
[REQUIRED]
The type of app. Supported apps are JupyterServer and KernelGateway . TensorBoard is not supported.
[REQUIRED]
The name of the app.
Each tag consists of a key and an optional value. Tag keys must be unique per resource.
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
The instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.
The ARN of the SageMaker image that the image version belongs to.
The ARN of the image version created on the instance.
The instance type that the image version runs on.
dict
Response Syntax
{
'AppArn': 'string'
}
Response Structure
(dict) --
AppArn (string) --
The Amazon Resource Name (ARN) of the app.
Exceptions
Creates a configuration for running a SageMaker image as a KernelGateway app. The configuration specifies the Amazon Elastic File System (EFS) storage volume on the image, and a list of the kernels in the image.
See also: AWS API Documentation
Request Syntax
response = client.create_app_image_config(
AppImageConfigName='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
KernelGatewayImageConfig={
'KernelSpecs': [
{
'Name': 'string',
'DisplayName': 'string'
},
],
'FileSystemConfig': {
'MountPath': 'string',
'DefaultUid': 123,
'DefaultGid': 123
}
}
)
[REQUIRED]
The name of the AppImageConfig. Must be unique to your account.
A list of tags to apply to the AppImageConfig.
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
The KernelGatewayImageConfig.
The specification of the Jupyter kernels in the image.
The specification of a Jupyter kernel.
The name of the Jupyter kernel in the image. This value is case sensitive.
The display name of the kernel.
The Amazon Elastic File System (EFS) storage configuration for a SageMaker image.
The path within the image to mount the user's EFS home directory. The directory should be empty. If not specified, defaults to /home/sagemaker-user .
The default POSIX user ID (UID). If not specified, defaults to 1000 .
The default POSIX group ID (GID). If not specified, defaults to 100 .
dict
Response Syntax
{
'AppImageConfigArn': 'string'
}
Response Structure
(dict) --
AppImageConfigArn (string) --
The Amazon Resource Name (ARN) of the AppImageConfig.
Exceptions
Creates an artifact . An artifact is a lineage tracking entity that represents a URI addressable object or data. Some examples are the S3 URI of a dataset and the ECR registry path of an image. For more information, see Amazon SageMaker ML Lineage Tracking .
Note
CreateArtifact can only be invoked from within an SageMaker managed environment. This includes SageMaker training jobs, processing jobs, transform jobs, and SageMaker notebooks. A call to CreateArtifact from outside one of these environments results in an error.
See also: AWS API Documentation
Request Syntax
response = client.create_artifact(
ArtifactName='string',
Source={
'SourceUri': 'string',
'SourceTypes': [
{
'SourceIdType': 'MD5Hash'|'S3ETag'|'S3Version'|'Custom',
'Value': 'string'
},
]
},
ArtifactType='string',
Properties={
'string': 'string'
},
MetadataProperties={
'CommitId': 'string',
'Repository': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string'
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
[REQUIRED]
The ID, ID type, and URI of the source.
The URI of the source.
A list of source types.
The ID and ID type of an artifact source.
The type of ID.
The ID.
[REQUIRED]
The artifact type.
A list of properties to add to the artifact.
Metadata properties of the tracking entity, trial, or trial component.
The commit ID.
The repository.
The entity this entity was generated by.
The project ID.
A list of tags to apply to the artifact.
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
dict
Response Syntax
{
'ArtifactArn': 'string'
}
Response Structure
(dict) --
ArtifactArn (string) --
The Amazon Resource Name (ARN) of the artifact.
Exceptions
Creates an Autopilot job.
Find the best performing model after you run an Autopilot job by calling .
For information about how to use Autopilot, see Automate Model Development with Amazon SageMaker Autopilot .
See also: AWS API Documentation
Request Syntax
response = client.create_auto_ml_job(
AutoMLJobName='string',
InputDataConfig=[
{
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix',
'S3Uri': 'string'
}
},
'CompressionType': 'None'|'Gzip',
'TargetAttributeName': 'string'
},
],
OutputDataConfig={
'KmsKeyId': 'string',
'S3OutputPath': 'string'
},
ProblemType='BinaryClassification'|'MulticlassClassification'|'Regression',
AutoMLJobObjective={
'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'
},
AutoMLJobConfig={
'CompletionCriteria': {
'MaxCandidates': 123,
'MaxRuntimePerTrainingJobInSeconds': 123,
'MaxAutoMLJobRuntimeInSeconds': 123
},
'SecurityConfig': {
'VolumeKmsKeyId': 'string',
'EnableInterContainerTrafficEncryption': True|False,
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
}
},
RoleArn='string',
GenerateCandidateDefinitionsOnly=True|False,
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
ModelDeployConfig={
'AutoGenerateEndpointName': True|False,
'EndpointName': 'string'
}
)
[REQUIRED]
Identifies an Autopilot job. The name must be unique to your account and is case-insensitive.
[REQUIRED]
An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to InputDataConfig supported by . Format(s) supported: CSV. Minimum of 500 rows.
A channel is a named input source that training algorithms can consume. For more information, see .
The data source for an AutoML channel.
The Amazon S3 location of the input data.
Note
The input data must be in CSV format and contain at least 500 rows.
The data type.
The URL to the Amazon S3 data source.
You can use Gzip or None . The default value is None .
The name of the target variable in supervised learning, usually represented by 'y'.
[REQUIRED]
Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.
The Amazon Web Services KMS encryption key ID.
The Amazon S3 output path. Must be 128 characters or less.
Defines the objective metric used to measure the predictive quality of an AutoML job. You provide an AutoMLJobObjective$MetricName and Autopilot infers whether to minimize or maximize it.
The name of the objective metric used to measure the predictive quality of a machine learning system. This metric is optimized during training to provide the best estimate for model parameter values from data.
Here are the options:
If you do not specify a metric explicitly, the default behavior is to automatically use:
Contains CompletionCriteria and SecurityConfig settings for the AutoML job.
How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.
The maximum number of times a training job is allowed to run.
The maximum time, in seconds, a training job is allowed to run as part of an AutoML job.
The maximum runtime, in seconds, an AutoML job has to complete.
The security configuration for traffic encryption or Amazon VPC settings.
The key used to encrypt stored data.
Whether to use traffic encryption between the container layers.
The VPC configuration.
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
[REQUIRED]
The ARN of the role that is used to access the data.
Each tag consists of a key and an optional value. Tag keys must be unique per resource.
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.
Set to True to automatically generate an endpoint name for a one-click Autopilot model deployment; set to False otherwise. The default value is False .
Note
If you set AutoGenerateEndpointName to True , do not specify the EndpointName ; otherwise a 400 error is thrown.
Specifies the endpoint name to use for a one-click Autopilot model deployment if the endpoint name is not generated automatically.
Note
Specify the EndpointName if and only if you set AutoGenerateEndpointName to False ; otherwise a 400 error is thrown.
dict
Response Syntax
{
'AutoMLJobArn': 'string'
}
Response Structure
(dict) --
AutoMLJobArn (string) --
The unique ARN that is assigned to the AutoML job when it is created.
Exceptions
Creates a Git repository as a resource in your Amazon SageMaker account. You can associate the repository with notebook instances so that you can use Git source control for the notebooks you create. The Git repository is a resource in your Amazon SageMaker account, so it can be associated with more than one notebook instance, and it persists independently from the lifecycle of any notebook instances it is associated with.
The repository can be hosted either in Amazon Web Services CodeCommit or in any other Git repository.
See also: AWS API Documentation
Request Syntax
response = client.create_code_repository(
CodeRepositoryName='string',
GitConfig={
'RepositoryUrl': 'string',
'Branch': 'string',
'SecretArn': 'string'
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
[REQUIRED]
The name of the Git repository. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
[REQUIRED]
Specifies details about the repository, including the URL where the repository is located, the default branch, and credentials to use to access the repository.
The URL where the Git repository is located.
The default branch for the Git repository.
The Amazon Resource Name (ARN) of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the git repository. The secret must have a staging label of AWSCURRENT and must be in the following format:
{"username": *UserName* , "password": *Password* }
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources .
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
dict
Response Syntax
{
'CodeRepositoryArn': 'string'
}
Response Structure
(dict) --
CodeRepositoryArn (string) --
The Amazon Resource Name (ARN) of the new repository.
Starts a model compilation job. After the model has been compiled, Amazon SageMaker saves the resulting model artifacts to an Amazon Simple Storage Service (Amazon S3) bucket that you specify.
If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts with Amazon Web Services IoT Greengrass. In that case, deploy them as an ML resource.
In the request body, you provide the following:
You can also provide a Tag to track the model compilation job's resource use and costs. The response body contains the CompilationJobArn for the compiled job.
To stop a model compilation job, use StopCompilationJob . To get information about a particular model compilation job, use DescribeCompilationJob . To get information about multiple model compilation jobs, use ListCompilationJobs .
See also: AWS API Documentation
Request Syntax
response = client.create_compilation_job(
CompilationJobName='string',
RoleArn='string',
InputConfig={
'S3Uri': 'string',
'DataInputConfig': 'string',
'Framework': 'TENSORFLOW'|'KERAS'|'MXNET'|'ONNX'|'PYTORCH'|'XGBOOST'|'TFLITE'|'DARKNET'|'SKLEARN',
'FrameworkVersion': 'string'
},
OutputConfig={
'S3OutputLocation': 'string',
'TargetDevice': 'lambda'|'ml_m4'|'ml_m5'|'ml_c4'|'ml_c5'|'ml_p2'|'ml_p3'|'ml_g4dn'|'ml_inf1'|'ml_eia2'|'jetson_tx1'|'jetson_tx2'|'jetson_nano'|'jetson_xavier'|'rasp3b'|'imx8qm'|'deeplens'|'rk3399'|'rk3288'|'aisage'|'sbe_c'|'qcs605'|'qcs603'|'sitara_am57x'|'amba_cv22'|'amba_cv25'|'x86_win32'|'x86_win64'|'coreml'|'jacinto_tda4vm',
'TargetPlatform': {
'Os': 'ANDROID'|'LINUX',
'Arch': 'X86_64'|'X86'|'ARM64'|'ARM_EABI'|'ARM_EABIHF',
'Accelerator': 'INTEL_GRAPHICS'|'MALI'|'NVIDIA'
},
'CompilerOptions': 'string',
'KmsKeyId': 'string'
},
VpcConfig={
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
StoppingCondition={
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
[REQUIRED]
A name for the model compilation job. The name must be unique within the Amazon Web Services Region and within your Amazon Web Services account.
[REQUIRED]
The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.
During model compilation, Amazon SageMaker needs your permission to:
You grant permissions for all of these tasks to an IAM role. To pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission. For more information, see Amazon SageMaker Roles.
[REQUIRED]
Provides information about the location of input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.
The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. The data inputs are InputConfig$Framework specific.
DataInputConfig supports the following parameters for CoreML OutputConfig$TargetDevice (ML Model format):
CoreML ClassifierConfig parameters can be specified using OutputConfig$CompilerOptions . CoreML converter supports Tensorflow and PyTorch models. CoreML conversion examples:
Depending on the model format, DataInputConfig requires the following parameters for ml_eia2 OutputConfig:TargetDevice .
Identifies the framework in which the model was trained. For example: TENSORFLOW.
Specifies the framework version to use.
This API field is only supported for PyTorch framework versions 1.4 , 1.5 , and 1.6 for cloud instance target devices: ml_c4 , ml_c5 , ml_m4 , ml_m5 , ml_p2 , ml_p3 , and ml_g4dn .
[REQUIRED]
Provides information about the output location for the compiled model and the target device the model runs on.
Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed. Alternatively, you can specify OS, architecture, and accelerator using TargetPlatform fields. It can be used instead of TargetPlatform .
Contains information about a target platform that you want your model to run on, such as OS, architecture, and accelerators. It is an alternative of TargetDevice .
The following examples show how to configure the TargetPlatform and CompilerOptions JSON strings for popular target platforms:
Specifies a target platform OS.
Specifies a target platform architecture.
Specifies a target platform accelerator (optional).
Specifies additional parameters for compiler options in JSON format. The compiler options are TargetPlatform specific. It is required for NVIDIA accelerators and highly recommended for CPU compilations. For any other cases, it is optional to specify CompilerOptions.
For example, {"dtype" : "float32"} .
For example, {'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver': '10.1'}
For example: {"precision_mode": "FP32", "output_names": ["output:0"]}
The Amazon Web Services Key Management Service key (Amazon Web Services KMS) that Amazon SageMaker uses to encrypt your output models with Amazon S3 server-side encryption after compilation job. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KmsKeyId can be any of the following formats:
A VpcConfig object that specifies the VPC that you want your compilation job to connect to. Control access to your models by configuring the VPC. For more information, see Protect Compilation Jobs by Using an Amazon Virtual Private Cloud .
The VPC security group IDs. IDs have the form of sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.
The ID of the subnets in the VPC that you want to connect the compilation job to for accessing the model in Amazon S3.
[REQUIRED]
Specifies a limit to how long a model compilation job can run. When the job reaches the time limit, Amazon SageMaker ends the compilation job. Use this API to cap model training costs.
The maximum length of time, in seconds, that a training or compilation job can run. If the job does not complete during this time, Amazon SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
The default value is 1 day. The maximum value is 28 days.
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, Amazon SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources .
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
dict
Response Syntax
{
'CompilationJobArn': 'string'
}
Response Structure
(dict) --
CompilationJobArn (string) --
If the action is successful, the service sends back an HTTP 200 response. Amazon SageMaker returns the following data in JSON format:
Exceptions
Creates a context . A context is a lineage tracking entity that represents a logical grouping of other tracking or experiment entities. Some examples are an endpoint and a model package. For more information, see Amazon SageMaker ML Lineage Tracking .
Note
CreateContext can only be invoked from within an SageMaker managed environment. This includes SageMaker training jobs, processing jobs, transform jobs, and SageMaker notebooks. A call to CreateContext from outside one of these environments results in an error.
See also: AWS API Documentation
Request Syntax
response = client.create_context(
ContextName='string',
Source={
'SourceUri': 'string',
'SourceType': 'string',
'SourceId': 'string'
},
ContextType='string',
Description='string',
Properties={
'string': 'string'
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
[REQUIRED]
The name of the context. Must be unique to your account in an Amazon Web Services Region.
[REQUIRED]
The source type, ID, and URI.
The URI of the source.
The type of the source.
The ID of the source.
[REQUIRED]
The context type.
A list of properties to add to the context.
A list of tags to apply to the context.
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
dict
Response Syntax
{
'ContextArn': 'string'
}
Response Structure
(dict) --
ContextArn (string) --
The Amazon Resource Name (ARN) of the context.
Exceptions
Creates a definition for a job that monitors data quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor .
See also: AWS API Documentation
Request Syntax
response = client.create_data_quality_job_definition(
JobDefinitionName='string',
DataQualityBaselineConfig={
'BaseliningJobName': 'string',
'ConstraintsResource': {
'S3Uri': 'string'
},
'StatisticsResource': {
'S3Uri': 'string'
}
},
DataQualityAppSpecification={
'ImageUri': 'string',
'ContainerEntrypoint': [
'string',
],
'ContainerArguments': [
'string',
],
'RecordPreprocessorSourceUri': 'string',
'PostAnalyticsProcessorSourceUri': 'string',
'Environment': {
'string': 'string'
}
},
DataQualityJobInput={
'EndpointInput': {
'EndpointName': 'string',
'LocalPath': 'string',
'S3InputMode': 'Pipe'|'File',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'FeaturesAttribute': 'string',
'InferenceAttribute': 'string',
'ProbabilityAttribute': 'string',
'ProbabilityThresholdAttribute': 123.0,
'StartTimeOffset': 'string',
'EndTimeOffset': 'string'
}
},
DataQualityJobOutputConfig={
'MonitoringOutputs': [
{
'S3Output': {
'S3Uri': 'string',
'LocalPath': 'string',
'S3UploadMode': 'Continuous'|'EndOfJob'
}
},
],
'KmsKeyId': 'string'
},
JobResources={
'ClusterConfig': {
'InstanceCount': 123,
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
}
},
NetworkConfig={
'EnableInterContainerTrafficEncryption': True|False,
'EnableNetworkIsolation': True|False,
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
},
RoleArn='string',
StoppingCondition={
'MaxRuntimeInSeconds': 123
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
[REQUIRED]
The name for the monitoring job definition.
Configures the constraints and baselines for the monitoring job.
The name of the job that performs baselining for the data quality monitoring job.
The constraints resource for a monitoring job.
The Amazon S3 URI for the constraints resource.
The statistics resource for a monitoring job.
The Amazon S3 URI for the statistics resource.
[REQUIRED]
Specifies the container that runs the monitoring job.
The container image that the data quality monitoring job runs.
The entrypoint for a container used to run a monitoring job.
The arguments to send to the container that the monitoring job runs.
An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flatted json so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.
An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.
Sets the environment variables in the container that the monitoring job runs.
[REQUIRED]
A list of inputs for the monitoring job. Currently endpoints are supported as monitoring inputs.
Input object for the endpoint
An endpoint in customer's account which has enabled DataCaptureConfig enabled.
Path to the filesystem where the endpoint data is available to the container.
Whether the Pipe or File is used as the input mode for transfering data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .
Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated
The attributes of the input data that are the input features.
The attribute of the input data that represents the ground truth label.
In a classification problem, the attribute that represents the class probability.
The threshold for the class probability to be evaluated as a positive result.
If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
[REQUIRED]
The output configuration for monitoring jobs.
Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.
The output object for a monitoring job.
The Amazon S3 storage location where the results of a monitoring job are saved.
A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.
The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.
Whether to upload the results of the monitoring job continuously or after the job completes.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.
[REQUIRED]
Identifies the resources to deploy for a monitoring job.
The configuration for the cluster resources used to run the processing job.
The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
The ML compute instance type for the processing job.
The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job.
Specifies networking configuration for the monitoring job.
Whether to encrypt all communications between the instances used for the monitoring jobs. Choose True to encrypt communications. Encryption provides greater security for distributed jobs, but the processing might take longer.
Whether to allow inbound and outbound network calls to and from the containers used for the monitoring job.
Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud .
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
[REQUIRED]
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
A time limit for how long the monitoring job is allowed to run before stopping.
The maximum runtime allowed in seconds.
Note
The MaxRuntimeInSeconds cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.
(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide .
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
dict
Response Syntax
{
'JobDefinitionArn': 'string'
}
Response Structure
(dict) --
JobDefinitionArn (string) --
The Amazon Resource Name (ARN) of the job definition.
Exceptions
Creates a device fleet.
See also: AWS API Documentation
Request Syntax
response = client.create_device_fleet(
DeviceFleetName='string',
RoleArn='string',
Description='string',
OutputConfig={
'S3OutputLocation': 'string',
'KmsKeyId': 'string',
'PresetDeploymentType': 'GreengrassV2Component',
'PresetDeploymentConfig': 'string'
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
EnableIotRoleAlias=True|False
)
[REQUIRED]
The name of the fleet that the device belongs to.
[REQUIRED]
The output configuration for storing sample data collected by the fleet.
The Amazon Simple Storage (S3) bucker URI.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume after compilation job. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account.
The deployment type SageMaker Edge Manager will create. Currently only supports Amazon Web Services IoT Greengrass Version 2 components.
The configuration used to create deployment artifacts. Specify configuration options with a JSON string. The available configuration options for each type are:
Note
Amazon Web Services IoT Greengrass uses semantic versions for components. Semantic versions follow a*major.minor.patch* number system. For example, version 1.0.0 represents the first major release for a component. For more information, see the semantic version specification .
Creates tags for the specified fleet.
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
Whether to create an Amazon Web Services IoT Role Alias during device fleet creation. The name of the role alias generated will match this pattern: "SageMakerEdge-{DeviceFleetName}".
For example, if your device fleet is called "demo-fleet", the name of the role alias will be "SageMakerEdge-demo-fleet".
None
Exceptions
Creates a Domain used by Amazon SageMaker Studio. A domain consists of an associated Amazon Elastic File System (EFS) volume, a list of authorized users, and a variety of security, application, policy, and Amazon Virtual Private Cloud (VPC) configurations. An Amazon Web Services account is limited to one domain per region. Users within a domain can share notebook files and other artifacts with each other.
EFS storage
When a domain is created, an EFS volume is created for use by all of the users within the domain. Each user receives a private home directory within the EFS volume for notebooks, Git repositories, and data files.
SageMaker uses the Amazon Web Services Key Management Service (Amazon Web Services KMS) to encrypt the EFS volume attached to the domain with an Amazon Web Services managed customer master key (CMK) by default. For more control, you can specify a customer managed CMK. For more information, see Protect Data at Rest Using Encryption .
VPC configuration
All SageMaker Studio traffic between the domain and the EFS volume is through the specified VPC and subnets. For other Studio traffic, you can specify the AppNetworkAccessType parameter. AppNetworkAccessType corresponds to the network access type that you choose when you onboard to Studio. The following options are available:
Warning
NFS traffic over TCP on port 2049 needs to be allowed in both inbound and outbound rules in order to launch a SageMaker Studio app successfully.
For more information, see Connect SageMaker Studio Notebooks to Resources in a VPC .
See also: AWS API Documentation
Request Syntax
response = client.create_domain(
DomainName='string',
AuthMode='SSO'|'IAM',
DefaultUserSettings={
'ExecutionRole': 'string',
'SecurityGroups': [
'string',
],
'SharingSettings': {
'NotebookOutputOption': 'Allowed'|'Disabled',
'S3OutputPath': 'string',
'S3KmsKeyId': 'string'
},
'JupyterServerAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'
}
},
'KernelGatewayAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
]
},
'TensorBoardAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'
}
}
},
SubnetIds=[
'string',
],
VpcId='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
AppNetworkAccessType='PublicInternetOnly'|'VpcOnly',
HomeEfsFileSystemKmsKeyId='string',
KmsKeyId='string'
)
[REQUIRED]
A name for the domain.
[REQUIRED]
The mode of authentication that members use to access the domain.
[REQUIRED]
The default settings to use to create a user profile when UserSettings isn't specified in the call to the CreateUserProfile API.
SecurityGroups is aggregated when specified in both calls. For all other settings in UserSettings , the values specified in CreateUserProfile take precedence over those specified in CreateDomain .
The execution role for the user.
The security groups for the Amazon Virtual Private Cloud (VPC) that Studio uses for communication.
Optional when the CreateDomain.AppNetworkAccessType parameter is set to PublicInternetOnly .
Required when the CreateDomain.AppNetworkAccessType parameter is set to VpcOnly .
Amazon SageMaker adds a security group to allow NFS traffic from SageMaker Studio. Therefore, the number of security groups that you can specify is one less than the maximum number shown.
Specifies options for sharing SageMaker Studio notebooks.
Whether to include the notebook cell output when sharing the notebook. The default is Disabled .
When NotebookOutputOption is Allowed , the Amazon S3 bucket used to store the shared notebook snapshots.
When NotebookOutputOption is Allowed , the Amazon Web Services Key Management Service (KMS) encryption key ID used to encrypt the notebook cell output in the Amazon S3 bucket.
The Jupyter server's app settings.
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app.
The ARN of the SageMaker image that the image version belongs to.
The ARN of the image version created on the instance.
The instance type that the image version runs on.
The kernel gateway app settings.
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.
The ARN of the SageMaker image that the image version belongs to.
The ARN of the image version created on the instance.
The instance type that the image version runs on.
A list of custom SageMaker images that are configured to run as a KernelGateway app.
A custom SageMaker image. For more information, see Bring your own SageMaker image .
The name of the CustomImage. Must be unique to your account.
The version number of the CustomImage.
The name of the AppImageConfig.
The TensorBoard app settings.
The default instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.
The ARN of the SageMaker image that the image version belongs to.
The ARN of the image version created on the instance.
The instance type that the image version runs on.
[REQUIRED]
The VPC subnets that Studio uses for communication.
[REQUIRED]
The ID of the Amazon Virtual Private Cloud (VPC) that Studio uses for communication.
Tags to associated with the Domain. Each tag consists of a key and an optional value. Tag keys must be unique per resource. Tags are searchable using the Search API.
Tags that you specify for the Domain are also added to all Apps that the Domain launches.
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
Specifies the VPC used for non-EFS traffic. The default value is PublicInternetOnly .
dict
Response Syntax
{
'DomainArn': 'string',
'Url': 'string'
}
Response Structure
(dict) --
DomainArn (string) --
The Amazon Resource Name (ARN) of the created domain.
Url (string) --
The URL to the created domain.
Exceptions
Starts a SageMaker Edge Manager model packaging job. Edge Manager will use the model artifacts from the Amazon Simple Storage Service bucket that you specify. After the model has been packaged, Amazon SageMaker saves the resulting artifacts to an S3 bucket that you specify.
See also: AWS API Documentation
Request Syntax
response = client.create_edge_packaging_job(
EdgePackagingJobName='string',
CompilationJobName='string',
ModelName='string',
ModelVersion='string',
RoleArn='string',
OutputConfig={
'S3OutputLocation': 'string',
'KmsKeyId': 'string',
'PresetDeploymentType': 'GreengrassV2Component',
'PresetDeploymentConfig': 'string'
},
ResourceKey='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
[REQUIRED]
The name of the edge packaging job.
[REQUIRED]
The name of the SageMaker Neo compilation job that will be used to locate model artifacts for packaging.
[REQUIRED]
The name of the model.
[REQUIRED]
The version of the model.
[REQUIRED]
The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to download and upload the model, and to contact SageMaker Neo.
[REQUIRED]
Provides information about the output location for the packaged model.
The Amazon Simple Storage (S3) bucker URI.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume after compilation job. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account.
The deployment type SageMaker Edge Manager will create. Currently only supports Amazon Web Services IoT Greengrass Version 2 components.
The configuration used to create deployment artifacts. Specify configuration options with a JSON string. The available configuration options for each type are:
Note
Amazon Web Services IoT Greengrass uses semantic versions for components. Semantic versions follow a*major.minor.patch* number system. For example, version 1.0.0 represents the first major release for a component. For more information, see the semantic version specification .
Creates tags for the packaging job.
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
None
Exceptions
Creates an endpoint using the endpoint configuration specified in the request. Amazon SageMaker uses the endpoint to provision resources and deploy models. You create the endpoint configuration with the CreateEndpointConfig API.
Use this API to deploy models using Amazon SageMaker hosting services.
For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (Amazon Web Services SDK for Python (Boto 3)).
Note
You must not delete an EndpointConfig that is in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. To update an endpoint, you must create a new EndpointConfig .
The endpoint name must be unique within an Amazon Web Services Region in your Amazon Web Services account.
When it receives the request, Amazon SageMaker creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them.
Note
When you call CreateEndpoint , a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting ` Eventually Consistent Reads https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/HowItWorks.ReadConsistency.html`__ , the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.
When Amazon SageMaker receives the request, it sets the endpoint status to Creating . After it creates the endpoint, it sets the status to InService . Amazon SageMaker can then process incoming requests for inferences. To check the status of an endpoint, use the DescribeEndpoint API.
If any of the models hosted at this endpoint get model data from an Amazon S3 location, Amazon SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provided. Amazon Web Services STS is activated in your IAM user account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide .
Note
To add the IAM role policies for using this API operation, go to the IAM console , and choose Roles in the left navigation pane. Search the IAM role that you want to grant access to use the CreateEndpoint and CreateEndpointConfig API operations, add the following policies to the role.
See also: AWS API Documentation
Request Syntax
response = client.create_endpoint(
EndpointName='string',
EndpointConfigName='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
[REQUIRED]
The name of the endpoint.The name must be unique within an Amazon Web Services Region in your Amazon Web Services account. The name is case-insensitive in CreateEndpoint , but the case is preserved and must be matched in .
[REQUIRED]
The name of an endpoint configuration. For more information, see CreateEndpointConfig .
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources .
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
dict
Response Syntax
{
'EndpointArn': 'string'
}
Response Structure
(dict) --
EndpointArn (string) --
The Amazon Resource Name (ARN) of the endpoint.
Exceptions
Creates an endpoint configuration that Amazon SageMaker hosting services uses to deploy models. In the configuration, you identify one or more models, created using the CreateModel API, to deploy and the resources that you want Amazon SageMaker to provision. Then you call the CreateEndpoint API.
Note
Use this API if you want to use Amazon SageMaker hosting services to deploy models into production.
In the request, you define a ProductionVariant , for each model that you want to deploy. Each ProductionVariant parameter also describes the resources that you want Amazon SageMaker to provision. This includes the number and type of ML compute instances to deploy.
If you are hosting multiple models, you also assign a VariantWeight to specify how much traffic you want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign traffic weight 2 for model A and 1 for model B. Amazon SageMaker distributes two-thirds of the traffic to Model A, and one-third to model B.
For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (Amazon Web Services SDK for Python (Boto 3)).
Note
When you call CreateEndpoint , a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting ` Eventually Consistent Reads https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/HowItWorks.ReadConsistency.html`__ , the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.
See also: AWS API Documentation
Request Syntax
response = client.create_endpoint_config(
EndpointConfigName='string',
ProductionVariants=[
{
'VariantName': 'string',
'ModelName': 'string',
'InitialInstanceCount': 123,
'InstanceType': 'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge',
'InitialVariantWeight': ...,
'AcceleratorType': 'ml.eia1.medium'|'ml.eia1.large'|'ml.eia1.xlarge'|'ml.eia2.medium'|'ml.eia2.large'|'ml.eia2.xlarge',
'CoreDumpConfig': {
'DestinationS3Uri': 'string',
'KmsKeyId': 'string'
}
},
],
DataCaptureConfig={
'EnableCapture': True|False,
'InitialSamplingPercentage': 123,
'DestinationS3Uri': 'string',
'KmsKeyId': 'string',
'CaptureOptions': [
{
'CaptureMode': 'Input'|'Output'
},
],
'CaptureContentTypeHeader': {
'CsvContentTypes': [
'string',
],
'JsonContentTypes': [
'string',
]
}
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
KmsKeyId='string'
)
[REQUIRED]
The name of the endpoint configuration. You specify this name in a CreateEndpoint request.
[REQUIRED]
An list of ProductionVariant objects, one for each model that you want to host at this endpoint.
Identifies a model that you want to host and the resources chosen to deploy for hosting it. If you are deploying multiple models, tell Amazon SageMaker how to distribute traffic among the models by specifying variant weights.
The name of the production variant.
The name of the model that you want to host. This is the name that you specified when creating the model.
Number of instances to launch initially.
The ML compute instance type.
Determines initial traffic distribution among all of the models that you specify in the endpoint configuration. The traffic to a production variant is determined by the ratio of the VariantWeight to the sum of all VariantWeight values across all ProductionVariants. If unspecified, it defaults to 1.0.
The size of the Elastic Inference (EI) instance to use for the production variant. EI instances provide on-demand GPU computing for inference. For more information, see Using Elastic Inference in Amazon SageMaker .
Specifies configuration for a core dump from the model container when the process crashes.
The Amazon S3 bucket to send the core dump to.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the core dump data at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
If you use a KMS key ID or an alias of your master key, the Amazon SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. Amazon SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig . If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateEndpoint and UpdateEndpoint requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources .
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint.
The KmsKeyId can be any of the following formats:
The KMS key policy must grant permission to the IAM role that you specify in your CreateEndpoint , UpdateEndpoint requests. For more information, refer to the Amazon Web Services Key Management Service section`Using Key Policies in Amazon Web Services KMS <https://docs.aws.amazon.com/kms/latest/developerguide/key-policies.html>`__
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a KmsKeyId when using an instance type with local storage. If any of the models that you specify in the ProductionVariants parameter use nitro-based instances with local storage, do not specify a value for the KmsKeyId parameter. If you specify a value for KmsKeyId when using any nitro-based instances with local storage, the call to CreateEndpointConfig fails.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
dict
Response Syntax
{
'EndpointConfigArn': 'string'
}
Response Structure
(dict) --
EndpointConfigArn (string) --
The Amazon Resource Name (ARN) of the endpoint configuration.
Exceptions
Creates an SageMaker experiment . An experiment is a collection of trials that are observed, compared and evaluated as a group. A trial is a set of steps, called trial components , that produce a machine learning model.
The goal of an experiment is to determine the components that produce the best model. Multiple trials are performed, each one isolating and measuring the impact of a change to one or more inputs, while keeping the remaining inputs constant.
When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK.
You can add tags to experiments, trials, trial components and then use the Search API to search for the tags.
To add a description to an experiment, specify the optional Description parameter. To add a description later, or to change the description, call the UpdateExperiment API.
To get a list of all your experiments, call the ListExperiments API. To view an experiment's properties, call the DescribeExperiment API. To get a list of all the trials associated with an experiment, call the ListTrials API. To create a trial call the CreateTrial API.
See also: AWS API Documentation
Request Syntax
response = client.create_experiment(
ExperimentName='string',
DisplayName='string',
Description='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
[REQUIRED]
The name of the experiment. The name must be unique in your Amazon Web Services account and is not case-sensitive.
A list of tags to associate with the experiment. You can use Search API to search on the tags.
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
dict
Response Syntax
{
'ExperimentArn': 'string'
}
Response Structure
(dict) --
ExperimentArn (string) --
The Amazon Resource Name (ARN) of the experiment.
Exceptions
Create a new FeatureGroup . A FeatureGroup is a group of Features defined in the FeatureStore to describe a Record .
The FeatureGroup defines the schema and features contained in the FeatureGroup. A FeatureGroup definition is composed of a list of Features , a RecordIdentifierFeatureName , an EventTimeFeatureName and configurations for its OnlineStore and OfflineStore . Check Amazon Web Services service quotas to see the FeatureGroup s quota for your Amazon Web Services account.
Warning
You must include at least one of OnlineStoreConfig and OfflineStoreConfig to create a FeatureGroup .
See also: AWS API Documentation
Request Syntax
response = client.create_feature_group(
FeatureGroupName='string',
RecordIdentifierFeatureName='string',
EventTimeFeatureName='string',
FeatureDefinitions=[
{
'FeatureName': 'string',
'FeatureType': 'Integral'|'Fractional'|'String'
},
],
OnlineStoreConfig={
'SecurityConfig': {
'KmsKeyId': 'string'
},
'EnableOnlineStore': True|False
},
OfflineStoreConfig={
'S3StorageConfig': {
'S3Uri': 'string',
'KmsKeyId': 'string',
'ResolvedOutputS3Uri': 'string'
},
'DisableGlueTableCreation': True|False,
'DataCatalogConfig': {
'TableName': 'string',
'Catalog': 'string',
'Database': 'string'
}
},
RoleArn='string',
Description='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
[REQUIRED]
The name of the FeatureGroup . The name must be unique within an Amazon Web Services Region in an Amazon Web Services account. The name:
[REQUIRED]
The name of the Feature whose value uniquely identifies a Record defined in the FeatureStore . Only the latest record per identifier value will be stored in the OnlineStore . RecordIdentifierFeatureName must be one of feature definitions' names.
You use the RecordIdentifierFeatureName to access data in a FeatureStore .
This name:
[REQUIRED]
The name of the feature that stores the EventTime of a Record in a FeatureGroup .
An EventTime is a point in time when a new event occurs that corresponds to the creation or update of a Record in a FeatureGroup . All Records in the FeatureGroup must have a corresponding EventTime .
An EventTime can be a String or Fractional .
[REQUIRED]
A list of Feature names and types. Name and Type is compulsory per Feature .
Valid feature FeatureType s are Integral , Fractional and String .
FeatureName s cannot be any of the following: is_deleted , write_time , api_invocation_time
You can create up to 2,500 FeatureDefinition s per FeatureGroup .
A list of features. You must include FeatureName and FeatureType . Valid feature FeatureType s are Integral , Fractional and String .
The name of a feature. The type must be a string. FeatureName cannot be any of the following: is_deleted , write_time , api_invocation_time .
The value type of a feature. Valid values are Integral, Fractional, or String.
You can turn the OnlineStore on or off by specifying True for the EnableOnlineStore flag in OnlineStoreConfig ; the default value is False .
You can also include an Amazon Web Services KMS key ID (KMSKeyId ) for at-rest encryption of the OnlineStore .
Use to specify KMS Key ID (KMSKeyId ) for at-rest encryption of your OnlineStore .
The ID of the Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker Feature Store uses to encrypt the Amazon S3 objects at rest using Amazon S3 server-side encryption.
The caller (either IAM user or IAM role) of CreateFeatureGroup must have below permissions to the OnlineStore KmsKeyId :
The caller (either IAM user or IAM role) to all DataPlane operations (PutRecord , GetRecord , DeleteRecord ) must have the following permissions to the KmsKeyId :
Turn OnlineStore off by specifying False for the EnableOnlineStore flag. Turn OnlineStore on by specifying True for the EnableOnlineStore flag.
The default value is False .
Use this to configure an OfflineFeatureStore . This parameter allows you to specify:
To learn more about this parameter, see OfflineStoreConfig .
The Amazon Simple Storage (Amazon S3) location of OfflineStore .
The S3 URI, or location in Amazon S3, of OfflineStore .
S3 URIs have a format similar to the following: s3://example-bucket/prefix/ .
The Amazon Web Services Key Management Service (KMS) key ID of the key used to encrypt any objects written into the OfflineStore S3 location.
The IAM roleARN that is passed as a parameter to CreateFeatureGroup must have below permissions to the KmsKeyId :
The S3 path where offline records are written.
Set to True to disable the automatic creation of an Amazon Web Services Glue table when configuring an OfflineStore .
The meta data of the Glue table that is autogenerated when an OfflineStore is created.
The name of the Glue table.
The name of the Glue table catalog.
The name of the Glue table database.
Tags used to identify Features in each FeatureGroup .
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
dict
Response Syntax
{
'FeatureGroupArn': 'string'
}
Response Structure
(dict) --
FeatureGroupArn (string) --
The Amazon Resource Name (ARN) of the FeatureGroup . This is a unique identifier for the feature group.
Exceptions
Creates a flow definition.
See also: AWS API Documentation
Request Syntax
response = client.create_flow_definition(
FlowDefinitionName='string',
HumanLoopRequestSource={
'AwsManagedHumanLoopRequestSource': 'AWS/Rekognition/DetectModerationLabels/Image/V3'|'AWS/Textract/AnalyzeDocument/Forms/V1'
},
HumanLoopActivationConfig={
'HumanLoopActivationConditionsConfig': {
'HumanLoopActivationConditions': 'string'
}
},
HumanLoopConfig={
'WorkteamArn': 'string',
'HumanTaskUiArn': 'string',
'TaskTitle': 'string',
'TaskDescription': 'string',
'TaskCount': 123,
'TaskAvailabilityLifetimeInSeconds': 123,
'TaskTimeLimitInSeconds': 123,
'TaskKeywords': [
'string',
],
'PublicWorkforceTaskPrice': {
'AmountInUsd': {
'Dollars': 123,
'Cents': 123,
'TenthFractionsOfACent': 123
}
}
},
OutputConfig={
'S3OutputPath': 'string',
'KmsKeyId': 'string'
},
RoleArn='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
[REQUIRED]
The name of your flow definition.
Container for configuring the source of human task requests. Use to specify if Amazon Rekognition or Amazon Textract is used as an integration source.
Specifies whether Amazon Rekognition or Amazon Textract are used as the integration source. The default field settings and JSON parsing rules are different based on the integration source. Valid values:
An object containing information about the events that trigger a human workflow.
Container structure for defining under what conditions SageMaker creates a human loop.
JSON expressing use-case specific conditions declaratively. If any condition is matched, atomic tasks are created against the configured work team. The set of conditions is different for Rekognition and Textract. For more information about how to structure the JSON, see JSON Schema for Human Loop Activation Conditions in Amazon Augmented AI in the Amazon SageMaker Developer Guide .
[REQUIRED]
An object containing information about the tasks the human reviewers will perform.
Amazon Resource Name (ARN) of a team of workers. To learn more about the types of workforces and work teams you can create and use with Amazon A2I, see Create and Manage Workforces .
The Amazon Resource Name (ARN) of the human task user interface.
You can use standard HTML and Crowd HTML Elements to create a custom worker task template. You use this template to create a human task UI.
To learn how to create a custom HTML template, see Create Custom Worker Task Template .
To learn how to create a human task UI, which is a worker task template that can be used in a flow definition, see Create and Delete a Worker Task Templates .
A title for the human worker task.
A description for the human worker task.
The number of distinct workers who will perform the same task on each object. For example, if TaskCount is set to 3 for an image classification labeling job, three workers will classify each input image. Increasing TaskCount can improve label accuracy.
The length of time that a task remains available for review by human workers.
The amount of time that a worker has to complete a task. The default value is 3,600 seconds (1 hour).
Keywords used to describe the task so that workers can discover the task.
Defines the amount of money paid to an Amazon Mechanical Turk worker for each task performed.
Use one of the following prices for bounding box tasks. Prices are in US dollars and should be based on the complexity of the task; the longer it takes in your initial testing, the more you should offer.
Use one of the following prices for image classification, text classification, and custom tasks. Prices are in US dollars.
Use one of the following prices for semantic segmentation tasks. Prices are in US dollars.
Use one of the following prices for Textract AnalyzeDocument Important Form Key Amazon Augmented AI review tasks. Prices are in US dollars.
Use one of the following prices for Rekognition DetectModerationLabels Amazon Augmented AI review tasks. Prices are in US dollars.
Use one of the following prices for Amazon Augmented AI custom human review tasks. Prices are in US dollars.
Defines the amount of money paid to an Amazon Mechanical Turk worker in United States dollars.
The whole number of dollars in the amount.
The fractional portion, in cents, of the amount.
Fractions of a cent, in tenths.
[REQUIRED]
An object containing information about where the human review results will be uploaded.
The Amazon S3 path where the object containing human output will be made available.
To learn more about the format of Amazon A2I output data, see Amazon A2I Output Data .
The Amazon Key Management Service (KMS) key ID for server-side encryption.
[REQUIRED]
The Amazon Resource Name (ARN) of the role needed to call other services on your behalf. For example, arn:aws:iam::1234567890:role/service-role/AmazonSageMaker-ExecutionRole-20180111T151298 .
An array of key-value pairs that contain metadata to help you categorize and organize a flow definition. Each tag consists of a key and a value, both of which you define.
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
dict
Response Syntax
{
'FlowDefinitionArn': 'string'
}
Response Structure
(dict) --
FlowDefinitionArn (string) --
The Amazon Resource Name (ARN) of the flow definition you create.
Exceptions
Defines the settings you will use for the human review workflow user interface. Reviewers will see a three-panel interface with an instruction area, the item to review, and an input area.
See also: AWS API Documentation
Request Syntax
response = client.create_human_task_ui(
HumanTaskUiName='string',
UiTemplate={
'Content': 'string'
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
[REQUIRED]
The name of the user interface you are creating.
[REQUIRED]
The Liquid template for the worker user interface.
The content of the Liquid template for the worker user interface.
An array of key-value pairs that contain metadata to help you categorize and organize a human review workflow user interface. Each tag consists of a key and a value, both of which you define.
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
dict
Response Syntax
{
'HumanTaskUiArn': 'string'
}
Response Structure
(dict) --
HumanTaskUiArn (string) --
The Amazon Resource Name (ARN) of the human review workflow user interface you create.
Exceptions
Starts a hyperparameter tuning job. A hyperparameter tuning job finds the best version of a model by running many training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by an objective metric that you choose.
See also: AWS API Documentation
Request Syntax
response = client.create_hyper_parameter_tuning_job(
HyperParameterTuningJobName='string',
HyperParameterTuningJobConfig={
'Strategy': 'Bayesian'|'Random',
'HyperParameterTuningJobObjective': {
'Type': 'Maximize'|'Minimize',
'MetricName': 'string'
},
'ResourceLimits': {
'MaxNumberOfTrainingJobs': 123,
'MaxParallelTrainingJobs': 123
},
'ParameterRanges': {
'IntegerParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string',
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'ContinuousParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string',
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'CategoricalParameterRanges': [
{
'Name': 'string',
'Values': [
'string',
]
},
]
},
'TrainingJobEarlyStoppingType': 'Off'|'Auto',
'TuningJobCompletionCriteria': {
'TargetObjectiveMetricValue': ...
}
},
TrainingJobDefinition={
'DefinitionName': 'string',
'TuningObjective': {
'Type': 'Maximize'|'Minimize',
'MetricName': 'string'
},
'HyperParameterRanges': {
'IntegerParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string',
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'ContinuousParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string',
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'CategoricalParameterRanges': [
{
'Name': 'string',
'Values': [
'string',
]
},
]
},
'StaticHyperParameters': {
'string': 'string'
},
'AlgorithmSpecification': {
'TrainingImage': 'string',
'TrainingInputMode': 'Pipe'|'File',
'AlgorithmName': 'string',
'MetricDefinitions': [
{
'Name': 'string',
'Regex': 'string'
},
]
},
'RoleArn': 'string',
'InputDataConfig': [
{
'ChannelName': 'string',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'AttributeNames': [
'string',
]
},
'FileSystemDataSource': {
'FileSystemId': 'string',
'FileSystemAccessMode': 'rw'|'ro',
'FileSystemType': 'EFS'|'FSxLustre',
'DirectoryPath': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File',
'ShuffleConfig': {
'Seed': 123
}
},
],
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
'OutputDataConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string'
},
'ResourceConfig': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123
},
'EnableNetworkIsolation': True|False,
'EnableInterContainerTrafficEncryption': True|False,
'EnableManagedSpotTraining': True|False,
'CheckpointConfig': {
'S3Uri': 'string',
'LocalPath': 'string'
},
'RetryStrategy': {
'MaximumRetryAttempts': 123
}
},
TrainingJobDefinitions=[
{
'DefinitionName': 'string',
'TuningObjective': {
'Type': 'Maximize'|'Minimize',
'MetricName': 'string'
},
'HyperParameterRanges': {
'IntegerParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string',
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'ContinuousParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string',
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'CategoricalParameterRanges': [
{
'Name': 'string',
'Values': [
'string',
]
},
]
},
'StaticHyperParameters': {
'string': 'string'
},
'AlgorithmSpecification': {
'TrainingImage': 'string',
'TrainingInputMode': 'Pipe'|'File',
'AlgorithmName': 'string',
'MetricDefinitions': [
{
'Name': 'string',
'Regex': 'string'
},
]
},
'RoleArn': 'string',
'InputDataConfig': [
{
'ChannelName': 'string',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'AttributeNames': [
'string',
]
},
'FileSystemDataSource': {
'FileSystemId': 'string',
'FileSystemAccessMode': 'rw'|'ro',
'FileSystemType': 'EFS'|'FSxLustre',
'DirectoryPath': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File',
'ShuffleConfig': {
'Seed': 123
}
},
],
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
'OutputDataConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string'
},
'ResourceConfig': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123
},
'EnableNetworkIsolation': True|False,
'EnableInterContainerTrafficEncryption': True|False,
'EnableManagedSpotTraining': True|False,
'CheckpointConfig': {
'S3Uri': 'string',
'LocalPath': 'string'
},
'RetryStrategy': {
'MaximumRetryAttempts': 123
}
},
],
WarmStartConfig={
'ParentHyperParameterTuningJobs': [
{
'HyperParameterTuningJobName': 'string'
},
],
'WarmStartType': 'IdenticalDataAndAlgorithm'|'TransferLearning'
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
[REQUIRED]
The name of the tuning job. This name is the prefix for the names of all training jobs that this tuning job launches. The name must be unique within the same Amazon Web Services account and Amazon Web Services Region. The name must have 1 to 32 characters. Valid characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case sensitive.
[REQUIRED]
The HyperParameterTuningJobConfig object that describes the tuning job, including the search strategy, the objective metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the tuning job. For more information, see How Hyperparameter Tuning Works .
Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job it launches. To use the Bayesian search strategy, set this to Bayesian . To randomly search, set it to Random . For information about search strategies, see How Hyperparameter Tuning Works .
The HyperParameterTuningJobObjective object that specifies the objective metric for this tuning job.
Whether to minimize or maximize the objective metric.
The name of the metric to use for the objective metric.
The ResourceLimits object that specifies the maximum number of training jobs and parallel training jobs for this tuning job.
The maximum number of training jobs that a hyperparameter tuning job can launch.
The maximum number of concurrent training jobs that a hyperparameter tuning job can launch.
The ParameterRanges object that specifies the ranges of hyperparameters that this tuning job searches.
The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches.
The name of the hyperparameter to search.
The minimum value of the hyperparameter to search.
The maximum value of the hyperparameter to search.
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
Amazon SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
A list of continuous hyperparameters to tune.
The name of the continuous hyperparameter to tune.
The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValue for tuning.
The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
Amazon SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ReverseLogarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
A list of categorical hyperparameters to tune.
The name of the categorical hyperparameter to tune.
A list of the categories for the hyperparameter.
Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job. This can be one of the following values (the default value is OFF ):
OFF
Training jobs launched by the hyperparameter tuning job do not use early stopping.
AUTO
Amazon SageMaker stops training jobs launched by the hyperparameter tuning job when they are unlikely to perform better than previously completed training jobs. For more information, see Stop Training Jobs Early .
The tuning job's completion criteria.
The value of the objective metric.
The HyperParameterTrainingJobDefinition object that describes the training jobs that this tuning job launches, including static hyperparameters, input data configuration, output data configuration, resource configuration, and stopping condition.
The job definition name.
Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the Type parameter.
Whether to minimize or maximize the objective metric.
The name of the metric to use for the objective metric.
Specifies ranges of integer, continuous, and categorical hyperparameters that a hyperparameter tuning job searches. The hyperparameter tuning job launches training jobs with hyperparameter values within these ranges to find the combination of values that result in the training job with the best performance as measured by the objective metric of the hyperparameter tuning job.
Note
You can specify a maximum of 20 hyperparameters that a hyperparameter tuning job can search over. Every possible value of a categorical parameter range counts against this limit.
The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches.
The name of the hyperparameter to search.
The minimum value of the hyperparameter to search.
The maximum value of the hyperparameter to search.
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
Amazon SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
A list of continuous hyperparameters to tune.
The name of the continuous hyperparameter to tune.
The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValue for tuning.
The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
Amazon SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ReverseLogarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
A list of categorical hyperparameters to tune.
The name of the categorical hyperparameter to tune.
A list of the categories for the hyperparameter.
Specifies the values of hyperparameters that do not change for the tuning job.
The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.
The registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters . Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
The input mode that the algorithm supports: File or Pipe. In File input mode, Amazon SageMaker downloads the training data from Amazon S3 to the storage volume that is attached to the training instance and mounts the directory to the Docker volume for the training container. In Pipe input mode, Amazon SageMaker streams data directly from Amazon S3 to the container.
If you specify File mode, make sure that you provision the storage volume that is attached to the training instance with enough capacity to accommodate the training data downloaded from Amazon S3, the model artifacts, and intermediate information.
For more information about input modes, see Algorithms .
The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this parameter, do not specify a value for TrainingImage .
An array of MetricDefinition objects that specify the metrics that the algorithm emits.
Specifies a metric that the training algorithm writes to stderr or stdout . Amazon SageMakerhyperparameter tuning captures all defined metrics. You specify one metric that a hyperparameter tuning job uses as its objective metric to choose the best training job.
The name of the metric.
A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining Objective Metrics .
The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
An array of Channel objects that specify the input for the training jobs that the tuning job launches.
A channel is a named input source that training algorithms can consume.
The name of the channel.
The location of the channel data.
The S3 location of the data source that is associated with a channel.
If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects that match the specified key name prefix for model training.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for model training.
If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
If you want Amazon SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .
If you want Amazon SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.
A list of one or more attribute names to use that are found in a specified augmented manifest file.
The file system that is associated with a channel.
The file system id.
The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.
The file system type.
The full path to the directory to associate with the channel.
The MIME type of the data.
If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, Amazon SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO .
In File mode, leave this field unset or set it to None.
(Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , Amazon SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
To use a model for incremental training, choose File input model.
A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
Determines the shuffling order in ShuffleConfig value.
The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud .
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
If you use a KMS key ID or an alias of your master key, the Amazon SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. Amazon SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig . If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
Identifies the S3 path where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want Amazon SageMaker to use the storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.
The ML compute instance type.
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.
You must specify sufficient ML storage for your scenario.
Note
Amazon SageMaker supports only the General Purpose SSD (gp2) ML storage volume type.
Note
Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for training, Amazon SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can't request a VolumeSizeInGB greater than the total size of the local instance storage.
For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes .
The Amazon Web Services KMS key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be in any of the following formats:
Specifies a limit to how long a model hyperparameter training job can run. It also specifies how long a managed spot training job has to complete. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.
The maximum length of time, in seconds, that a training or compilation job can run. If the job does not complete during this time, Amazon SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
The default value is 1 day. The maximum value is 28 days.
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, Amazon SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
To encrypt all communications between ML compute instances in distributed training, choose True . Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.
A Boolean indicating whether managed spot training is enabled (True ) or not (False ).
Contains information about the output location for managed spot training checkpoint data.
Identifies the S3 path where you want Amazon SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix .
(Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/ .
The number of times to retry the job when the job fails due to an InternalServerError .
The number of times to retry the job. When the job is retried, it's SecondaryStatus is changed to STARTING .
A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.
Defines the training jobs launched by a hyperparameter tuning job.
The job definition name.
Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the Type parameter.
Whether to minimize or maximize the objective metric.
The name of the metric to use for the objective metric.
Specifies ranges of integer, continuous, and categorical hyperparameters that a hyperparameter tuning job searches. The hyperparameter tuning job launches training jobs with hyperparameter values within these ranges to find the combination of values that result in the training job with the best performance as measured by the objective metric of the hyperparameter tuning job.
Note
You can specify a maximum of 20 hyperparameters that a hyperparameter tuning job can search over. Every possible value of a categorical parameter range counts against this limit.
The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches.
The name of the hyperparameter to search.
The minimum value of the hyperparameter to search.
The maximum value of the hyperparameter to search.
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
Amazon SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
A list of continuous hyperparameters to tune.
The name of the continuous hyperparameter to tune.
The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValue for tuning.
The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
Amazon SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ReverseLogarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
A list of categorical hyperparameters to tune.
The name of the categorical hyperparameter to tune.
A list of the categories for the hyperparameter.
Specifies the values of hyperparameters that do not change for the tuning job.
The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.
The registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters . Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
The input mode that the algorithm supports: File or Pipe. In File input mode, Amazon SageMaker downloads the training data from Amazon S3 to the storage volume that is attached to the training instance and mounts the directory to the Docker volume for the training container. In Pipe input mode, Amazon SageMaker streams data directly from Amazon S3 to the container.
If you specify File mode, make sure that you provision the storage volume that is attached to the training instance with enough capacity to accommodate the training data downloaded from Amazon S3, the model artifacts, and intermediate information.
For more information about input modes, see Algorithms .
The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this parameter, do not specify a value for TrainingImage .
An array of MetricDefinition objects that specify the metrics that the algorithm emits.
Specifies a metric that the training algorithm writes to stderr or stdout . Amazon SageMakerhyperparameter tuning captures all defined metrics. You specify one metric that a hyperparameter tuning job uses as its objective metric to choose the best training job.
The name of the metric.
A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining Objective Metrics .
The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
An array of Channel objects that specify the input for the training jobs that the tuning job launches.
A channel is a named input source that training algorithms can consume.
The name of the channel.
The location of the channel data.
The S3 location of the data source that is associated with a channel.
If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects that match the specified key name prefix for model training.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for model training.
If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
If you want Amazon SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .
If you want Amazon SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.
A list of one or more attribute names to use that are found in a specified augmented manifest file.
The file system that is associated with a channel.
The file system id.
The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.
The file system type.
The full path to the directory to associate with the channel.
The MIME type of the data.
If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, Amazon SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO .
In File mode, leave this field unset or set it to None.
(Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , Amazon SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
To use a model for incremental training, choose File input model.
A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
Determines the shuffling order in ShuffleConfig value.
The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud .
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
If you use a KMS key ID or an alias of your master key, the Amazon SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. Amazon SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig . If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
Identifies the S3 path where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want Amazon SageMaker to use the storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.
The ML compute instance type.
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.
You must specify sufficient ML storage for your scenario.
Note
Amazon SageMaker supports only the General Purpose SSD (gp2) ML storage volume type.
Note
Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for training, Amazon SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can't request a VolumeSizeInGB greater than the total size of the local instance storage.
For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes .
The Amazon Web Services KMS key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be in any of the following formats:
Specifies a limit to how long a model hyperparameter training job can run. It also specifies how long a managed spot training job has to complete. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.
The maximum length of time, in seconds, that a training or compilation job can run. If the job does not complete during this time, Amazon SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
The default value is 1 day. The maximum value is 28 days.
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, Amazon SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
To encrypt all communications between ML compute instances in distributed training, choose True . Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.
A Boolean indicating whether managed spot training is enabled (True ) or not (False ).
Contains information about the output location for managed spot training checkpoint data.
Identifies the S3 path where you want Amazon SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix .
(Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/ .
The number of times to retry the job when the job fails due to an InternalServerError .
The number of times to retry the job. When the job is retried, it's SecondaryStatus is changed to STARTING .
Specifies the configuration for starting the hyperparameter tuning job using one or more previous tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.
All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric. If you specify IDENTICAL_DATA_AND_ALGORITHM as the WarmStartType value for the warm start configuration, the training job that performs the best in the new tuning job is compared to the best training jobs from the parent tuning jobs. From these, the training job that performs the best as measured by the objective metric is returned as the overall best training job.
Note
All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count against the limit of training jobs for the tuning job.
An array of hyperparameter tuning jobs that are used as the starting point for the new hyperparameter tuning job. For more information about warm starting a hyperparameter tuning job, see Using a Previous Hyperparameter Tuning Job as a Starting Point .
Hyperparameter tuning jobs created before October 1, 2018 cannot be used as parent jobs for warm start tuning jobs.
A previously completed or stopped hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job.
The name of the hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job.
Specifies one of the following:
IDENTICAL_DATA_AND_ALGORITHM
The new hyperparameter tuning job uses the same input data and training image as the parent tuning jobs. You can change the hyperparameter ranges to search and the maximum number of training jobs that the hyperparameter tuning job launches. You cannot use a new version of the training algorithm, unless the changes in the new version do not affect the algorithm itself. For example, changes that improve logging or adding support for a different data format are allowed. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.
TRANSFER_LEARNING
The new hyperparameter tuning job can include input data, hyperparameter ranges, maximum number of concurrent training jobs, and maximum number of training jobs that are different than those of its parent hyperparameter tuning jobs. The training image can also be a different version from the version used in the parent hyperparameter tuning job. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources .
Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
dict
Response Syntax
{
'HyperParameterTuningJobArn': 'string'
}
Response Structure
(dict) --
HyperParameterTuningJobArn (string) --
The Amazon Resource Name (ARN) of the tuning job. Amazon SageMaker assigns an ARN to a hyperparameter tuning job when you create it.
Exceptions
Creates a custom SageMaker image. A SageMaker image is a set of image versions. Each image version represents a container image stored in Amazon Container Registry (ECR). For more information, see Bring your own SageMaker image .
See also: AWS API Documentation
Request Syntax
response = client.create_image(
Description='string',
DisplayName='string',
ImageName='string',
RoleArn='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
[REQUIRED]
The name of the image. Must be unique to your account.
[REQUIRED]
The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.
A list of tags to apply to the image.
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
dict
Response Syntax
{
'ImageArn': 'string'
}
Response Structure
(dict) --
ImageArn (string) --
The Amazon Resource Name (ARN) of the image.
Exceptions
Creates a version of the SageMaker image specified by ImageName . The version represents the Amazon Container Registry (ECR) container image specified by BaseImage .
See also: AWS API Documentation
Request Syntax
response = client.create_image_version(
BaseImage='string',
ClientToken='string',
ImageName='string'
)
[REQUIRED]
The registry path of the container image to use as the starting point for this version. The path is an Amazon Container Registry (ECR) URI in the following format:
<acct-id>.dkr.ecr.<region>.amazonaws.com/<repo-name[:tag] or [@digest]>
[REQUIRED]
A unique ID. If not specified, the Amazon Web Services CLI and Amazon Web Services SDKs, such as the SDK for Python (Boto3), add a unique value to the call.
This field is autopopulated if not provided.
[REQUIRED]
The ImageName of the Image to create a version of.
dict
Response Syntax
{
'ImageVersionArn': 'string'
}
Response Structure
(dict) --
ImageVersionArn (string) --
The Amazon Resource Name (ARN) of the image version.
Exceptions
Creates a job that uses workers to label the data objects in your input dataset. You can use the labeled data to train machine learning models.
You can select your workforce from one of three providers:
You can also use automated data labeling to reduce the number of data objects that need to be labeled by a human. Automated data labeling uses active learning to determine if a data object can be labeled by machine or if it needs to be sent to a human worker. For more information, see Using Automated Data Labeling .
The data objects to be labeled are contained in an Amazon S3 bucket. You create a manifest file that describes the location of each object. For more information, see Using Input and Output Data .
The output can be used as the manifest file for another labeling job or as training data for your machine learning models.
You can use this operation to create a static labeling job or a streaming labeling job. A static labeling job stops if all data objects in the input manifest file identified in ManifestS3Uri have been labeled. A streaming labeling job runs perpetually until it is manually stopped, or remains idle for 10 days. You can send new data objects to an active (InProgress ) streaming labeling job in real time. To learn how to create a static labeling job, see Create a Labeling Job (API) in the Amazon SageMaker Developer Guide. To learn how to create a streaming labeling job, see Create a Streaming Labeling Job .
See also: AWS API Documentation
Request Syntax
response = client.create_labeling_job(
LabelingJobName='string',
LabelAttributeName='string',
InputConfig={
'DataSource': {
'S3DataSource': {
'ManifestS3Uri': 'string'
},
'SnsDataSource': {
'SnsTopicArn': 'string'
}
},
'DataAttributes': {
'ContentClassifiers': [
'FreeOfPersonallyIdentifiableInformation'|'FreeOfAdultContent',
]
}
},
OutputConfig={
'S3OutputPath': 'string',
'KmsKeyId': 'string',
'SnsTopicArn': 'string'
},
RoleArn='string',
LabelCategoryConfigS3Uri='string',
StoppingConditions={
'MaxHumanLabeledObjectCount': 123,
'MaxPercentageOfInputDatasetLabeled': 123
},
LabelingJobAlgorithmsConfig={
'LabelingJobAlgorithmSpecificationArn': 'string',
'InitialActiveLearningModelArn': 'string',
'LabelingJobResourceConfig': {
'VolumeKmsKeyId': 'string'
}
},
HumanTaskConfig={
'WorkteamArn': 'string',
'UiConfig': {
'UiTemplateS3Uri': 'string',
'HumanTaskUiArn': 'string'
},
'PreHumanTaskLambdaArn': 'string',
'TaskKeywords': [
'string',
],
'TaskTitle': 'string',
'TaskDescription': 'string',
'NumberOfHumanWorkersPerDataObject': 123,
'TaskTimeLimitInSeconds': 123,
'TaskAvailabilityLifetimeInSeconds': 123,
'MaxConcurrentTaskCount': 123,
'AnnotationConsolidationConfig': {
'AnnotationConsolidationLambdaArn': 'string'
},
'PublicWorkforceTaskPrice': {
'AmountInUsd': {
'Dollars': 123,
'Cents': 123,
'TenthFractionsOfACent': 123
}
}
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
[REQUIRED]
The name of the labeling job. This name is used to identify the job in a list of labeling jobs. Labeling job names must be unique within an Amazon Web Services account and region. LabelingJobName is not case sensitive. For example, Example-job and example-job are considered the same labeling job name by Ground Truth.
[REQUIRED]
The attribute name to use for the label in the output manifest file. This is the key for the key/value pair formed with the label that a worker assigns to the object. The LabelAttributeName must meet the following requirements.
Warning
If you are creating an adjustment or verification labeling job, you must use a different LabelAttributeName than the one used in the original labeling job. The original labeling job is the Ground Truth labeling job that produced the labels that you want verified or adjusted. To learn more about adjustment and verification labeling jobs, see Verify and Adjust Labels .
[REQUIRED]
Input data for the labeling job, such as the Amazon S3 location of the data objects and the location of the manifest file that describes the data objects.
You must specify at least one of the following: S3DataSource or SnsDataSource .
If you use the Amazon Mechanical Turk workforce, your input data should not include confidential information, personal information or protected health information. Use ContentClassifiers to specify that your data is free of personally identifiable information and adult content.
The location of the input data.
The Amazon S3 location of the input data objects.
The Amazon S3 location of the manifest file that describes the input data objects.
The input manifest file referenced in ManifestS3Uri must contain one of the following keys: source-ref or source . The value of the keys are interpreted as follows:
If you are a new user of Ground Truth, it is recommended you review Use an Input Manifest File in the Amazon SageMaker Developer Guide to learn how to create an input manifest file.
An Amazon SNS data source used for streaming labeling jobs. To learn more, see Send Data to a Streaming Labeling Job .
The Amazon SNS input topic Amazon Resource Name (ARN). Specify the ARN of the input topic you will use to send new data objects to a streaming labeling job.
Attributes of the data specified by the customer.
Declares that your content is free of personally identifiable information or adult content. Amazon SageMaker may restrict the Amazon Mechanical Turk workers that can view your task based on this information.
[REQUIRED]
The location of the output data and the Amazon Web Services Key Management Service key ID for the key used to encrypt the output data, if any.
The Amazon S3 location to write output data.
The Amazon Web Services Key Management Service ID of the key used to encrypt the output data, if any.
If you provide your own KMS key ID, you must add the required permissions to your KMS key described in Encrypt Output Data and Storage Volume with Amazon Web Services KMS .
If you don't provide a KMS key ID, Amazon SageMaker uses the default Amazon Web Services KMS key for Amazon S3 for your role's account to encrypt your output data.
If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
An Amazon Simple Notification Service (Amazon SNS) output topic ARN. Provide a SnsTopicArn if you want to do real time chaining to another streaming job and receive an Amazon SNS notifications each time a data object is submitted by a worker.
If you provide an SnsTopicArn in OutputConfig , when workers complete labeling tasks, Ground Truth will send labeling task output data to the SNS output topic you specify here.
To learn more, see Receive Output Data from a Streaming Labeling Job .
[REQUIRED]
The Amazon Resource Number (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during data labeling. You must grant this role the necessary permissions so that Amazon SageMaker can successfully complete data labeling.
The S3 URI of the file, referred to as a label category configuration file , that defines the categories used to label the data objects.
For 3D point cloud and video frame task types, you can add label category attributes and frame attributes to your label category configuration file. To learn how, see Create a Labeling Category Configuration File for 3D Point Cloud Labeling Jobs .
For all other built-in task types and custom tasks , your label category configuration file must be a JSON file in the following format. Identify the labels you want to use by replacing label_1 , label_2 ,``...`` ,``label_n`` with your label categories.
{"document-version": "2018-11-28",
"labels": [{"label": "label_1"},{"label": "label_2"},...{"label": "label_n"}]
}
Note the following about the label category configuration file:
A set of conditions for stopping the labeling job. If any of the conditions are met, the job is automatically stopped. You can use these conditions to control the cost of data labeling.
The maximum number of objects that can be labeled by human workers.
The maximum number of input data objects that should be labeled.
Configures the information required to perform automated data labeling.
Specifies the Amazon Resource Name (ARN) of the algorithm used for auto-labeling. You must select one of the following ARNs:
At the end of an auto-label job Ground Truth sends the Amazon Resource Name (ARN) of the final model used for auto-labeling. You can use this model as the starting point for subsequent similar jobs by providing the ARN of the model here.
Provides configuration information for a labeling job.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training and inference jobs used for automated data labeling.
You can only specify a VolumeKmsKeyId when you create a labeling job with automated data labeling enabled using the API operation CreateLabelingJob . You cannot specify an Amazon Web Services KMS customer managed CMK to encrypt the storage volume used for automated data labeling model training and inference when you create a labeling job using the console. To learn more, see Output Data and Storage Volume Encryption .
The VolumeKmsKeyId can be any of the following formats:
[REQUIRED]
Configures the labeling task and how it is presented to workers; including, but not limited to price, keywords, and batch size (task count).
The Amazon Resource Name (ARN) of the work team assigned to complete the tasks.
Information about the user interface that workers use to complete the labeling task.
The Amazon S3 bucket location of the UI template, or worker task template. This is the template used to render the worker UI and tools for labeling job tasks. For more information about the contents of a UI template, see Creating Your Custom Labeling Task Template .
The ARN of the worker task template used to render the worker UI and tools for labeling job tasks.
Use this parameter when you are creating a labeling job for 3D point cloud and video fram labeling jobs. Use your labeling job task type to select one of the following ARNs and use it with this parameter when you create a labeling job. Replace aws-region with the Amazon Web Services region you are creating your labeling job in.
3D Point Cloud HumanTaskUiArns
Use this HumanTaskUiArn for 3D point cloud object detection and 3D point cloud object detection adjustment labeling jobs.
Use this HumanTaskUiArn for 3D point cloud object tracking and 3D point cloud object tracking adjustment labeling jobs.
Use this HumanTaskUiArn for 3D point cloud semantic segmentation and 3D point cloud semantic segmentation adjustment labeling jobs.
Video Frame HumanTaskUiArns
Use this HumanTaskUiArn for video frame object detection and video frame object detection adjustment labeling jobs.
Use this HumanTaskUiArn for video frame object tracking and video frame object tracking adjustment labeling jobs.
The Amazon Resource Name (ARN) of a Lambda function that is run before a data object is sent to a human worker. Use this function to provide input to a custom labeling job.
For built-in task types , use one of the following Amazon SageMaker Ground Truth Lambda function ARNs for PreHumanTaskLambdaArn . For custom labeling workflows, see Pre-annotation Lambda .
Bounding box - Finds the most similar boxes from different workers based on the Jaccard index of the boxes.
Image classification - Uses a variant of the Expectation Maximization approach to estimate the true class of an image based on annotations from individual workers.
Multi-label image classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of an image based on annotations from individual workers.
Semantic segmentation - Treats each pixel in an image as a multi-class classification and treats pixel annotations from workers as "votes" for the correct label.
Text classification - Uses a variant of the Expectation Maximization approach to estimate the true class of text based on annotations from individual workers.
Multi-label text classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of text based on annotations from individual workers.
Named entity recognition - Groups similar selections and calculates aggregate boundaries, resolving to most-assigned label.
Video Classification - Use this task type when you need workers to classify videos using predefined labels that you specify. Workers are shown videos and are asked to choose one label for each video.
Video Frame Object Detection - Use this task type to have workers identify and locate objects in a sequence of video frames (images extracted from a video) using bounding boxes. For example, you can use this task to ask workers to identify and localize various objects in a series of video frames, such as cars, bikes, and pedestrians.
Video Frame Object Tracking - Use this task type to have workers track the movement of objects in a sequence of video frames (images extracted from a video) using bounding boxes. For example, you can use this task to ask workers to track the movement of objects, such as cars, bikes, and pedestrians.
3D Point Cloud Modalities
Use the following pre-annotation lambdas for 3D point cloud labeling modality tasks. See 3D Point Cloud Task types to learn more.
3D Point Cloud Object Detection - Use this task type when you want workers to classify objects in a 3D point cloud by drawing 3D cuboids around objects. For example, you can use this task type to ask workers to identify different types of objects in a point cloud, such as cars, bikes, and pedestrians.
3D Point Cloud Object Tracking - Use this task type when you want workers to draw 3D cuboids around objects that appear in a sequence of 3D point cloud frames. For example, you can use this task type to ask workers to track the movement of vehicles across multiple point cloud frames.
3D Point Cloud Semantic Segmentation - Use this task type when you want workers to create a point-level semantic segmentation masks by painting objects in a 3D point cloud using different colors where each color is assigned to one of the classes you specify.
Use the following ARNs for Label Verification and Adjustment Jobs
Use label verification and adjustment jobs to review and adjust labels. To learn more, see Verify and Adjust Labels .
Bounding box verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgement for bounding box labels based on annotations from individual workers.
Bounding box adjustment - Finds the most similar boxes from different workers based on the Jaccard index of the adjusted annotations.
Semantic segmentation verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgment for semantic segmentation labels based on annotations from individual workers.
Semantic segmentation adjustment - Treats each pixel in an image as a multi-class classification and treats pixel adjusted annotations from workers as "votes" for the correct label.
Video Frame Object Detection Adjustment - Use this task type when you want workers to adjust bounding boxes that workers have added to video frames to classify and localize objects in a sequence of video frames.
Video Frame Object Tracking Adjustment - Use this task type when you want workers to adjust bounding boxes that workers have added to video frames to track object movement across a sequence of video frames.
3D point cloud object detection adjustment - Adjust 3D cuboids in a point cloud frame.
3D point cloud object tracking adjustment - Adjust 3D cuboids across a sequence of point cloud frames.
3D point cloud semantic segmentation adjustment - Adjust semantic segmentation masks in a 3D point cloud.
Keywords used to describe the task so that workers on Amazon Mechanical Turk can discover the task.
A title for the task for your human workers.
A description of the task for your human workers.
The number of human workers that will label an object.
The amount of time that a worker has to complete a task.
If you create a custom labeling job, the maximum value for this parameter is 8 hours (28,800 seconds).
If you create a labeling job using a built-in task type the maximum for this parameter depends on the task type you use:
The length of time that a task remains available for labeling by human workers. The default and maximum values for this parameter depend on the type of workforce you use.
Defines the maximum number of data objects that can be labeled by human workers at the same time. Also referred to as batch size. Each object may have more than one worker at one time. The default value is 1000 objects.
Configures how labels are consolidated across human workers.
The Amazon Resource Name (ARN) of a Lambda function implements the logic for annotation consolidation and to process output data.
This parameter is required for all labeling jobs. For built-in task types , use one of the following Amazon SageMaker Ground Truth Lambda function ARNs for AnnotationConsolidationLambdaArn . For custom labeling workflows, see Post-annotation Lambda .
Bounding box - Finds the most similar boxes from different workers based on the Jaccard index of the boxes.
Image classification - Uses a variant of the Expectation Maximization approach to estimate the true class of an image based on annotations from individual workers.
Multi-label image classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of an image based on annotations from individual workers.
Semantic segmentation - Treats each pixel in an image as a multi-class classification and treats pixel annotations from workers as "votes" for the correct label.
Text classification - Uses a variant of the Expectation Maximization approach to estimate the true class of text based on annotations from individual workers.
Multi-label text classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of text based on annotations from individual workers.
Named entity recognition - Groups similar selections and calculates aggregate boundaries, resolving to most-assigned label.
Video Classification - Use this task type when you need workers to classify videos using predefined labels that you specify. Workers are shown videos and are asked to choose one label for each video.
Video Frame Object Detection - Use this task type to have workers identify and locate objects in a sequence of video frames (images extracted from a video) using bounding boxes. For example, you can use this task to ask workers to identify and localize various objects in a series of video frames, such as cars, bikes, and pedestrians.
Video Frame Object Tracking - Use this task type to have workers track the movement of objects in a sequence of video frames (images extracted from a video) using bounding boxes. For example, you can use this task to ask workers to track the movement of objects, such as cars, bikes, and pedestrians.
3D Point Cloud Object Detection - Use this task type when you want workers to classify objects in a 3D point cloud by drawing 3D cuboids around objects. For example, you can use this task type to ask workers to identify different types of objects in a point cloud, such as cars, bikes, and pedestrians.
3D Point Cloud Object Tracking - Use this task type when you want workers to draw 3D cuboids around objects that appear in a sequence of 3D point cloud frames. For example, you can use this task type to ask workers to track the movement of vehicles across multiple point cloud frames.
3D Point Cloud Semantic Segmentation - Use this task type when you want workers to create a point-level semantic segmentation masks by painting objects in a 3D point cloud using different colors where each color is assigned to one of the classes you specify.
Use the following ARNs for Label Verification and Adjustment Jobs
Use label verification and adjustment jobs to review and adjust labels. To learn more, see Verify and Adjust Labels .
Semantic Segmentation Adjustment - Treats each pixel in an image as a multi-class classification and treats pixel adjusted annotations from workers as "votes" for the correct label.
Semantic Segmentation Verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgment for semantic segmentation labels based on annotations from individual workers.
Bounding Box Adjustment - Finds the most similar boxes from different workers based on the Jaccard index of the adjusted annotations.
Bounding Box Verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgement for bounding box labels based on annotations from individual workers.
Video Frame Object Detection Adjustment - Use this task type when you want workers to adjust bounding boxes that workers have added to video frames to classify and localize objects in a sequence of video frames.
Video Frame Object Tracking Adjustment - Use this task type when you want workers to adjust bounding boxes that workers have added to video frames to track object movement across a sequence of video frames.
3D Point Cloud Object Detection Adjustment - Use this task type when you want workers to adjust 3D cuboids around objects in a 3D point cloud.
3D Point Cloud Object Tracking Adjustment - Use this task type when you want workers to adjust 3D cuboids around objects that appear in a sequence of 3D point cloud frames.
3D Point Cloud Semantic Segmentation Adjustment - Use this task type when you want workers to adjust a point-level semantic segmentation masks using a paint tool.
The price that you pay for each task performed by an Amazon Mechanical Turk worker.
Defines the amount of money paid to an Amazon Mechanical Turk worker in United States dollars.
The whole number of dollars in the amount.
The fractional portion, in cents, of the amount.
Fractions of a cent, in tenths.
An array of key/value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide .
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
dict
Response Syntax
{
'LabelingJobArn': 'string'
}
Response Structure
(dict) --
LabelingJobArn (string) --
The Amazon Resource Name (ARN) of the labeling job. You use this ARN to identify the labeling job.
Exceptions
Creates a model in Amazon SageMaker. In the request, you name the model and describe a primary container. For the primary container, you specify the Docker image that contains inference code, artifacts (from prior training), and a custom environment map that the inference code uses when you deploy the model for predictions.
Use this API to create a model if you want to use Amazon SageMaker hosting services or run a batch transform job.
To host your model, you create an endpoint configuration with the CreateEndpointConfig API, and then create an endpoint with the CreateEndpoint API. Amazon SageMaker then deploys all of the containers that you defined for the model in the hosting environment.
For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (Amazon Web Services SDK for Python (Boto 3)).
To run a batch transform using your model, you start a job with the CreateTransformJob API. Amazon SageMaker uses your model and your dataset to get inferences which are then saved to a specified S3 location.
In the CreateModel request, you must define a container with the PrimaryContainer parameter.
In the request, you also provide an IAM role that Amazon SageMaker can assume to access model artifacts and docker image for deployment on ML compute hosting instances or for batch transform jobs. In addition, you also use the IAM role to manage permissions the inference code needs. For example, if the inference code access any other Amazon Web Services resources, you grant necessary permissions via this role.
See also: AWS API Documentation
Request Syntax
response = client.create_model(
ModelName='string',
PrimaryContainer={
'ContainerHostname': 'string',
'Image': 'string',
'ImageConfig': {
'RepositoryAccessMode': 'Platform'|'Vpc',
'RepositoryAuthConfig': {
'RepositoryCredentialsProviderArn': 'string'
}
},
'Mode': 'SingleModel'|'MultiModel',
'ModelDataUrl': 'string',
'Environment': {
'string': 'string'
},
'ModelPackageName': 'string',
'MultiModelConfig': {
'ModelCacheSetting': 'Enabled'|'Disabled'
}
},
Containers=[
{
'ContainerHostname': 'string',
'Image': 'string',
'ImageConfig': {
'RepositoryAccessMode': 'Platform'|'Vpc',
'RepositoryAuthConfig': {
'RepositoryCredentialsProviderArn': 'string'
}
},
'Mode': 'SingleModel'|'MultiModel',
'ModelDataUrl': 'string',
'Environment': {
'string': 'string'
},
'ModelPackageName': 'string',
'MultiModelConfig': {
'ModelCacheSetting': 'Enabled'|'Disabled'
}
},
],
InferenceExecutionConfig={
'Mode': 'Serial'|'Direct'
},
ExecutionRoleArn='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
VpcConfig={
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
EnableNetworkIsolation=True|False
)
[REQUIRED]
The name of the new model.
The location of the primary docker image containing inference code, associated artifacts, and custom environment map that the inference code uses when the model is deployed for predictions.
This parameter is ignored for models that contain only a PrimaryContainer .
When a ContainerDefinition is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline . If you don't specify a value for this parameter for a ContainerDefinition that is part of an inference pipeline, a unique name is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value for the ContainerHostName for any ContainerDefinition that is part of an inference pipeline, you must specify a value for the ContainerHostName parameter of every ContainerDefinition in that pipeline.
The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. If you are using your own custom algorithm instead of an algorithm provided by Amazon SageMaker, the inference code must meet Amazon SageMaker requirements. Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker
Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers
Set this to one of the following values:
(Optional) Specifies an authentication configuration for the private docker registry where your model image is hosted. Specify a value for this property only if you specified Vpc as the value for the RepositoryAccessMode field, and the private Docker registry where the model image is hosted requires authentication.
The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function that provides credentials to authenticate to the private Docker registry where your model image is hosted. For information about how to create an Amazon Web Services Lambda function, see Create a Lambda function with the console in the Amazon Web Services Lambda Developer Guide .
Whether the container hosts a single model or multiple models.
The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). The S3 path is required for Amazon SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters .
Note
The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.
If you provide a value for this parameter, Amazon SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provide. Amazon Web Services STS is activated in your IAM user account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide .
Warning
If you use a built-in algorithm to create a model, Amazon SageMaker requires that you provide a S3 path to the model artifacts in ModelDataUrl .
The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
The name or Amazon Resource Name (ARN) of the model package to use to create the model.
Specifies additional configuration for multi-model endpoints.
Whether to cache models for a multi-model endpoint. By default, multi-model endpoints cache models so that a model does not have to be loaded into memory each time it is invoked. Some use cases do not benefit from model caching. For example, if an endpoint hosts a large number of models that are each invoked infrequently, the endpoint might perform better if you disable model caching. To disable model caching, set the value of this parameter to Disabled .
Specifies the containers in the inference pipeline.
Describes the container, as part of model definition.
This parameter is ignored for models that contain only a PrimaryContainer .
When a ContainerDefinition is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline . If you don't specify a value for this parameter for a ContainerDefinition that is part of an inference pipeline, a unique name is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value for the ContainerHostName for any ContainerDefinition that is part of an inference pipeline, you must specify a value for the ContainerHostName parameter of every ContainerDefinition in that pipeline.
The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. If you are using your own custom algorithm instead of an algorithm provided by Amazon SageMaker, the inference code must meet Amazon SageMaker requirements. Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker
Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers
Set this to one of the following values:
(Optional) Specifies an authentication configuration for the private docker registry where your model image is hosted. Specify a value for this property only if you specified Vpc as the value for the RepositoryAccessMode field, and the private Docker registry where the model image is hosted requires authentication.
The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function that provides credentials to authenticate to the private Docker registry where your model image is hosted. For information about how to create an Amazon Web Services Lambda function, see Create a Lambda function with the console in the Amazon Web Services Lambda Developer Guide .
Whether the container hosts a single model or multiple models.
The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). The S3 path is required for Amazon SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters .
Note
The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.
If you provide a value for this parameter, Amazon SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provide. Amazon Web Services STS is activated in your IAM user account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide .
Warning
If you use a built-in algorithm to create a model, Amazon SageMaker requires that you provide a S3 path to the model artifacts in ModelDataUrl .
The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
The name or Amazon Resource Name (ARN) of the model package to use to create the model.
Specifies additional configuration for multi-model endpoints.
Whether to cache models for a multi-model endpoint. By default, multi-model endpoints cache models so that a model does not have to be loaded into memory each time it is invoked. Some use cases do not benefit from model caching. For example, if an endpoint hosts a large number of models that are each invoked infrequently, the endpoint might perform better if you disable model caching. To disable model caching, set the value of this parameter to Disabled .
Specifies details of how containers in a multi-container endpoint are called.
How containers in a multi-container are run. The following values are valid.
[REQUIRED]
The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker can assume to access model artifacts and docker image for deployment on ML compute instances or for batch transform jobs. Deploying on ML compute instances is part of model hosting. For more information, see Amazon SageMaker Roles .
Note
To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission.
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources .
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
A VpcConfig object that specifies the VPC that you want your model to connect to. Control access to and from your model container by configuring the VPC. VpcConfig is used in hosting services and in batch transform. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Data in Batch Transform Jobs by Using an Amazon Virtual Private Cloud .
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
dict
Response Syntax
{
'ModelArn': 'string'
}
Response Structure
(dict) --
ModelArn (string) --
The ARN of the model created in Amazon SageMaker.
Exceptions
Creates the definition for a model bias job.
See also: AWS API Documentation
Request Syntax
response = client.create_model_bias_job_definition(
JobDefinitionName='string',
ModelBiasBaselineConfig={
'BaseliningJobName': 'string',
'ConstraintsResource': {
'S3Uri': 'string'
}
},
ModelBiasAppSpecification={
'ImageUri': 'string',
'ConfigUri': 'string',
'Environment': {
'string': 'string'
}
},
ModelBiasJobInput={
'EndpointInput': {
'EndpointName': 'string',
'LocalPath': 'string',
'S3InputMode': 'Pipe'|'File',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'FeaturesAttribute': 'string',
'InferenceAttribute': 'string',
'ProbabilityAttribute': 'string',
'ProbabilityThresholdAttribute': 123.0,
'StartTimeOffset': 'string',
'EndTimeOffset': 'string'
},
'GroundTruthS3Input': {
'S3Uri': 'string'
}
},
ModelBiasJobOutputConfig={
'MonitoringOutputs': [
{
'S3Output': {
'S3Uri': 'string',
'LocalPath': 'string',
'S3UploadMode': 'Continuous'|'EndOfJob'
}
},
],
'KmsKeyId': 'string'
},
JobResources={
'ClusterConfig': {
'InstanceCount': 123,
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
}
},
NetworkConfig={
'EnableInterContainerTrafficEncryption': True|False,
'EnableNetworkIsolation': True|False,
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
},
RoleArn='string',
StoppingCondition={
'MaxRuntimeInSeconds': 123
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
[REQUIRED]
The name of the bias job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
The baseline configuration for a model bias job.
The name of the baseline model bias job.
The constraints resource for a monitoring job.
The Amazon S3 URI for the constraints resource.
[REQUIRED]
Configures the model bias job to run a specified Docker container image.
The container image to be run by the model bias job.
JSON formatted S3 file that defines bias parameters. For more information on this JSON configuration file, see Configure bias parameters .
Sets the environment variables in the Docker container.
[REQUIRED]
Inputs for the model bias job.
Input object for the endpoint
An endpoint in customer's account which has enabled DataCaptureConfig enabled.
Path to the filesystem where the endpoint data is available to the container.
Whether the Pipe or File is used as the input mode for transfering data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .
Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated
The attributes of the input data that are the input features.
The attribute of the input data that represents the ground truth label.
In a classification problem, the attribute that represents the class probability.
The threshold for the class probability to be evaluated as a positive result.
If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
Location of ground truth labels to use in model bias job.
The address of the Amazon S3 location of the ground truth labels.
[REQUIRED]
The output configuration for monitoring jobs.
Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.
The output object for a monitoring job.
The Amazon S3 storage location where the results of a monitoring job are saved.
A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.
The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.
Whether to upload the results of the monitoring job continuously or after the job completes.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.
[REQUIRED]
Identifies the resources to deploy for a monitoring job.
The configuration for the cluster resources used to run the processing job.
The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
The ML compute instance type for the processing job.
The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job.
Networking options for a model bias job.
Whether to encrypt all communications between the instances used for the monitoring jobs. Choose True to encrypt communications. Encryption provides greater security for distributed jobs, but the processing might take longer.
Whether to allow inbound and outbound network calls to and from the containers used for the monitoring job.
Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud .
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
[REQUIRED]
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
A time limit for how long the monitoring job is allowed to run before stopping.
The maximum runtime allowed in seconds.
Note
The MaxRuntimeInSeconds cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.
(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide .
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
dict
Response Syntax
{
'JobDefinitionArn': 'string'
}
Response Structure
(dict) --
JobDefinitionArn (string) --
The Amazon Resource Name (ARN) of the model bias job.
Exceptions
Creates the definition for a model explainability job.
See also: AWS API Documentation
Request Syntax
response = client.create_model_explainability_job_definition(
JobDefinitionName='string',
ModelExplainabilityBaselineConfig={
'BaseliningJobName': 'string',
'ConstraintsResource': {
'S3Uri': 'string'
}
},
ModelExplainabilityAppSpecification={
'ImageUri': 'string',
'ConfigUri': 'string',
'Environment': {
'string': 'string'
}
},
ModelExplainabilityJobInput={
'EndpointInput': {
'EndpointName': 'string',
'LocalPath': 'string',
'S3InputMode': 'Pipe'|'File',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'FeaturesAttribute': 'string',
'InferenceAttribute': 'string',
'ProbabilityAttribute': 'string',
'ProbabilityThresholdAttribute': 123.0,
'StartTimeOffset': 'string',
'EndTimeOffset': 'string'
}
},
ModelExplainabilityJobOutputConfig={
'MonitoringOutputs': [
{
'S3Output': {
'S3Uri': 'string',
'LocalPath': 'string',
'S3UploadMode': 'Continuous'|'EndOfJob'
}
},
],
'KmsKeyId': 'string'
},
JobResources={
'ClusterConfig': {
'InstanceCount': 123,
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
}
},
NetworkConfig={
'EnableInterContainerTrafficEncryption': True|False,
'EnableNetworkIsolation': True|False,
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
},
RoleArn='string',
StoppingCondition={
'MaxRuntimeInSeconds': 123
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
[REQUIRED]
The name of the model explainability job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
The baseline configuration for a model explainability job.
The name of the baseline model explainability job.
The constraints resource for a monitoring job.
The Amazon S3 URI for the constraints resource.
[REQUIRED]
Configures the model explainability job to run a specified Docker container image.
The container image to be run by the model explainability job.
JSON formatted S3 file that defines explainability parameters. For more information on this JSON configuration file, see Configure model explainability parameters .
Sets the environment variables in the Docker container.
[REQUIRED]
Inputs for the model explainability job.
Input object for the endpoint
An endpoint in customer's account which has enabled DataCaptureConfig enabled.
Path to the filesystem where the endpoint data is available to the container.
Whether the Pipe or File is used as the input mode for transfering data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .
Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated
The attributes of the input data that are the input features.
The attribute of the input data that represents the ground truth label.
In a classification problem, the attribute that represents the class probability.
The threshold for the class probability to be evaluated as a positive result.
If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
[REQUIRED]
The output configuration for monitoring jobs.
Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.
The output object for a monitoring job.
The Amazon S3 storage location where the results of a monitoring job are saved.
A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.
The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.
Whether to upload the results of the monitoring job continuously or after the job completes.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.
[REQUIRED]
Identifies the resources to deploy for a monitoring job.
The configuration for the cluster resources used to run the processing job.
The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
The ML compute instance type for the processing job.
The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job.
Networking options for a model explainability job.
Whether to encrypt all communications between the instances used for the monitoring jobs. Choose True to encrypt communications. Encryption provides greater security for distributed jobs, but the processing might take longer.
Whether to allow inbound and outbound network calls to and from the containers used for the monitoring job.
Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud .
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
[REQUIRED]
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
A time limit for how long the monitoring job is allowed to run before stopping.
The maximum runtime allowed in seconds.
Note
The MaxRuntimeInSeconds cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.
(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide .
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
dict
Response Syntax
{
'JobDefinitionArn': 'string'
}
Response Structure
(dict) --
JobDefinitionArn (string) --
The Amazon Resource Name (ARN) of the model explainability job.
Exceptions
Creates a model package that you can use to create Amazon SageMaker models or list on Amazon Web Services Marketplace, or a versioned model that is part of a model group. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in Amazon SageMaker.
To create a model package by specifying a Docker container that contains your inference code and the Amazon S3 location of your model artifacts, provide values for InferenceSpecification . To create a model from an algorithm resource that you created or subscribed to in Amazon Web Services Marketplace, provide a value for SourceAlgorithmSpecification .
Note
There are two types of model packages:
See also: AWS API Documentation
Request Syntax
response = client.create_model_package(
ModelPackageName='string',
ModelPackageGroupName='string',
ModelPackageDescription='string',
InferenceSpecification={
'Containers': [
{
'ContainerHostname': 'string',
'Image': 'string',
'ImageDigest': 'string',
'ModelDataUrl': 'string',
'ProductId': 'string',
'Environment': {
'string': 'string'
}
},
],
'SupportedTransformInstanceTypes': [
'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
],
'SupportedRealtimeInferenceInstanceTypes': [
'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge',
],
'SupportedContentTypes': [
'string',
],
'SupportedResponseMIMETypes': [
'string',
]
},
ValidationSpecification={
'ValidationRole': 'string',
'ValidationProfiles': [
{
'ProfileName': 'string',
'TransformJobDefinition': {
'MaxConcurrentTransforms': 123,
'MaxPayloadInMB': 123,
'BatchStrategy': 'MultiRecord'|'SingleRecord',
'Environment': {
'string': 'string'
},
'TransformInput': {
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'SplitType': 'None'|'Line'|'RecordIO'|'TFRecord'
},
'TransformOutput': {
'S3OutputPath': 'string',
'Accept': 'string',
'AssembleWith': 'None'|'Line',
'KmsKeyId': 'string'
},
'TransformResources': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'InstanceCount': 123,
'VolumeKmsKeyId': 'string'
}
}
},
]
},
SourceAlgorithmSpecification={
'SourceAlgorithms': [
{
'ModelDataUrl': 'string',
'AlgorithmName': 'string'
},
]
},
CertifyForMarketplace=True|False,
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
ModelApprovalStatus='Approved'|'Rejected'|'PendingManualApproval',
MetadataProperties={
'CommitId': 'string',
'Repository': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string'
},
ModelMetrics={
'ModelQuality': {
'Statistics': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'Constraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'ModelDataQuality': {
'Statistics': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'Constraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'Bias': {
'Report': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'Explainability': {
'Report': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
}
},
ClientToken='string'
)
The name of the model package. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
This parameter is required for unversioned models. It is not applicable to versioned models.
The name of the model group that this model version belongs to.
This parameter is required for versioned models, and does not apply to unversioned models.
Specifies details about inference jobs that can be run with models based on this model package, including the following:
The Amazon ECR registry path of the Docker image that contains the inference code.
Describes the Docker container for the model package.
The DNS host name for the Docker container.
The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by Amazon SageMaker, the inference code must meet Amazon SageMaker requirements. Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
An MD5 hash of the training algorithm that identifies the Docker image used for training.
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
Note
The model artifacts must be in an S3 bucket that is in the same region as the model package.
The Amazon Web Services Marketplace product ID of the model package.
The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
This parameter is required for unversioned models, and optional for versioned models.
A list of the instance types that are used to generate inferences in real-time.
This parameter is required for unversioned models, and optional for versioned models.
The supported MIME types for the input data.
The supported MIME types for the output data.
Specifies configurations for one or more transform jobs that Amazon SageMaker runs to test the model package.
The IAM roles to be used for the validation of the model package.
An array of ModelPackageValidationProfile objects, each of which specifies a batch transform job that Amazon SageMaker runs to validate your model package.
Contains data, such as the inputs and targeted instance types that are used in the process of validating the model package.
The data provided in the validation profile is made available to your buyers on Amazon Web Services Marketplace.
The name of the profile for the model package.
The TransformJobDefinition object that describes the transform job used for the validation of the model package.
The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1.
The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata).
A string that determines the number of records included in a single mini-batch.
SingleRecord means only one record is used per mini-batch. MultiRecord means a mini-batch is set to contain as many records that can fit within the MaxPayloadInMB limit.
The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
A description of the input source and the way the transform job consumes it.
Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
The S3 location of the data source that is associated with a channel.
If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.
The following values are compatible: ManifestFile , S3Prefix
The following value is not compatible: AugmentedManifestFile
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None .
The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None , which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:
When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord , Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord , Amazon SageMaker sends individual records in each request.
Note
Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord . Padding is not removed if the value of BatchStrategy is set to MultiRecord .
For more information about RecordIO , see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord , see Consuming TFRecord data in the TensorFlow documentation.
Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, s3://bucket-name/key-name-prefix .
For every S3 object used as input for the transform job, batch transform stores the transformed data with an .``out`` suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at s3://bucket-name/input-name-prefix/dataset01/data.csv , batch transform stores the transformed data at s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out . Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .``out`` file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.
The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify None . To add a newline character at the end of every transformed record, specify Line .
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
Identifies the ML compute instances for the transform job.
The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or ml.m5.large instance types.
The number of ML compute instances to use in the transform job. For distributed transform jobs, specify a value greater than 1. The default value is 1 .
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be any of the following formats:
Details about the algorithm that was used to create the model package.
A list of the algorithms that were used to create a model package.
Specifies an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your Amazon SageMaker account or an algorithm in Amazon Web Services Marketplace that you are subscribed to.
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
Note
The model artifacts must be in an S3 bucket that is in the same region as the algorithm.
The name of an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your Amazon SageMaker account or an algorithm in Amazon Web Services Marketplace that you are subscribed to.
Whether to certify the model package for listing on Amazon Web Services Marketplace.
This parameter is optional for unversioned models, and does not apply to versioned models.
A list of key value pairs associated with the model. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide .
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
Whether the model is approved for deployment.
This parameter is optional for versioned models, and does not apply to unversioned models.
For versioned models, the value of this parameter must be set to Approved to deploy the model.
Metadata properties of the tracking entity, trial, or trial component.
The commit ID.
The repository.
The entity this entity was generated by.
The project ID.
A structure that contains model metrics reports.
Metrics that measure the quality of a model.
Model quality statistics.
Model quality constraints.
Metrics that measure the quality of the input data for a model.
Data quality statistics for a model.
Data quality constraints for a model.
Metrics that measure bais in a model.
The bias report for a model
Metrics that help explain a model.
The explainability report for a model.
A unique token that guarantees that the call to this API is idempotent.
This field is autopopulated if not provided.
dict
Response Syntax
{
'ModelPackageArn': 'string'
}
Response Structure
(dict) --
ModelPackageArn (string) --
The Amazon Resource Name (ARN) of the new model package.
Exceptions
Creates a model group. A model group contains a group of model versions.
See also: AWS API Documentation
Request Syntax
response = client.create_model_package_group(
ModelPackageGroupName='string',
ModelPackageGroupDescription='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
[REQUIRED]
The name of the model group.
A list of key value pairs associated with the model group. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide .
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
dict
Response Syntax
{
'ModelPackageGroupArn': 'string'
}
Response Structure
(dict) --
ModelPackageGroupArn (string) --
The Amazon Resource Name (ARN) of the model group.
Exceptions
Creates a definition for a job that monitors model quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor .
See also: AWS API Documentation
Request Syntax
response = client.create_model_quality_job_definition(
JobDefinitionName='string',
ModelQualityBaselineConfig={
'BaseliningJobName': 'string',
'ConstraintsResource': {
'S3Uri': 'string'
}
},
ModelQualityAppSpecification={
'ImageUri': 'string',
'ContainerEntrypoint': [
'string',
],
'ContainerArguments': [
'string',
],
'RecordPreprocessorSourceUri': 'string',
'PostAnalyticsProcessorSourceUri': 'string',
'ProblemType': 'BinaryClassification'|'MulticlassClassification'|'Regression',
'Environment': {
'string': 'string'
}
},
ModelQualityJobInput={
'EndpointInput': {
'EndpointName': 'string',
'LocalPath': 'string',
'S3InputMode': 'Pipe'|'File',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'FeaturesAttribute': 'string',
'InferenceAttribute': 'string',
'ProbabilityAttribute': 'string',
'ProbabilityThresholdAttribute': 123.0,
'StartTimeOffset': 'string',
'EndTimeOffset': 'string'
},
'GroundTruthS3Input': {
'S3Uri': 'string'
}
},
ModelQualityJobOutputConfig={
'MonitoringOutputs': [
{
'S3Output': {
'S3Uri': 'string',
'LocalPath': 'string',
'S3UploadMode': 'Continuous'|'EndOfJob'
}
},
],
'KmsKeyId': 'string'
},
JobResources={
'ClusterConfig': {
'InstanceCount': 123,
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
}
},
NetworkConfig={
'EnableInterContainerTrafficEncryption': True|False,
'EnableNetworkIsolation': True|False,
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
},
RoleArn='string',
StoppingCondition={
'MaxRuntimeInSeconds': 123
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
[REQUIRED]
The name of the monitoring job definition.
Specifies the constraints and baselines for the monitoring job.
The name of the job that performs baselining for the monitoring job.
The constraints resource for a monitoring job.
The Amazon S3 URI for the constraints resource.
[REQUIRED]
The container that runs the monitoring job.
The address of the container image that the monitoring job runs.
Specifies the entrypoint for a container that the monitoring job runs.
An array of arguments for the container used to run the monitoring job.
An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flatted json so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.
An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.
The machine learning problem type of the model that the monitoring job monitors.
Sets the environment variables in the container that the monitoring job runs.
[REQUIRED]
A list of the inputs that are monitored. Currently endpoints are supported.
Input object for the endpoint
An endpoint in customer's account which has enabled DataCaptureConfig enabled.
Path to the filesystem where the endpoint data is available to the container.
Whether the Pipe or File is used as the input mode for transfering data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .
Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated
The attributes of the input data that are the input features.
The attribute of the input data that represents the ground truth label.
In a classification problem, the attribute that represents the class probability.
The threshold for the class probability to be evaluated as a positive result.
If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
The ground truth label provided for the model.
The address of the Amazon S3 location of the ground truth labels.
[REQUIRED]
The output configuration for monitoring jobs.
Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.
The output object for a monitoring job.
The Amazon S3 storage location where the results of a monitoring job are saved.
A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.
The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.
Whether to upload the results of the monitoring job continuously or after the job completes.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.
[REQUIRED]
Identifies the resources to deploy for a monitoring job.
The configuration for the cluster resources used to run the processing job.
The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
The ML compute instance type for the processing job.
The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job.
Specifies the network configuration for the monitoring job.
Whether to encrypt all communications between the instances used for the monitoring jobs. Choose True to encrypt communications. Encryption provides greater security for distributed jobs, but the processing might take longer.
Whether to allow inbound and outbound network calls to and from the containers used for the monitoring job.
Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud .
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
[REQUIRED]
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
A time limit for how long the monitoring job is allowed to run before stopping.
The maximum runtime allowed in seconds.
Note
The MaxRuntimeInSeconds cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.
(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide .
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
dict
Response Syntax
{
'JobDefinitionArn': 'string'
}
Response Structure
(dict) --
JobDefinitionArn (string) --
The Amazon Resource Name (ARN) of the model quality monitoring job.
Exceptions
Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endoint.
See also: AWS API Documentation
Request Syntax
response = client.create_monitoring_schedule(
MonitoringScheduleName='string',
MonitoringScheduleConfig={
'ScheduleConfig': {
'ScheduleExpression': 'string'
},
'MonitoringJobDefinition': {
'BaselineConfig': {
'BaseliningJobName': 'string',
'ConstraintsResource': {
'S3Uri': 'string'
},
'StatisticsResource': {
'S3Uri': 'string'
}
},
'MonitoringInputs': [
{
'EndpointInput': {
'EndpointName': 'string',
'LocalPath': 'string',
'S3InputMode': 'Pipe'|'File',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'FeaturesAttribute': 'string',
'InferenceAttribute': 'string',
'ProbabilityAttribute': 'string',
'ProbabilityThresholdAttribute': 123.0,
'StartTimeOffset': 'string',
'EndTimeOffset': 'string'
}
},
],
'MonitoringOutputConfig': {
'MonitoringOutputs': [
{
'S3Output': {
'S3Uri': 'string',
'LocalPath': 'string',
'S3UploadMode': 'Continuous'|'EndOfJob'
}
},
],
'KmsKeyId': 'string'
},
'MonitoringResources': {
'ClusterConfig': {
'InstanceCount': 123,
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
}
},
'MonitoringAppSpecification': {
'ImageUri': 'string',
'ContainerEntrypoint': [
'string',
],
'ContainerArguments': [
'string',
],
'RecordPreprocessorSourceUri': 'string',
'PostAnalyticsProcessorSourceUri': 'string'
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123
},
'Environment': {
'string': 'string'
},
'NetworkConfig': {
'EnableInterContainerTrafficEncryption': True|False,
'EnableNetworkIsolation': True|False,
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
},
'RoleArn': 'string'
},
'MonitoringJobDefinitionName': 'string',
'MonitoringType': 'DataQuality'|'ModelQuality'|'ModelBias'|'ModelExplainability'
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
[REQUIRED]
The name of the monitoring schedule. The name must be unique within an Amazon Web Services Region within an Amazon Web Services account.
[REQUIRED]
The configuration object that specifies the monitoring schedule and defines the monitoring job.
Configures the monitoring schedule.
A cron expression that describes details about the monitoring schedule.
Currently the only supported cron expressions are:
For example, the following are valid cron expressions:
To support running every 6, 12 hours, the following are also supported:
cron(0 [00-23]/[01-24] ? * * *)
For example, the following are valid cron expressions:
Note
Defines the monitoring job.
Baseline configuration used to validate that the data conforms to the specified constraints and statistics
The name of the job that performs baselining for the monitoring job.
The baseline constraint file in Amazon S3 that the current monitoring job should validated against.
The Amazon S3 URI for the constraints resource.
The baseline statistics file in Amazon S3 that the current monitoring job should be validated against.
The Amazon S3 URI for the statistics resource.
The array of inputs for the monitoring job. Currently we support monitoring an Amazon SageMaker Endpoint.
The inputs for a monitoring job.
The endpoint for a monitoring job.
An endpoint in customer's account which has enabled DataCaptureConfig enabled.
Path to the filesystem where the endpoint data is available to the container.
Whether the Pipe or File is used as the input mode for transfering data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .
Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated
The attributes of the input data that are the input features.
The attribute of the input data that represents the ground truth label.
In a classification problem, the attribute that represents the class probability.
The threshold for the class probability to be evaluated as a positive result.
If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
The array of outputs from the monitoring job to be uploaded to Amazon Simple Storage Service (Amazon S3).
Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.
The output object for a monitoring job.
The Amazon S3 storage location where the results of a monitoring job are saved.
A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.
The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.
Whether to upload the results of the monitoring job continuously or after the job completes.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.
Identifies the resources, ML compute instances, and ML storage volumes to deploy for a monitoring job. In distributed processing, you specify more than one instance.
The configuration for the cluster resources used to run the processing job.
The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
The ML compute instance type for the processing job.
The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job.
Configures the monitoring job to run a specified Docker container image.
The container image to be run by the monitoring job.
Specifies the entrypoint for a container used to run the monitoring job.
An array of arguments for the container used to run the monitoring job.
An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flatted json so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.
An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.
Specifies a time limit for how long the monitoring job is allowed to run.
The maximum runtime allowed in seconds.
Note
The MaxRuntimeInSeconds cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.
Sets the environment variables in the Docker container.
Specifies networking options for an monitoring job.
Whether to encrypt all communications between distributed processing jobs. Choose True to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer.
Whether to allow inbound and outbound network calls to and from the containers used for the processing job.
Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud .
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
The name of the monitoring job definition to schedule.
The type of the monitoring job definition to schedule.
(Optional) An array of key-value pairs. For more information, see `Using Cost Allocation Tags < https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-whatURL>`__ in the Amazon Web Services Billing and Cost Management User Guide .
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
dict
Response Syntax
{
'MonitoringScheduleArn': 'string'
}
Response Structure
(dict) --
MonitoringScheduleArn (string) --
The Amazon Resource Name (ARN) of the monitoring schedule.
Exceptions
Creates an Amazon SageMaker notebook instance. A notebook instance is a machine learning (ML) compute instance running on a Jupyter notebook.
In a CreateNotebookInstance request, specify the type of ML compute instance that you want to run. Amazon SageMaker launches the instance, installs common libraries that you can use to explore datasets for model training, and attaches an ML storage volume to the notebook instance.
Amazon SageMaker also provides a set of example notebooks. Each notebook demonstrates how to use Amazon SageMaker with a specific algorithm or with a machine learning framework.
After receiving the request, Amazon SageMaker does the following:
After creating the notebook instance, Amazon SageMaker returns its Amazon Resource Name (ARN). You can't change the name of a notebook instance after you create it.
After Amazon SageMaker creates the notebook instance, you can connect to the Jupyter server and work in Jupyter notebooks. For example, you can write code to explore a dataset that you can use for model training, train a model, host models by creating Amazon SageMaker endpoints, and validate hosted models.
For more information, see How It Works .
See also: AWS API Documentation
Request Syntax
response = client.create_notebook_instance(
NotebookInstanceName='string',
InstanceType='ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge',
SubnetId='string',
SecurityGroupIds=[
'string',
],
RoleArn='string',
KmsKeyId='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
LifecycleConfigName='string',
DirectInternetAccess='Enabled'|'Disabled',
VolumeSizeInGB=123,
AcceleratorTypes=[
'ml.eia1.medium'|'ml.eia1.large'|'ml.eia1.xlarge'|'ml.eia2.medium'|'ml.eia2.large'|'ml.eia2.xlarge',
],
DefaultCodeRepository='string',
AdditionalCodeRepositories=[
'string',
],
RootAccess='Enabled'|'Disabled'
)
[REQUIRED]
The name of the new notebook instance.
[REQUIRED]
The type of ML compute instance to launch for the notebook instance.
The VPC security group IDs, in the form sg-xxxxxxxx. The security groups must be for the same VPC as specified in the subnet.
[REQUIRED]
When you send any requests to Amazon Web Services resources from the notebook instance, Amazon SageMaker assumes this role to perform tasks on your behalf. You must grant this role necessary permissions so Amazon SageMaker can perform these tasks. The policy must allow the Amazon SageMaker service principal (sagemaker.amazonaws.com) permissions to assume this role. For more information, see Amazon SageMaker Roles .
Note
To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission.
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources .
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
Sets whether Amazon SageMaker provides internet access to the notebook instance. If you set this to Disabled this notebook instance is able to access resources only in your VPC, and is not be able to connect to Amazon SageMaker training and endpoint services unless you configure a NAT Gateway in your VPC.
For more information, see Notebook Instances Are Internet-Enabled by Default . You can set the value of this parameter to Disabled only if you set a value for the SubnetId parameter.
A list of Elastic Inference (EI) instance types to associate with this notebook instance. Currently, only one instance type can be associated with a notebook instance. For more information, see Using Elastic Inference in Amazon SageMaker .
An array of up to three Git repositories to associate with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in Amazon Web Services CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances .
Whether root access is enabled or disabled for users of the notebook instance. The default value is Enabled .
Note
Lifecycle configurations need root access to be able to set up a notebook instance. Because of this, lifecycle configurations associated with a notebook instance always run with root access even if you disable root access for users.
dict
Response Syntax
{
'NotebookInstanceArn': 'string'
}
Response Structure
(dict) --
NotebookInstanceArn (string) --
The Amazon Resource Name (ARN) of the notebook instance.
Exceptions
Creates a lifecycle configuration that you can associate with a notebook instance. A lifecycle configuration is a collection of shell scripts that run when you create or start a notebook instance.
Each lifecycle configuration script has a limit of 16384 characters.
The value of the $PATH environment variable that is available to both scripts is /sbin:bin:/usr/sbin:/usr/bin .
View CloudWatch Logs for notebook instance lifecycle configurations in log group /aws/sagemaker/NotebookInstances in log stream [notebook-instance-name]/[LifecycleConfigHook] .
Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or started.
For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance .
See also: AWS API Documentation
Request Syntax
response = client.create_notebook_instance_lifecycle_config(
NotebookInstanceLifecycleConfigName='string',
OnCreate=[
{
'Content': 'string'
},
],
OnStart=[
{
'Content': 'string'
},
]
)
[REQUIRED]
The name of the lifecycle configuration.
A shell script that runs only once, when you create a notebook instance. The shell script must be a base64-encoded string.
Contains the notebook instance lifecycle configuration script.
Each lifecycle configuration script has a limit of 16384 characters.
The value of the $PATH environment variable that is available to both scripts is /sbin:bin:/usr/sbin:/usr/bin .
View CloudWatch Logs for notebook instance lifecycle configurations in log group /aws/sagemaker/NotebookInstances in log stream [notebook-instance-name]/[LifecycleConfigHook] .
Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or started.
For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance .
A base64-encoded string that contains a shell script for a notebook instance lifecycle configuration.
A shell script that runs every time you start a notebook instance, including when you create the notebook instance. The shell script must be a base64-encoded string.
Contains the notebook instance lifecycle configuration script.
Each lifecycle configuration script has a limit of 16384 characters.
The value of the $PATH environment variable that is available to both scripts is /sbin:bin:/usr/sbin:/usr/bin .
View CloudWatch Logs for notebook instance lifecycle configurations in log group /aws/sagemaker/NotebookInstances in log stream [notebook-instance-name]/[LifecycleConfigHook] .
Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or started.
For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance .
A base64-encoded string that contains a shell script for a notebook instance lifecycle configuration.
dict
Response Syntax
{
'NotebookInstanceLifecycleConfigArn': 'string'
}
Response Structure
(dict) --
NotebookInstanceLifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the lifecycle configuration.
Exceptions
Creates a pipeline using a JSON pipeline definition.
See also: AWS API Documentation
Request Syntax
response = client.create_pipeline(
PipelineName='string',
PipelineDisplayName='string',
PipelineDefinition='string',
PipelineDescription='string',
ClientRequestToken='string',
RoleArn='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
[REQUIRED]
The name of the pipeline.
[REQUIRED]
The JSON pipeline definition of the pipeline.
[REQUIRED]
A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.
This field is autopopulated if not provided.
[REQUIRED]
The Amazon Resource Name (ARN) of the role used by the pipeline to access and create resources.
A list of tags to apply to the created pipeline.
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
dict
Response Syntax
{
'PipelineArn': 'string'
}
Response Structure
(dict) --
PipelineArn (string) --
The Amazon Resource Name (ARN) of the created pipeline.
Exceptions
Creates a URL for a specified UserProfile in a Domain. When accessed in a web browser, the user will be automatically signed in to Amazon SageMaker Studio, and granted access to all of the Apps and files associated with the Domain's Amazon Elastic File System (EFS) volume. This operation can only be called when the authentication mode equals IAM.
The IAM role or user used to call this API defines the permissions to access the app. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the app.
You can restrict access to this API and to the URL that it returns to a list of IP addresses, Amazon VPCs or Amazon VPC Endpoints that you specify. For more information, see Connect to SageMaker Studio Through an Interface VPC Endpoint .
Note
The URL that you get from a call to CreatePresignedDomainUrl has a default timeout of 5 minutes. You can configure this value using ExpiresInSeconds . If you try to use the URL after the timeout limit expires, you are directed to the Amazon Web Services console sign-in page.
See also: AWS API Documentation
Request Syntax
response = client.create_presigned_domain_url(
DomainId='string',
UserProfileName='string',
SessionExpirationDurationInSeconds=123,
ExpiresInSeconds=123
)
[REQUIRED]
The domain ID.
[REQUIRED]
The name of the UserProfile to sign-in as.
dict
Response Syntax
{
'AuthorizedUrl': 'string'
}
Response Structure
(dict) --
AuthorizedUrl (string) --
The presigned URL.
Exceptions
Returns a URL that you can use to connect to the Jupyter server from a notebook instance. In the Amazon SageMaker console, when you choose Open next to a notebook instance, Amazon SageMaker opens a new tab showing the Jupyter server home page from the notebook instance. The console uses this API to get the URL and show the page.
The IAM role or user used to call this API defines the permissions to access the notebook instance. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the notebook instance.
You can restrict access to this API and to the URL that it returns to a list of IP addresses that you specify. Use the NotIpAddress condition operator and the aws:SourceIP condition context key to specify the list of IP addresses that you want to have access to the notebook instance. For more information, see Limit Access to a Notebook Instance by IP Address .
Note
The URL that you get from a call to CreatePresignedNotebookInstanceUrl is valid only for 5 minutes. If you try to use the URL after the 5-minute limit expires, you are directed to the Amazon Web Services console sign-in page.
See also: AWS API Documentation
Request Syntax
response = client.create_presigned_notebook_instance_url(
NotebookInstanceName='string',
SessionExpirationDurationInSeconds=123
)
[REQUIRED]
The name of the notebook instance.
dict
Response Syntax
{
'AuthorizedUrl': 'string'
}
Response Structure
(dict) --
AuthorizedUrl (string) --
A JSON object that contains the URL string.
Creates a processing job.
See also: AWS API Documentation
Request Syntax
response = client.create_processing_job(
ProcessingInputs=[
{
'InputName': 'string',
'AppManaged': True|False,
'S3Input': {
'S3Uri': 'string',
'LocalPath': 'string',
'S3DataType': 'ManifestFile'|'S3Prefix',
'S3InputMode': 'Pipe'|'File',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'S3CompressionType': 'None'|'Gzip'
},
'DatasetDefinition': {
'AthenaDatasetDefinition': {
'Catalog': 'string',
'Database': 'string',
'QueryString': 'string',
'WorkGroup': 'string',
'OutputS3Uri': 'string',
'KmsKeyId': 'string',
'OutputFormat': 'PARQUET'|'ORC'|'AVRO'|'JSON'|'TEXTFILE',
'OutputCompression': 'GZIP'|'SNAPPY'|'ZLIB'
},
'RedshiftDatasetDefinition': {
'ClusterId': 'string',
'Database': 'string',
'DbUser': 'string',
'QueryString': 'string',
'ClusterRoleArn': 'string',
'OutputS3Uri': 'string',
'KmsKeyId': 'string',
'OutputFormat': 'PARQUET'|'CSV',
'OutputCompression': 'None'|'GZIP'|'BZIP2'|'ZSTD'|'SNAPPY'
},
'LocalPath': 'string',
'DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'InputMode': 'Pipe'|'File'
}
},
],
ProcessingOutputConfig={
'Outputs': [
{
'OutputName': 'string',
'S3Output': {
'S3Uri': 'string',
'LocalPath': 'string',
'S3UploadMode': 'Continuous'|'EndOfJob'
},
'FeatureStoreOutput': {
'FeatureGroupName': 'string'
},
'AppManaged': True|False
},
],
'KmsKeyId': 'string'
},
ProcessingJobName='string',
ProcessingResources={
'ClusterConfig': {
'InstanceCount': 123,
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
}
},
StoppingCondition={
'MaxRuntimeInSeconds': 123
},
AppSpecification={
'ImageUri': 'string',
'ContainerEntrypoint': [
'string',
],
'ContainerArguments': [
'string',
]
},
Environment={
'string': 'string'
},
NetworkConfig={
'EnableInterContainerTrafficEncryption': True|False,
'EnableNetworkIsolation': True|False,
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
},
RoleArn='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
ExperimentConfig={
'ExperimentName': 'string',
'TrialName': 'string',
'TrialComponentDisplayName': 'string'
}
)
An array of inputs configuring the data to download into the processing container.
The inputs for a processing job. The processing input must specify exactly one of either S3Input or DatasetDefinition types.
The name for the processing job input.
When True , input operations such as data download are managed natively by the processing job application. When False (default), input operations are managed by Amazon SageMaker.
Configuration for downloading input data from Amazon S3 into the processing container.
The URI of the Amazon S3 prefix Amazon SageMaker downloads data required to run a processing job.
The local path in your container where you want Amazon SageMaker to write input data to. LocalPath is an absolute path to the input data and must begin with /opt/ml/processing/ . LocalPath is a required parameter when AppManaged is False (default).
Whether you use an S3Prefix or a ManifestFile for the data type. If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for the processing job. If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for the processing job.
Whether to use File or Pipe input mode. In File mode, Amazon SageMaker copies the data from the input source onto the local ML storage volume before starting your processing container. This is the most commonly used input mode. In Pipe mode, Amazon SageMaker streams input data from the source directly to your processing container into named pipes without using the ML storage volume.
Whether to distribute the data from Amazon S3 to all processing instances with FullyReplicated , or whether the data from Amazon S3 is shared by Amazon S3 key, downloading one shard of data to each processing instance.
Whether to GZIP-decompress the data in Amazon S3 as it is streamed into the processing container. Gzip can only be used when Pipe mode is specified as the S3InputMode . In Pipe mode, Amazon SageMaker streams input data from the source directly to your container without using the EBS volume.
Configuration for a Dataset Definition input.
Configuration for Athena Dataset Definition input.
The name of the data catalog used in Athena query execution.
The name of the database used in the Athena query execution.
The SQL query statements, to be executed.
The name of the workgroup in which the Athena query is being started.
The location in Amazon S3 where Athena query results are stored.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data generated from an Athena query execution.
The data storage format for Athena query results.
The compression used for Athena query results.
Configuration for Redshift Dataset Definition input.
The Redshift cluster Identifier.
The name of the Redshift database used in Redshift query execution.
The database user name used in Redshift query execution.
The SQL query statements to be executed.
The IAM role attached to your Redshift cluster that Amazon SageMaker uses to generate datasets.
The location in Amazon S3 where the Redshift query results are stored.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data from a Redshift execution.
The data storage format for Redshift query results.
The compression used for Redshift query results.
The local path where you want Amazon SageMaker to download the Dataset Definition inputs to run a processing job. LocalPath is an absolute path to the input data. This is a required parameter when AppManaged is False (default).
Whether the generated dataset is FullyReplicated or ShardedByS3Key (default).
Whether to use File or Pipe input mode. In File (default) mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode. In Pipe mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume.
Output configuration for the processing job.
An array of outputs configuring the data to upload from the processing container.
Describes the results of a processing job. The processing output must specify exactly one of either S3Output or FeatureStoreOutput types.
The name for the processing job output.
Configuration for processing job outputs in Amazon S3.
A URI that identifies the Amazon S3 bucket where you want Amazon SageMaker to save the results of a processing job.
The local path of a directory where you want Amazon SageMaker to upload its contents to Amazon S3. LocalPath is an absolute path to a directory containing output files. This directory will be created by the platform and exist when your container's entrypoint is invoked.
Whether to upload the results of the processing job continuously or after the job completes.
Configuration for processing job outputs in Amazon SageMaker Feature Store. This processing output type is only supported when AppManaged is specified.
The name of the Amazon SageMaker FeatureGroup to use as the destination for processing job output. Note that your processing script is responsible for putting records into your Feature Store.
When True , output operations such as data upload are managed natively by the processing job application. When False (default), output operations are managed by Amazon SageMaker.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the processing job output. KmsKeyId can be an ID of a KMS key, ARN of a KMS key, alias of a KMS key, or alias of a KMS key. The KmsKeyId is applied to all outputs.
[REQUIRED]
The name of the processing job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
[REQUIRED]
Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job. In distributed training, you specify more than one instance.
The configuration for the resources in a cluster used to run the processing job.
The number of ML compute instances to use in the processing job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
The ML compute instance type for the processing job.
The size of the ML storage volume in gigabytes that you want to provision. You must specify sufficient ML storage for your scenario.
Note
Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for processing, Amazon SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can't request a VolumeSizeInGB greater than the total size of the local instance storage.
For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes .
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the processing job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The time limit for how long the processing job is allowed to run.
Specifies the maximum runtime in seconds.
[REQUIRED]
Configures the processing job to run a specified Docker container image.
The container image to be run by the processing job.
The entrypoint for a container used to run a processing job.
The arguments for a container used to run a processing job.
The environment variables to set in the Docker container. Up to 100 key and values entries in the map are supported.
Networking options for a processing job, such as whether to allow inbound and outbound network calls to and from processing containers, and the VPC subnets and security groups to use for VPC-enabled processing jobs.
Whether to encrypt all communications between distributed processing jobs. Choose True to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer.
Whether to allow inbound and outbound network calls to and from the containers used for the processing job.
Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud .
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
[REQUIRED]
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide .
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
The name of an existing experiment to associate the trial component with.
The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
The display name for the trial component. If this key isn't specified, the display name is the trial component name.
dict
Response Syntax
{
'ProcessingJobArn': 'string'
}
Response Structure
(dict) --
ProcessingJobArn (string) --
The Amazon Resource Name (ARN) of the processing job.
Exceptions
Creates a machine learning (ML) project that can contain one or more templates that set up an ML pipeline from training to deploying an approved model.
See also: AWS API Documentation
Request Syntax
response = client.create_project(
ProjectName='string',
ProjectDescription='string',
ServiceCatalogProvisioningDetails={
'ProductId': 'string',
'ProvisioningArtifactId': 'string',
'PathId': 'string',
'ProvisioningParameters': [
{
'Key': 'string',
'Value': 'string'
},
]
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
[REQUIRED]
The name of the project.
[REQUIRED]
The product ID and provisioning artifact ID to provision a service catalog. For information, see What is Amazon Web Services Service Catalog .
The ID of the product to provision.
The ID of the provisioning artifact.
The path identifier of the product. This value is optional if the product has a default path, and required if the product has more than one path.
A list of key value pairs that you specify when you provision a product.
A key value pair used when you provision a project as a service catalog product. For information, see What is Amazon Web Services Service Catalog .
The key that identifies a provisioning parameter.
The value of the provisioning parameter.
An array of key-value pairs that you want to use to organize and track your Amazon Web Services resource costs. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide .
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
dict
Response Syntax
{
'ProjectArn': 'string',
'ProjectId': 'string'
}
Response Structure
(dict) --
ProjectArn (string) --
The Amazon Resource Name (ARN) of the project.
ProjectId (string) --
The ID of the new project.
Exceptions
Starts a model training job. After training completes, Amazon SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify.
If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts in a machine learning service other than Amazon SageMaker, provided that you know how to use them for inference.
In the request body, you provide the following:
For more information about Amazon SageMaker, see How It Works .
See also: AWS API Documentation
Request Syntax
response = client.create_training_job(
TrainingJobName='string',
HyperParameters={
'string': 'string'
},
AlgorithmSpecification={
'TrainingImage': 'string',
'AlgorithmName': 'string',
'TrainingInputMode': 'Pipe'|'File',
'MetricDefinitions': [
{
'Name': 'string',
'Regex': 'string'
},
],
'EnableSageMakerMetricsTimeSeries': True|False
},
RoleArn='string',
InputDataConfig=[
{
'ChannelName': 'string',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'AttributeNames': [
'string',
]
},
'FileSystemDataSource': {
'FileSystemId': 'string',
'FileSystemAccessMode': 'rw'|'ro',
'FileSystemType': 'EFS'|'FSxLustre',
'DirectoryPath': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File',
'ShuffleConfig': {
'Seed': 123
}
},
],
OutputDataConfig={
'KmsKeyId': 'string',
'S3OutputPath': 'string'
},
ResourceConfig={
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
},
VpcConfig={
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
StoppingCondition={
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
EnableNetworkIsolation=True|False,
EnableInterContainerTrafficEncryption=True|False,
EnableManagedSpotTraining=True|False,
CheckpointConfig={
'S3Uri': 'string',
'LocalPath': 'string'
},
DebugHookConfig={
'LocalPath': 'string',
'S3OutputPath': 'string',
'HookParameters': {
'string': 'string'
},
'CollectionConfigurations': [
{
'CollectionName': 'string',
'CollectionParameters': {
'string': 'string'
}
},
]
},
DebugRuleConfigurations=[
{
'RuleConfigurationName': 'string',
'LocalPath': 'string',
'S3OutputPath': 'string',
'RuleEvaluatorImage': 'string',
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'RuleParameters': {
'string': 'string'
}
},
],
TensorBoardOutputConfig={
'LocalPath': 'string',
'S3OutputPath': 'string'
},
ExperimentConfig={
'ExperimentName': 'string',
'TrialName': 'string',
'TrialComponentDisplayName': 'string'
},
ProfilerConfig={
'S3OutputPath': 'string',
'ProfilingIntervalInMilliseconds': 123,
'ProfilingParameters': {
'string': 'string'
}
},
ProfilerRuleConfigurations=[
{
'RuleConfigurationName': 'string',
'LocalPath': 'string',
'S3OutputPath': 'string',
'RuleEvaluatorImage': 'string',
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'RuleParameters': {
'string': 'string'
}
},
],
Environment={
'string': 'string'
},
RetryStrategy={
'MaximumRetryAttempts': 123
}
)
[REQUIRED]
The name of the training job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.
Algorithm-specific parameters that influence the quality of the model. You set hyperparameters before you start the learning process. For a list of hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms .
You can specify a maximum of 100 hyperparameters. Each hyperparameter is a key-value pair. Each key and value is limited to 256 characters, as specified by the Length Constraint .
[REQUIRED]
The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata, including the input mode. For more information about algorithms provided by Amazon SageMaker, see Algorithms . For information about providing your own algorithms, see Using Your Own Algorithms with Amazon SageMaker .
The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters . Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
The name of the algorithm resource to use for the training job. This must be an algorithm resource that you created or subscribe to on Amazon Web Services Marketplace. If you specify a value for this parameter, you can't specify a value for TrainingImage .
The input mode that the algorithm supports. For the input modes that Amazon SageMaker algorithms support, see Algorithms . If an algorithm supports the File input mode, Amazon SageMaker downloads the training data from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an algorithm supports the Pipe input mode, Amazon SageMaker streams data directly from S3 to the container.
In File mode, make sure you provision ML storage volume with sufficient capacity to accommodate the data download from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container use ML storage volume to also store intermediate information, if any.
For distributed algorithms using File mode, training data is distributed uniformly, and your training duration is predictable if the input data objects size is approximately same. Amazon SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed where one host in a training cluster is overloaded, thus becoming bottleneck in training.
A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. Amazon SageMaker publishes each metric to Amazon CloudWatch.
Specifies a metric that the training algorithm writes to stderr or stdout . Amazon SageMakerhyperparameter tuning captures all defined metrics. You specify one metric that a hyperparameter tuning job uses as its objective metric to choose the best training job.
The name of the metric.
A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining Objective Metrics .
To generate and save time-series metrics during training, set to true . The default is false and time-series metrics aren't generated except in the following cases:
[REQUIRED]
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
During model training, Amazon SageMaker needs your permission to read input data from an S3 bucket, download a Docker image that contains training code, write model artifacts to an S3 bucket, write logs to Amazon CloudWatch Logs, and publish metrics to Amazon CloudWatch. You grant permissions for all of these tasks to an IAM role. For more information, see Amazon SageMaker Roles .
Note
To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission.
An array of Channel objects. Each channel is a named input source. InputDataConfig describes the input data and its location.
Algorithms can accept input data from one or more channels. For example, an algorithm might have two channels of input data, training_data and validation_data . The configuration for each channel provides the S3, EFS, or FSx location where the input data is stored. It also provides information about the stored data: the MIME type, compression method, and whether the data is wrapped in RecordIO format.
Depending on the input mode that the algorithm supports, Amazon SageMaker either copies input data files from an S3 bucket to a local directory in the Docker container, or makes it available as input streams. For example, if you specify an EFS location, input data files will be made available as input streams. They do not need to be downloaded.
A channel is a named input source that training algorithms can consume.
The name of the channel.
The location of the channel data.
The S3 location of the data source that is associated with a channel.
If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects that match the specified key name prefix for model training.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for model training.
If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
If you want Amazon SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .
If you want Amazon SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.
A list of one or more attribute names to use that are found in a specified augmented manifest file.
The file system that is associated with a channel.
The file system id.
The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.
The file system type.
The full path to the directory to associate with the channel.
The MIME type of the data.
If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, Amazon SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO .
In File mode, leave this field unset or set it to None.
(Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , Amazon SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
To use a model for incremental training, choose File input model.
A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
Determines the shuffling order in ShuffleConfig value.
[REQUIRED]
Specifies the path to the S3 location where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
If you use a KMS key ID or an alias of your master key, the Amazon SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. Amazon SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig . If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
Identifies the S3 path where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
[REQUIRED]
The resources, including the ML compute instances and ML storage volumes, to use for model training.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use ML storage volumes for scratch space. If you want Amazon SageMaker to use the ML storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.
The ML compute instance type.
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.
You must specify sufficient ML storage for your scenario.
Note
Amazon SageMaker supports only the General Purpose SSD (gp2) ML storage volume type.
Note
Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for training, Amazon SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can't request a VolumeSizeInGB greater than the total size of the local instance storage.
For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes .
The Amazon Web Services KMS key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be in any of the following formats:
A VpcConfig object that specifies the VPC that you want your training job to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud .
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
[REQUIRED]
Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.
The maximum length of time, in seconds, that a training or compilation job can run. If the job does not complete during this time, Amazon SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
The default value is 1 day. The maximum value is 28 days.
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, Amazon SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources .
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
To train models using managed spot training, choose True . Managed spot training provides a fully managed and scalable infrastructure for training machine learning models. this option is useful when training jobs can be interrupted and when there is flexibility when the training job is run.
The complete and intermediate results of jobs are stored in an Amazon S3 bucket, and can be used as a starting point to train models incrementally. Amazon SageMaker provides metrics and logs in CloudWatch. They can be used to see when managed spot training jobs are running, interrupted, resumed, or completed.
Contains information about the output location for managed spot training checkpoint data.
Identifies the S3 path where you want Amazon SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix .
(Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/ .
Configuration information for the Debugger hook parameters, metric and tensor collections, and storage paths. To learn more about how to configure the DebugHookConfig parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
Path to local storage location for metrics and tensors. Defaults to /opt/ml/output/tensors/ .
Path to Amazon S3 storage location for metrics and tensors.
Configuration information for the Debugger hook parameters.
Configuration information for Debugger tensor collections. To learn more about how to configure the CollectionConfiguration parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
Configuration information for the Debugger output tensor collections.
The name of the tensor collection. The name must be unique relative to other rule configuration names.
Parameter values for the tensor collection. The allowed parameters are "name" , "include_regex" , "reduction_config" , "save_config" , "tensor_names" , and "save_histogram" .
Configuration information for Debugger rules for debugging output tensors.
Configuration information for SageMaker Debugger rules for debugging. To learn more about how to configure the DebugRuleConfiguration parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
The name of the rule configuration. It must be unique relative to other rule configuration names.
Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/ .
Path to Amazon S3 storage location for rules.
The Amazon Elastic Container (ECR) Image for the managed rule evaluation.
The instance type to deploy a Debugger custom rule for debugging a training job.
The size, in GB, of the ML storage volume attached to the processing instance.
Runtime configuration for rule container.
Configuration of storage locations for the Debugger TensorBoard output data.
Path to local storage location for tensorBoard output. Defaults to /opt/ml/output/tensorboard .
Path to Amazon S3 storage location for TensorBoard output.
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
The name of an existing experiment to associate the trial component with.
The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
The display name for the trial component. If this key isn't specified, the display name is the trial component name.
Configuration information for Debugger system monitoring, framework profiling, and storage paths.
Path to Amazon S3 storage location for system and framework metrics.
A time interval for capturing system metrics in milliseconds. Available values are 100, 200, 500, 1000 (1 second), 5000 (5 seconds), and 60000 (1 minute) milliseconds. The default value is 500 milliseconds.
Configuration information for capturing framework metrics. Available key strings for different profiling options are DetailedProfilingConfig , PythonProfilingConfig , and DataLoaderProfilingConfig . The following codes are configuration structures for the ProfilingParameters parameter. To learn more about how to configure the ProfilingParameters parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
Configuration information for Debugger rules for profiling system and framework metrics.
Configuration information for profiling rules.
The name of the rule configuration. It must be unique relative to other rule configuration names.
Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/ .
Path to Amazon S3 storage location for rules.
The Amazon Elastic Container (ECR) Image for the managed rule evaluation.
The instance type to deploy a Debugger custom rule for profiling a training job.
The size, in GB, of the ML storage volume attached to the processing instance.
Runtime configuration for rule container.
The environment variables to set in the Docker container.
The number of times to retry the job when the job fails due to an InternalServerError .
The number of times to retry the job. When the job is retried, it's SecondaryStatus is changed to STARTING .
dict
Response Syntax
{
'TrainingJobArn': 'string'
}
Response Structure
(dict) --
TrainingJobArn (string) --
The Amazon Resource Name (ARN) of the training job.
Exceptions
Starts a transform job. A transform job uses a trained model to get inferences on a dataset and saves these results to an Amazon S3 location that you specify.
To perform batch transformations, you create a transform job and use the data that you have readily available.
In the request body, you provide the following:
For more information about how batch transformation works, see Batch Transform .
See also: AWS API Documentation
Request Syntax
response = client.create_transform_job(
TransformJobName='string',
ModelName='string',
MaxConcurrentTransforms=123,
ModelClientConfig={
'InvocationsTimeoutInSeconds': 123,
'InvocationsMaxRetries': 123
},
MaxPayloadInMB=123,
BatchStrategy='MultiRecord'|'SingleRecord',
Environment={
'string': 'string'
},
TransformInput={
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'SplitType': 'None'|'Line'|'RecordIO'|'TFRecord'
},
TransformOutput={
'S3OutputPath': 'string',
'Accept': 'string',
'AssembleWith': 'None'|'Line',
'KmsKeyId': 'string'
},
TransformResources={
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'InstanceCount': 123,
'VolumeKmsKeyId': 'string'
},
DataProcessing={
'InputFilter': 'string',
'OutputFilter': 'string',
'JoinSource': 'Input'|'None'
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
ExperimentConfig={
'ExperimentName': 'string',
'TrialName': 'string',
'TrialComponentDisplayName': 'string'
}
)
[REQUIRED]
The name of the transform job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.
[REQUIRED]
The name of the model that you want to use for the transform job. ModelName must be the name of an existing Amazon SageMaker model within an Amazon Web Services Region in an Amazon Web Services account.
Configures the timeout and maximum number of retries for processing a transform job invocation.
The timeout value in seconds for an invocation request.
The maximum number of retries when invocation requests are failing.
The maximum allowed size of the payload, in MB. A payload is the data portion of a record (without metadata). The value in MaxPayloadInMB must be greater than, or equal to, the size of a single record. To estimate the size of a record in MB, divide the size of your dataset by the number of records. To ensure that the records fit within the maximum payload size, we recommend using a slightly larger value. The default value is 6 MB.
For cases where the payload might be arbitrarily large and is transmitted using HTTP chunked encoding, set the value to 0 . This feature works only in supported algorithms. Currently, Amazon SageMaker built-in algorithms do not support HTTP chunked encoding.
Specifies the number of records to include in a mini-batch for an HTTP inference request. A record is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record.
To enable the batch strategy, you must set the SplitType property to Line , RecordIO , or TFRecord .
To use only one record when making an HTTP invocation request to a container, set BatchStrategy to SingleRecord and SplitType to Line .
To fit as many records in a mini-batch as can fit within the MaxPayloadInMB limit, set BatchStrategy to MultiRecord and SplitType to Line .
The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
[REQUIRED]
Describes the input source and the way the transform job consumes it.
Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
The S3 location of the data source that is associated with a channel.
If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.
The following values are compatible: ManifestFile , S3Prefix
The following value is not compatible: AugmentedManifestFile
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None .
The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None , which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:
When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord , Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord , Amazon SageMaker sends individual records in each request.
Note
Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord . Padding is not removed if the value of BatchStrategy is set to MultiRecord .
For more information about RecordIO , see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord , see Consuming TFRecord data in the TensorFlow documentation.
[REQUIRED]
Describes the results of the transform job.
The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, s3://bucket-name/key-name-prefix .
For every S3 object used as input for the transform job, batch transform stores the transformed data with an .``out`` suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at s3://bucket-name/input-name-prefix/dataset01/data.csv , batch transform stores the transformed data at s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out . Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .``out`` file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.
The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify None . To add a newline character at the end of every transformed record, specify Line .
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
[REQUIRED]
Describes the resources, including ML instance types and ML instance count, to use for the transform job.
The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or ml.m5.large instance types.
The number of ML compute instances to use in the transform job. For distributed transform jobs, specify a value greater than 1. The default value is 1 .
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be any of the following formats:
The data structure used to specify the data to be used for inference in a batch transform job and to associate the data that is relevant to the prediction results in the output. The input filter provided allows you to exclude input data that is not needed for inference in a batch transform job. The output filter provided allows you to include input data relevant to interpreting the predictions in the output from the job. For more information, see Associate Prediction Results with their Corresponding Input Records .
A JSONPath expression used to select a portion of the input data to pass to the algorithm. Use the InputFilter parameter to exclude fields, such as an ID column, from the input. If you want Amazon SageMaker to pass the entire input dataset to the algorithm, accept the default value $ .
Examples: "$" , "$[1:]" , "$.features"
A JSONPath expression used to select a portion of the joined dataset to save in the output file for a batch transform job. If you want Amazon SageMaker to store the entire input dataset in the output file, leave the default value, $ . If you specify indexes that aren't within the dimension size of the joined dataset, you get an error.
Examples: "$" , "$[0,5:]" , "$['id','SageMakerOutput']"
Specifies the source of the data to join with the transformed data. The valid values are None and Input . The default value is None , which specifies not to join the input with the transformed data. If you want the batch transform job to join the original input data with the transformed data, set JoinSource to Input . You can specify OutputFilter as an additional filter to select a portion of the joined dataset and store it in the output file.
For JSON or JSONLines objects, such as a JSON array, Amazon SageMaker adds the transformed data to the input JSON object in an attribute called SageMakerOutput . The joined result for JSON must be a key-value pair object. If the input is not a key-value pair object, Amazon SageMaker creates a new JSON file. In the new JSON file, and the input data is stored under the SageMakerInput key and the results are stored in SageMakerOutput .
For CSV data, Amazon SageMaker takes each row as a JSON array and joins the transformed data with the input by appending each transformed row to the end of the input. The joined data has the original input data followed by the transformed data and the output is a CSV file.
For information on how joining in applied, see Workflow for Associating Inferences with Input Records .
(Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide .
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
The name of an existing experiment to associate the trial component with.
The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
The display name for the trial component. If this key isn't specified, the display name is the trial component name.
dict
Response Syntax
{
'TransformJobArn': 'string'
}
Response Structure
(dict) --
TransformJobArn (string) --
The Amazon Resource Name (ARN) of the transform job.
Exceptions
Creates an SageMaker trial . A trial is a set of steps called trial components that produce a machine learning model. A trial is part of a single SageMaker experiment .
When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK.
You can add tags to a trial and then use the Search API to search for the tags.
To get a list of all your trials, call the ListTrials API. To view a trial's properties, call the DescribeTrial API. To create a trial component, call the CreateTrialComponent API.
See also: AWS API Documentation
Request Syntax
response = client.create_trial(
TrialName='string',
DisplayName='string',
ExperimentName='string',
MetadataProperties={
'CommitId': 'string',
'Repository': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string'
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
[REQUIRED]
The name of the trial. The name must be unique in your Amazon Web Services account and is not case-sensitive.
[REQUIRED]
The name of the experiment to associate the trial with.
Metadata properties of the tracking entity, trial, or trial component.
The commit ID.
The repository.
The entity this entity was generated by.
The project ID.
A list of tags to associate with the trial. You can use Search API to search on the tags.
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
dict
Response Syntax
{
'TrialArn': 'string'
}
Response Structure
(dict) --
TrialArn (string) --
The Amazon Resource Name (ARN) of the trial.
Exceptions
Creates a trial component , which is a stage of a machine learning trial . A trial is composed of one or more trial components. A trial component can be used in multiple trials.
Trial components include pre-processing jobs, training jobs, and batch transform jobs.
When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK.
You can add tags to a trial component and then use the Search API to search for the tags.
See also: AWS API Documentation
Request Syntax
response = client.create_trial_component(
TrialComponentName='string',
DisplayName='string',
Status={
'PrimaryStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'Message': 'string'
},
StartTime=datetime(2015, 1, 1),
EndTime=datetime(2015, 1, 1),
Parameters={
'string': {
'StringValue': 'string',
'NumberValue': 123.0
}
},
InputArtifacts={
'string': {
'MediaType': 'string',
'Value': 'string'
}
},
OutputArtifacts={
'string': {
'MediaType': 'string',
'Value': 'string'
}
},
MetadataProperties={
'CommitId': 'string',
'Repository': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string'
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
[REQUIRED]
The name of the component. The name must be unique in your Amazon Web Services account and is not case-sensitive.
The status of the component. States include:
The status of the trial component.
If the component failed, a message describing why.
The hyperparameters for the component.
The value of a hyperparameter. Only one of NumberValue or StringValue can be specified.
This object is specified in the CreateTrialComponent request.
The string value of a categorical hyperparameter. If you specify a value for this parameter, you can't specify the NumberValue parameter.
The numeric value of a numeric hyperparameter. If you specify a value for this parameter, you can't specify the StringValue parameter.
The input artifacts for the component. Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and instance types.
Represents an input or output artifact of a trial component. You specify TrialComponentArtifact as part of the InputArtifacts and OutputArtifacts parameters in the CreateTrialComponent request.
Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and instance types. Examples of output artifacts are metrics, snapshots, logs, and images.
The media type of the artifact, which indicates the type of data in the artifact file. The media type consists of a type and a subtype concatenated with a slash (/) character, for example, text/csv, image/jpeg, and s3/uri. The type specifies the category of the media. The subtype specifies the kind of data.
The location of the artifact.
The output artifacts for the component. Examples of output artifacts are metrics, snapshots, logs, and images.
Represents an input or output artifact of a trial component. You specify TrialComponentArtifact as part of the InputArtifacts and OutputArtifacts parameters in the CreateTrialComponent request.
Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and instance types. Examples of output artifacts are metrics, snapshots, logs, and images.
The media type of the artifact, which indicates the type of data in the artifact file. The media type consists of a type and a subtype concatenated with a slash (/) character, for example, text/csv, image/jpeg, and s3/uri. The type specifies the category of the media. The subtype specifies the kind of data.
The location of the artifact.
Metadata properties of the tracking entity, trial, or trial component.
The commit ID.
The repository.
The entity this entity was generated by.
The project ID.
A list of tags to associate with the component. You can use Search API to search on the tags.
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
dict
Response Syntax
{
'TrialComponentArn': 'string'
}
Response Structure
(dict) --
TrialComponentArn (string) --
The Amazon Resource Name (ARN) of the trial component.
Exceptions
Creates a user profile. A user profile represents a single user within a domain, and is the main way to reference a "person" for the purposes of sharing, reporting, and other user-oriented features. This entity is created when a user onboards to Amazon SageMaker Studio. If an administrator invites a person by email or imports them from SSO, a user profile is automatically created. A user profile is the primary holder of settings for an individual user and has a reference to the user's private Amazon Elastic File System (EFS) home directory.
See also: AWS API Documentation
Request Syntax
response = client.create_user_profile(
DomainId='string',
UserProfileName='string',
SingleSignOnUserIdentifier='string',
SingleSignOnUserValue='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
UserSettings={
'ExecutionRole': 'string',
'SecurityGroups': [
'string',
],
'SharingSettings': {
'NotebookOutputOption': 'Allowed'|'Disabled',
'S3OutputPath': 'string',
'S3KmsKeyId': 'string'
},
'JupyterServerAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'
}
},
'KernelGatewayAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
]
},
'TensorBoardAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'
}
}
}
)
[REQUIRED]
The ID of the associated Domain.
[REQUIRED]
A name for the UserProfile. This value is not case sensitive.
Each tag consists of a key and an optional value. Tag keys must be unique per resource.
Tags that you specify for the User Profile are also added to all Apps that the User Profile launches.
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
A collection of settings.
The execution role for the user.
The security groups for the Amazon Virtual Private Cloud (VPC) that Studio uses for communication.
Optional when the CreateDomain.AppNetworkAccessType parameter is set to PublicInternetOnly .
Required when the CreateDomain.AppNetworkAccessType parameter is set to VpcOnly .
Amazon SageMaker adds a security group to allow NFS traffic from SageMaker Studio. Therefore, the number of security groups that you can specify is one less than the maximum number shown.
Specifies options for sharing SageMaker Studio notebooks.
Whether to include the notebook cell output when sharing the notebook. The default is Disabled .
When NotebookOutputOption is Allowed , the Amazon S3 bucket used to store the shared notebook snapshots.
When NotebookOutputOption is Allowed , the Amazon Web Services Key Management Service (KMS) encryption key ID used to encrypt the notebook cell output in the Amazon S3 bucket.
The Jupyter server's app settings.
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app.
The ARN of the SageMaker image that the image version belongs to.
The ARN of the image version created on the instance.
The instance type that the image version runs on.
The kernel gateway app settings.
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.
The ARN of the SageMaker image that the image version belongs to.
The ARN of the image version created on the instance.
The instance type that the image version runs on.
A list of custom SageMaker images that are configured to run as a KernelGateway app.
A custom SageMaker image. For more information, see Bring your own SageMaker image .
The name of the CustomImage. Must be unique to your account.
The version number of the CustomImage.
The name of the AppImageConfig.
The TensorBoard app settings.
The default instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.
The ARN of the SageMaker image that the image version belongs to.
The ARN of the image version created on the instance.
The instance type that the image version runs on.
dict
Response Syntax
{
'UserProfileArn': 'string'
}
Response Structure
(dict) --
UserProfileArn (string) --
The user profile Amazon Resource Name (ARN).
Exceptions
Use this operation to create a workforce. This operation will return an error if a workforce already exists in the Amazon Web Services Region that you specify. You can only create one workforce in each Amazon Web Services Region per Amazon Web Services account.
If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use the API operation to delete the existing workforce and then use CreateWorkforce to create a new workforce.
To create a private workforce using Amazon Cognito, you must specify a Cognito user pool in CognitoConfig . You can also create an Amazon Cognito workforce using the Amazon SageMaker console. For more information, see Create a Private Workforce (Amazon Cognito) .
To create a private workforce using your own OIDC Identity Provider (IdP), specify your IdP configuration in OidcConfig . Your OIDC IdP must support groups because groups are used by Ground Truth and Amazon A2I to create work teams. For more information, see Create a Private Workforce (OIDC IdP) .
See also: AWS API Documentation
Request Syntax
response = client.create_workforce(
CognitoConfig={
'UserPool': 'string',
'ClientId': 'string'
},
OidcConfig={
'ClientId': 'string',
'ClientSecret': 'string',
'Issuer': 'string',
'AuthorizationEndpoint': 'string',
'TokenEndpoint': 'string',
'UserInfoEndpoint': 'string',
'LogoutEndpoint': 'string',
'JwksUri': 'string'
},
SourceIpConfig={
'Cidrs': [
'string',
]
},
WorkforceName='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
Use this parameter to configure an Amazon Cognito private workforce. A single Cognito workforce is created using and corresponds to a single Amazon Cognito user pool .
Do not use OidcConfig if you specify values for CognitoConfig .
A user pool is a user directory in Amazon Cognito. With a user pool, your users can sign in to your web or mobile app through Amazon Cognito. Your users can also sign in through social identity providers like Google, Facebook, Amazon, or Apple, and through SAML identity providers.
The client ID for your Amazon Cognito user pool.
Use this parameter to configure a private workforce using your own OIDC Identity Provider.
Do not use CognitoConfig if you specify values for OidcConfig .
The OIDC IdP client ID used to configure your private workforce.
The OIDC IdP client secret used to configure your private workforce.
The OIDC IdP issuer used to configure your private workforce.
The OIDC IdP authorization endpoint used to configure your private workforce.
The OIDC IdP token endpoint used to configure your private workforce.
The OIDC IdP user information endpoint used to configure your private workforce.
The OIDC IdP logout endpoint used to configure your private workforce.
The OIDC IdP JSON Web Key Set (Jwks) URI used to configure your private workforce.
A list of IP address ranges (CIDRs ). Used to create an allow list of IP addresses for a private workforce. Workers will only be able to login to their worker portal from an IP address within this range. By default, a workforce isn't restricted to specific IP addresses.
A list of one to ten Classless Inter-Domain Routing (CIDR) values.
Maximum: Ten CIDR values
Note
The following Length Constraints apply to individual CIDR values in the CIDR value list.
[REQUIRED]
The name of the private workforce.
An array of key-value pairs that contain metadata to help you categorize and organize our workforce. Each tag consists of a key and a value, both of which you define.
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
dict
Response Syntax
{
'WorkforceArn': 'string'
}
Response Structure
(dict) --
WorkforceArn (string) --
The Amazon Resource Name (ARN) of the workforce.
Creates a new work team for labeling your data. A work team is defined by one or more Amazon Cognito user pools. You must first create the user pools before you can create a work team.
You cannot create more than 25 work teams in an account and region.
See also: AWS API Documentation
Request Syntax
response = client.create_workteam(
WorkteamName='string',
WorkforceName='string',
MemberDefinitions=[
{
'CognitoMemberDefinition': {
'UserPool': 'string',
'UserGroup': 'string',
'ClientId': 'string'
},
'OidcMemberDefinition': {
'Groups': [
'string',
]
}
},
],
Description='string',
NotificationConfiguration={
'NotificationTopicArn': 'string'
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
[REQUIRED]
The name of the work team. Use this name to identify the work team.
[REQUIRED]
A list of MemberDefinition objects that contains objects that identify the workers that make up the work team.
Workforces can be created using Amazon Cognito or your own OIDC Identity Provider (IdP). For private workforces created using Amazon Cognito use CognitoMemberDefinition . For workforces created using your own OIDC identity provider (IdP) use OidcMemberDefinition . Do not provide input for both of these parameters in a single request.
For workforces created using Amazon Cognito, private work teams correspond to Amazon Cognito user groups within the user pool used to create a workforce. All of the CognitoMemberDefinition objects that make up the member definition must have the same ClientId and UserPool values. To add a Amazon Cognito user group to an existing worker pool, see Adding groups to a User Pool . For more information about user pools, see `Amazon Cognito User Pools .
For workforces created using your own OIDC IdP, specify the user groups that you want to include in your private work team in OidcMemberDefinition by listing those groups in Groups .
Defines an Amazon Cognito or your own OIDC IdP user group that is part of a work team.
The Amazon Cognito user group that is part of the work team.
An identifier for a user pool. The user pool must be in the same region as the service that you are calling.
An identifier for a user group.
An identifier for an application client. You must create the app client ID using Amazon Cognito.
A list user groups that exist in your OIDC Identity Provider (IdP). One to ten groups can be used to create a single private work team. When you add a user group to the list of Groups , you can add that user group to one or more private work teams. If you add a user group to a private work team, all workers in that user group are added to the work team.
A list of comma seperated strings that identifies user groups in your OIDC IdP. Each user group is made up of a group of private workers.
[REQUIRED]
A description of the work team.
Configures notification of workers regarding available or expiring work items.
The ARN for the SNS topic to which notifications should be published.
An array of key-value pairs.
For more information, see Resource Tag and Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide .
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
dict
Response Syntax
{
'WorkteamArn': 'string'
}
Response Structure
(dict) --
WorkteamArn (string) --
The Amazon Resource Name (ARN) of the work team. You can use this ARN to identify the work team.
Exceptions
Deletes an action.
See also: AWS API Documentation
Request Syntax
response = client.delete_action(
ActionName='string'
)
[REQUIRED]
The name of the action to delete.
{
'ActionArn': 'string'
}
Response Structure
The Amazon Resource Name (ARN) of the action.
Exceptions
Removes the specified algorithm from your account.
See also: AWS API Documentation
Request Syntax
response = client.delete_algorithm(
AlgorithmName='string'
)
[REQUIRED]
The name of the algorithm to delete.
Used to stop and delete an app.
See also: AWS API Documentation
Request Syntax
response = client.delete_app(
DomainId='string',
UserProfileName='string',
AppType='JupyterServer'|'KernelGateway'|'TensorBoard',
AppName='string'
)
[REQUIRED]
The domain ID.
[REQUIRED]
The user profile name.
[REQUIRED]
The type of app.
[REQUIRED]
The name of the app.
None
Exceptions
Deletes an AppImageConfig.
See also: AWS API Documentation
Request Syntax
response = client.delete_app_image_config(
AppImageConfigName='string'
)
[REQUIRED]
The name of the AppImageConfig to delete.
Exceptions
Deletes an artifact. Either ArtifactArn or Source must be specified.
See also: AWS API Documentation
Request Syntax
response = client.delete_artifact(
ArtifactArn='string',
Source={
'SourceUri': 'string',
'SourceTypes': [
{
'SourceIdType': 'MD5Hash'|'S3ETag'|'S3Version'|'Custom',
'Value': 'string'
},
]
}
)
The URI of the source.
The URI of the source.
A list of source types.
The ID and ID type of an artifact source.
The type of ID.
The ID.
dict
Response Syntax
{
'ArtifactArn': 'string'
}
Response Structure
(dict) --
ArtifactArn (string) --
The Amazon Resource Name (ARN) of the artifact.
Exceptions
Deletes an association.
See also: AWS API Documentation
Request Syntax
response = client.delete_association(
SourceArn='string',
DestinationArn='string'
)
[REQUIRED]
The ARN of the source.
[REQUIRED]
The Amazon Resource Name (ARN) of the destination.
dict
Response Syntax
{
'SourceArn': 'string',
'DestinationArn': 'string'
}
Response Structure
(dict) --
SourceArn (string) --
The ARN of the source.
DestinationArn (string) --
The Amazon Resource Name (ARN) of the destination.
Exceptions
Deletes the specified Git repository from your account.
See also: AWS API Documentation
Request Syntax
response = client.delete_code_repository(
CodeRepositoryName='string'
)
[REQUIRED]
The name of the Git repository to delete.
Deletes an context.
See also: AWS API Documentation
Request Syntax
response = client.delete_context(
ContextName='string'
)
[REQUIRED]
The name of the context to delete.
{
'ContextArn': 'string'
}
Response Structure
The Amazon Resource Name (ARN) of the context.
Exceptions
Deletes a data quality monitoring job definition.
See also: AWS API Documentation
Request Syntax
response = client.delete_data_quality_job_definition(
JobDefinitionName='string'
)
[REQUIRED]
The name of the data quality monitoring job definition to delete.
Exceptions
Deletes a fleet.
See also: AWS API Documentation
Request Syntax
response = client.delete_device_fleet(
DeviceFleetName='string'
)
[REQUIRED]
The name of the fleet to delete.
Exceptions
Used to delete a domain. If you onboarded with IAM mode, you will need to delete your domain to onboard again using SSO. Use with caution. All of the members of the domain will lose access to their EFS volume, including data, notebooks, and other artifacts.
See also: AWS API Documentation
Request Syntax
response = client.delete_domain(
DomainId='string',
RetentionPolicy={
'HomeEfsFileSystem': 'Retain'|'Delete'
}
)
[REQUIRED]
The domain ID.
The retention policy for this domain, which specifies whether resources will be retained after the Domain is deleted. By default, all resources are retained (not automatically deleted).
The default is Retain , which specifies to keep the data stored on the EFS volume.
Specify Delete to delete the data stored on the EFS volume.
None
Exceptions
Deletes an endpoint. Amazon SageMaker frees up all of the resources that were deployed when the endpoint was created.
Amazon SageMaker retires any custom KMS key grants associated with the endpoint, meaning you don't need to use the RevokeGrant API call.
See also: AWS API Documentation
Request Syntax
response = client.delete_endpoint(
EndpointName='string'
)
[REQUIRED]
The name of the endpoint that you want to delete.
Deletes an endpoint configuration. The DeleteEndpointConfig API deletes only the specified configuration. It does not delete endpoints created using the configuration.
You must not delete an EndpointConfig in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. If you delete the EndpointConfig of an endpoint that is active or being created or updated you may lose visibility into the instance type the endpoint is using. The endpoint must be deleted in order to stop incurring charges.
See also: AWS API Documentation
Request Syntax
response = client.delete_endpoint_config(
EndpointConfigName='string'
)
[REQUIRED]
The name of the endpoint configuration that you want to delete.
Deletes an SageMaker experiment. All trials associated with the experiment must be deleted first. Use the ListTrials API to get a list of the trials associated with the experiment.
See also: AWS API Documentation
Request Syntax
response = client.delete_experiment(
ExperimentName='string'
)
[REQUIRED]
The name of the experiment to delete.
{
'ExperimentArn': 'string'
}
Response Structure
The Amazon Resource Name (ARN) of the experiment that is being deleted.
Exceptions
Delete the FeatureGroup and any data that was written to the OnlineStore of the FeatureGroup . Data cannot be accessed from the OnlineStore immediately after DeleteFeatureGroup is called.
Data written into the OfflineStore will not be deleted. The Amazon Web Services Glue database and tables that are automatically created for your OfflineStore are not deleted.
See also: AWS API Documentation
Request Syntax
response = client.delete_feature_group(
FeatureGroupName='string'
)
[REQUIRED]
The name of the FeatureGroup you want to delete. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.
Exceptions
Deletes the specified flow definition.
See also: AWS API Documentation
Request Syntax
response = client.delete_flow_definition(
FlowDefinitionName='string'
)
[REQUIRED]
The name of the flow definition you are deleting.
{}
Response Structure
Exceptions
Use this operation to delete a human task user interface (worker task template).
To see a list of human task user interfaces (work task templates) in your account, use . When you delete a worker task template, it no longer appears when you call ListHumanTaskUis .
See also: AWS API Documentation
Request Syntax
response = client.delete_human_task_ui(
HumanTaskUiName='string'
)
[REQUIRED]
The name of the human task user interface (work task template) you want to delete.
{}
Response Structure
Exceptions
Deletes a SageMaker image and all versions of the image. The container images aren't deleted.
See also: AWS API Documentation
Request Syntax
response = client.delete_image(
ImageName='string'
)
[REQUIRED]
The name of the image to delete.
{}
Response Structure
Exceptions
Deletes a version of a SageMaker image. The container image the version represents isn't deleted.
See also: AWS API Documentation
Request Syntax
response = client.delete_image_version(
ImageName='string',
Version=123
)
[REQUIRED]
The name of the image.
[REQUIRED]
The version to delete.
dict
Response Syntax
{}
Response Structure
Exceptions
Deletes a model. The DeleteModel API deletes only the model entry that was created in Amazon SageMaker when you called the CreateModel API. It does not delete model artifacts, inference code, or the IAM role that you specified when creating the model.
See also: AWS API Documentation
Request Syntax
response = client.delete_model(
ModelName='string'
)
[REQUIRED]
The name of the model to delete.
Deletes an Amazon SageMaker model bias job definition.
See also: AWS API Documentation
Request Syntax
response = client.delete_model_bias_job_definition(
JobDefinitionName='string'
)
[REQUIRED]
The name of the model bias job definition to delete.
Exceptions
Deletes an Amazon SageMaker model explainability job definition.
See also: AWS API Documentation
Request Syntax
response = client.delete_model_explainability_job_definition(
JobDefinitionName='string'
)
[REQUIRED]
The name of the model explainability job definition to delete.
Exceptions
Deletes a model package.
A model package is used to create Amazon SageMaker models or list on Amazon Web Services Marketplace. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in Amazon SageMaker.
See also: AWS API Documentation
Request Syntax
response = client.delete_model_package(
ModelPackageName='string'
)
[REQUIRED]
The name or Amazon Resource Name (ARN) of the model package to delete.
When you specify a name, the name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
Exceptions
Deletes the specified model group.
See also: AWS API Documentation
Request Syntax
response = client.delete_model_package_group(
ModelPackageGroupName='string'
)
[REQUIRED]
The name of the model group to delete.
Exceptions
Deletes a model group resource policy.
See also: AWS API Documentation
Request Syntax
response = client.delete_model_package_group_policy(
ModelPackageGroupName='string'
)
[REQUIRED]
The name of the model group for which to delete the policy.
Deletes the secified model quality monitoring job definition.
See also: AWS API Documentation
Request Syntax
response = client.delete_model_quality_job_definition(
JobDefinitionName='string'
)
[REQUIRED]
The name of the model quality monitoring job definition to delete.
Exceptions
Deletes a monitoring schedule. Also stops the schedule had not already been stopped. This does not delete the job execution history of the monitoring schedule.
See also: AWS API Documentation
Request Syntax
response = client.delete_monitoring_schedule(
MonitoringScheduleName='string'
)
[REQUIRED]
The name of the monitoring schedule to delete.
Exceptions
Deletes an Amazon SageMaker notebook instance. Before you can delete a notebook instance, you must call the StopNotebookInstance API.
Warning
When you delete a notebook instance, you lose all of your data. Amazon SageMaker removes the ML compute instance, and deletes the ML storage volume and the network interface associated with the notebook instance.
See also: AWS API Documentation
Request Syntax
response = client.delete_notebook_instance(
NotebookInstanceName='string'
)
[REQUIRED]
The name of the Amazon SageMaker notebook instance to delete.
Deletes a notebook instance lifecycle configuration.
See also: AWS API Documentation
Request Syntax
response = client.delete_notebook_instance_lifecycle_config(
NotebookInstanceLifecycleConfigName='string'
)
[REQUIRED]
The name of the lifecycle configuration to delete.
Deletes a pipeline if there are no running instances of the pipeline. To delete a pipeline, you must stop all running instances of the pipeline using the StopPipelineExecution API. When you delete a pipeline, all instances of the pipeline are deleted.
See also: AWS API Documentation
Request Syntax
response = client.delete_pipeline(
PipelineName='string',
ClientRequestToken='string'
)
[REQUIRED]
The name of the pipeline to delete.
[REQUIRED]
A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.
This field is autopopulated if not provided.
dict
Response Syntax
{
'PipelineArn': 'string'
}
Response Structure
(dict) --
PipelineArn (string) --
The Amazon Resource Name (ARN) of the pipeline to delete.
Exceptions
Delete the specified project.
See also: AWS API Documentation
Request Syntax
response = client.delete_project(
ProjectName='string'
)
[REQUIRED]
The name of the project to delete.
Exceptions
Deletes the specified tags from an Amazon SageMaker resource.
To list a resource's tags, use the ListTags API.
Note
When you call this API to delete tags from a hyperparameter tuning job, the deleted tags are not removed from training jobs that the hyperparameter tuning job launched before you called this API.
Note
When you call this API to delete tags from a SageMaker Studio Domain or User Profile, the deleted tags are not removed from Apps that the SageMaker Studio Domain or User Profile launched before you called this API.
See also: AWS API Documentation
Request Syntax
response = client.delete_tags(
ResourceArn='string',
TagKeys=[
'string',
]
)
[REQUIRED]
The Amazon Resource Name (ARN) of the resource whose tags you want to delete.
[REQUIRED]
An array or one or more tag keys to delete.
dict
Response Syntax
{}
Response Structure
Deletes the specified trial. All trial components that make up the trial must be deleted first. Use the DescribeTrialComponent API to get the list of trial components.
See also: AWS API Documentation
Request Syntax
response = client.delete_trial(
TrialName='string'
)
[REQUIRED]
The name of the trial to delete.
{
'TrialArn': 'string'
}
Response Structure
The Amazon Resource Name (ARN) of the trial that is being deleted.
Exceptions
Deletes the specified trial component. A trial component must be disassociated from all trials before the trial component can be deleted. To disassociate a trial component from a trial, call the DisassociateTrialComponent API.
See also: AWS API Documentation
Request Syntax
response = client.delete_trial_component(
TrialComponentName='string'
)
[REQUIRED]
The name of the component to delete.
{
'TrialComponentArn': 'string'
}
Response Structure
The Amazon Resource Name (ARN) of the component is being deleted.
Exceptions
Deletes a user profile. When a user profile is deleted, the user loses access to their EFS volume, including data, notebooks, and other artifacts.
See also: AWS API Documentation
Request Syntax
response = client.delete_user_profile(
DomainId='string',
UserProfileName='string'
)
[REQUIRED]
The domain ID.
[REQUIRED]
The user profile name.
None
Exceptions
Use this operation to delete a workforce.
If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use this operation to delete the existing workforce and then use to create a new workforce.
Warning
If a private workforce contains one or more work teams, you must use the operation to delete all work teams before you delete the workforce. If you try to delete a workforce that contains one or more work teams, you will recieve a ResourceInUse error.
See also: AWS API Documentation
Request Syntax
response = client.delete_workforce(
WorkforceName='string'
)
[REQUIRED]
The name of the workforce.
{}
Response Structure
Deletes an existing work team. This operation can't be undone.
See also: AWS API Documentation
Request Syntax
response = client.delete_workteam(
WorkteamName='string'
)
[REQUIRED]
The name of the work team to delete.
{
'Success': True|False
}
Response Structure
Returns true if the work team was successfully deleted; otherwise, returns false .
Exceptions
Deregisters the specified devices. After you deregister a device, you will need to re-register the devices.
See also: AWS API Documentation
Request Syntax
response = client.deregister_devices(
DeviceFleetName='string',
DeviceNames=[
'string',
]
)
[REQUIRED]
The name of the fleet the devices belong to.
[REQUIRED]
The unique IDs of the devices.
None
Describes an action.
See also: AWS API Documentation
Request Syntax
response = client.describe_action(
ActionName='string'
)
[REQUIRED]
The name of the action to describe.
{
'ActionName': 'string',
'ActionArn': 'string',
'Source': {
'SourceUri': 'string',
'SourceType': 'string',
'SourceId': 'string'
},
'ActionType': 'string',
'Description': 'string',
'Status': 'Unknown'|'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'Properties': {
'string': 'string'
},
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'LastModifiedTime': datetime(2015, 1, 1),
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'MetadataProperties': {
'CommitId': 'string',
'Repository': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string'
}
}
Response Structure
The name of the action.
The Amazon Resource Name (ARN) of the action.
The source of the action.
The URI of the source.
The type of the source.
The ID of the source.
The type of the action.
The description of the action.
The status of the action.
A list of the action's properties.
When the action was created.
Information about the user who created or modified an experiment, trial, or trial component.
The Amazon Resource Name (ARN) of the user's profile.
The name of the user's profile.
The domain associated with the user.
When the action was last modified.
Information about the user who created or modified an experiment, trial, or trial component.
The Amazon Resource Name (ARN) of the user's profile.
The name of the user's profile.
The domain associated with the user.
Metadata properties of the tracking entity, trial, or trial component.
The commit ID.
The repository.
The entity this entity was generated by.
The project ID.
Exceptions
Returns a description of the specified algorithm that is in your account.
See also: AWS API Documentation
Request Syntax
response = client.describe_algorithm(
AlgorithmName='string'
)
[REQUIRED]
The name of the algorithm to describe.
{
'AlgorithmName': 'string',
'AlgorithmArn': 'string',
'AlgorithmDescription': 'string',
'CreationTime': datetime(2015, 1, 1),
'TrainingSpecification': {
'TrainingImage': 'string',
'TrainingImageDigest': 'string',
'SupportedHyperParameters': [
{
'Name': 'string',
'Description': 'string',
'Type': 'Integer'|'Continuous'|'Categorical'|'FreeText',
'Range': {
'IntegerParameterRangeSpecification': {
'MinValue': 'string',
'MaxValue': 'string'
},
'ContinuousParameterRangeSpecification': {
'MinValue': 'string',
'MaxValue': 'string'
},
'CategoricalParameterRangeSpecification': {
'Values': [
'string',
]
}
},
'IsTunable': True|False,
'IsRequired': True|False,
'DefaultValue': 'string'
},
],
'SupportedTrainingInstanceTypes': [
'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge',
],
'SupportsDistributedTraining': True|False,
'MetricDefinitions': [
{
'Name': 'string',
'Regex': 'string'
},
],
'TrainingChannels': [
{
'Name': 'string',
'Description': 'string',
'IsRequired': True|False,
'SupportedContentTypes': [
'string',
],
'SupportedCompressionTypes': [
'None'|'Gzip',
],
'SupportedInputModes': [
'Pipe'|'File',
]
},
],
'SupportedTuningJobObjectiveMetrics': [
{
'Type': 'Maximize'|'Minimize',
'MetricName': 'string'
},
]
},
'InferenceSpecification': {
'Containers': [
{
'ContainerHostname': 'string',
'Image': 'string',
'ImageDigest': 'string',
'ModelDataUrl': 'string',
'ProductId': 'string',
'Environment': {
'string': 'string'
}
},
],
'SupportedTransformInstanceTypes': [
'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
],
'SupportedRealtimeInferenceInstanceTypes': [
'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge',
],
'SupportedContentTypes': [
'string',
],
'SupportedResponseMIMETypes': [
'string',
]
},
'ValidationSpecification': {
'ValidationRole': 'string',
'ValidationProfiles': [
{
'ProfileName': 'string',
'TrainingJobDefinition': {
'TrainingInputMode': 'Pipe'|'File',
'HyperParameters': {
'string': 'string'
},
'InputDataConfig': [
{
'ChannelName': 'string',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'AttributeNames': [
'string',
]
},
'FileSystemDataSource': {
'FileSystemId': 'string',
'FileSystemAccessMode': 'rw'|'ro',
'FileSystemType': 'EFS'|'FSxLustre',
'DirectoryPath': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File',
'ShuffleConfig': {
'Seed': 123
}
},
],
'OutputDataConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string'
},
'ResourceConfig': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123
}
},
'TransformJobDefinition': {
'MaxConcurrentTransforms': 123,
'MaxPayloadInMB': 123,
'BatchStrategy': 'MultiRecord'|'SingleRecord',
'Environment': {
'string': 'string'
},
'TransformInput': {
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'SplitType': 'None'|'Line'|'RecordIO'|'TFRecord'
},
'TransformOutput': {
'S3OutputPath': 'string',
'Accept': 'string',
'AssembleWith': 'None'|'Line',
'KmsKeyId': 'string'
},
'TransformResources': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'InstanceCount': 123,
'VolumeKmsKeyId': 'string'
}
}
},
]
},
'AlgorithmStatus': 'Pending'|'InProgress'|'Completed'|'Failed'|'Deleting',
'AlgorithmStatusDetails': {
'ValidationStatuses': [
{
'Name': 'string',
'Status': 'NotStarted'|'InProgress'|'Completed'|'Failed',
'FailureReason': 'string'
},
],
'ImageScanStatuses': [
{
'Name': 'string',
'Status': 'NotStarted'|'InProgress'|'Completed'|'Failed',
'FailureReason': 'string'
},
]
},
'ProductId': 'string',
'CertifyForMarketplace': True|False
}
Response Structure
The name of the algorithm being described.
The Amazon Resource Name (ARN) of the algorithm.
A brief summary about the algorithm.
A timestamp specifying when the algorithm was created.
Details about training jobs run by this algorithm.
The Amazon ECR registry path of the Docker image that contains the training algorithm.
An MD5 hash of the training algorithm that identifies the Docker image used for training.
A list of the HyperParameterSpecification objects, that define the supported hyperparameters. This is required if the algorithm supports automatic model tuning.>
Defines a hyperparameter to be used by an algorithm.
The name of this hyperparameter. The name must be unique.
A brief description of the hyperparameter.
The type of this hyperparameter. The valid types are Integer , Continuous , Categorical , and FreeText .
The allowed range for this hyperparameter.
A IntegerParameterRangeSpecification object that defines the possible values for an integer hyperparameter.
The minimum integer value allowed.
The maximum integer value allowed.
A ContinuousParameterRangeSpecification object that defines the possible values for a continuous hyperparameter.
The minimum floating-point value allowed.
The maximum floating-point value allowed.
A CategoricalParameterRangeSpecification object that defines the possible values for a categorical hyperparameter.
The allowed categories for the hyperparameter.
Indicates whether this hyperparameter is tunable in a hyperparameter tuning job.
Indicates whether this hyperparameter is required.
The default value for this hyperparameter. If a default value is specified, a hyperparameter cannot be required.
A list of the instance types that this algorithm can use for training.
Indicates whether the algorithm supports distributed training. If set to false, buyers can't request more than one instance during training.
A list of MetricDefinition objects, which are used for parsing metrics generated by the algorithm.
Specifies a metric that the training algorithm writes to stderr or stdout . Amazon SageMakerhyperparameter tuning captures all defined metrics. You specify one metric that a hyperparameter tuning job uses as its objective metric to choose the best training job.
The name of the metric.
A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining Objective Metrics .
A list of ChannelSpecification objects, which specify the input sources to be used by the algorithm.
Defines a named input source, called a channel, to be used by an algorithm.
The name of the channel.
A brief description of the channel.
Indicates whether the channel is required by the algorithm.
The supported MIME types for the data.
The allowed compression types, if data compression is used.
The allowed input mode, either FILE or PIPE.
In FILE mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode.
In PIPE mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume.
A list of the metrics that the algorithm emits that can be used as the objective metric in a hyperparameter tuning job.
Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the Type parameter.
Whether to minimize or maximize the objective metric.
The name of the metric to use for the objective metric.
Details about inference jobs that the algorithm runs.
The Amazon ECR registry path of the Docker image that contains the inference code.
Describes the Docker container for the model package.
The DNS host name for the Docker container.
The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by Amazon SageMaker, the inference code must meet Amazon SageMaker requirements. Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
An MD5 hash of the training algorithm that identifies the Docker image used for training.
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
Note
The model artifacts must be in an S3 bucket that is in the same region as the model package.
The Amazon Web Services Marketplace product ID of the model package.
The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
This parameter is required for unversioned models, and optional for versioned models.
A list of the instance types that are used to generate inferences in real-time.
This parameter is required for unversioned models, and optional for versioned models.
The supported MIME types for the input data.
The supported MIME types for the output data.
Details about configurations for one or more training jobs that Amazon SageMaker runs to test the algorithm.
The IAM roles that Amazon SageMaker uses to run the training jobs.
An array of AlgorithmValidationProfile objects, each of which specifies a training job and batch transform job that Amazon SageMaker runs to validate your algorithm.
Defines a training job and a batch transform job that Amazon SageMaker runs to validate your algorithm.
The data provided in the validation profile is made available to your buyers on Amazon Web Services Marketplace.
The name of the profile for the algorithm. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
The TrainingJobDefinition object that describes the training job that Amazon SageMaker runs to validate your algorithm.
The input mode used by the algorithm for the training job. For the input modes that Amazon SageMaker algorithms support, see Algorithms .
If an algorithm supports the File input mode, Amazon SageMaker downloads the training data from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an algorithm supports the Pipe input mode, Amazon SageMaker streams data directly from S3 to the container.
The hyperparameters used for the training job.
An array of Channel objects, each of which specifies an input source.
A channel is a named input source that training algorithms can consume.
The name of the channel.
The location of the channel data.
The S3 location of the data source that is associated with a channel.
If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects that match the specified key name prefix for model training.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for model training.
If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
If you want Amazon SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .
If you want Amazon SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.
A list of one or more attribute names to use that are found in a specified augmented manifest file.
The file system that is associated with a channel.
The file system id.
The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.
The file system type.
The full path to the directory to associate with the channel.
The MIME type of the data.
If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, Amazon SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO .
In File mode, leave this field unset or set it to None.
(Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , Amazon SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
To use a model for incremental training, choose File input model.
A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
Determines the shuffling order in ShuffleConfig value.
the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
If you use a KMS key ID or an alias of your master key, the Amazon SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. Amazon SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig . If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
Identifies the S3 path where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
The resources, including the ML compute instances and ML storage volumes, to use for model training.
The ML compute instance type.
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.
You must specify sufficient ML storage for your scenario.
Note
Amazon SageMaker supports only the General Purpose SSD (gp2) ML storage volume type.
Note
Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for training, Amazon SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can't request a VolumeSizeInGB greater than the total size of the local instance storage.
For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes .
The Amazon Web Services KMS key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be in any of the following formats:
Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts.
The maximum length of time, in seconds, that a training or compilation job can run. If the job does not complete during this time, Amazon SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
The default value is 1 day. The maximum value is 28 days.
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, Amazon SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
The TransformJobDefinition object that describes the transform job that Amazon SageMaker runs to validate your algorithm.
The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1.
The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata).
A string that determines the number of records included in a single mini-batch.
SingleRecord means only one record is used per mini-batch. MultiRecord means a mini-batch is set to contain as many records that can fit within the MaxPayloadInMB limit.
The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
A description of the input source and the way the transform job consumes it.
Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
The S3 location of the data source that is associated with a channel.
If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.
The following values are compatible: ManifestFile , S3Prefix
The following value is not compatible: AugmentedManifestFile
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None .
The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None , which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:
When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord , Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord , Amazon SageMaker sends individual records in each request.
Note
Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord . Padding is not removed if the value of BatchStrategy is set to MultiRecord .
For more information about RecordIO , see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord , see Consuming TFRecord data in the TensorFlow documentation.
Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, s3://bucket-name/key-name-prefix .
For every S3 object used as input for the transform job, batch transform stores the transformed data with an .``out`` suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at s3://bucket-name/input-name-prefix/dataset01/data.csv , batch transform stores the transformed data at s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out . Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .``out`` file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.
The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify None . To add a newline character at the end of every transformed record, specify Line .
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
Identifies the ML compute instances for the transform job.
The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or ml.m5.large instance types.
The number of ML compute instances to use in the transform job. For distributed transform jobs, specify a value greater than 1. The default value is 1 .
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be any of the following formats:
The current status of the algorithm.
Details about the current status of the algorithm.
The status of algorithm validation.
Represents the overall status of an algorithm.
The name of the algorithm for which the overall status is being reported.
The current status.
if the overall status is Failed , the reason for the failure.
The status of the scan of the algorithm's Docker image container.
Represents the overall status of an algorithm.
The name of the algorithm for which the overall status is being reported.
The current status.
if the overall status is Failed , the reason for the failure.
The product identifier of the algorithm.
Whether the algorithm is certified to be listed in Amazon Web Services Marketplace.
Describes the app.
See also: AWS API Documentation
Request Syntax
response = client.describe_app(
DomainId='string',
UserProfileName='string',
AppType='JupyterServer'|'KernelGateway'|'TensorBoard',
AppName='string'
)
[REQUIRED]
The domain ID.
[REQUIRED]
The user profile name.
[REQUIRED]
The type of app.
[REQUIRED]
The name of the app.
dict
Response Syntax
{
'AppArn': 'string',
'AppType': 'JupyterServer'|'KernelGateway'|'TensorBoard',
'AppName': 'string',
'DomainId': 'string',
'UserProfileName': 'string',
'Status': 'Deleted'|'Deleting'|'Failed'|'InService'|'Pending',
'LastHealthCheckTimestamp': datetime(2015, 1, 1),
'LastUserActivityTimestamp': datetime(2015, 1, 1),
'CreationTime': datetime(2015, 1, 1),
'FailureReason': 'string',
'ResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'
}
}
Response Structure
(dict) --
AppArn (string) --
The Amazon Resource Name (ARN) of the app.
AppType (string) --
The type of app.
AppName (string) --
The name of the app.
DomainId (string) --
The domain ID.
UserProfileName (string) --
The user profile name.
Status (string) --
The status.
LastHealthCheckTimestamp (datetime) --
The timestamp of the last health check.
LastUserActivityTimestamp (datetime) --
The timestamp of the last user's activity.
CreationTime (datetime) --
The creation time.
FailureReason (string) --
The failure reason.
ResourceSpec (dict) --
The instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
InstanceType (string) --
The instance type that the image version runs on.
Exceptions
Describes an AppImageConfig.
See also: AWS API Documentation
Request Syntax
response = client.describe_app_image_config(
AppImageConfigName='string'
)
[REQUIRED]
The name of the AppImageConfig to describe.
{
'AppImageConfigArn': 'string',
'AppImageConfigName': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'KernelGatewayImageConfig': {
'KernelSpecs': [
{
'Name': 'string',
'DisplayName': 'string'
},
],
'FileSystemConfig': {
'MountPath': 'string',
'DefaultUid': 123,
'DefaultGid': 123
}
}
}
Response Structure
The Amazon Resource Name (ARN) of the AppImageConfig.
The name of the AppImageConfig.
When the AppImageConfig was created.
When the AppImageConfig was last modified.
The configuration of a KernelGateway app.
The specification of the Jupyter kernels in the image.
The specification of a Jupyter kernel.
The name of the Jupyter kernel in the image. This value is case sensitive.
The display name of the kernel.
The Amazon Elastic File System (EFS) storage configuration for a SageMaker image.
The path within the image to mount the user's EFS home directory. The directory should be empty. If not specified, defaults to /home/sagemaker-user .
The default POSIX user ID (UID). If not specified, defaults to 1000 .
The default POSIX group ID (GID). If not specified, defaults to 100 .
Exceptions
Describes an artifact.
See also: AWS API Documentation
Request Syntax
response = client.describe_artifact(
ArtifactArn='string'
)
[REQUIRED]
The Amazon Resource Name (ARN) of the artifact to describe.
{
'ArtifactName': 'string',
'ArtifactArn': 'string',
'Source': {
'SourceUri': 'string',
'SourceTypes': [
{
'SourceIdType': 'MD5Hash'|'S3ETag'|'S3Version'|'Custom',
'Value': 'string'
},
]
},
'ArtifactType': 'string',
'Properties': {
'string': 'string'
},
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'LastModifiedTime': datetime(2015, 1, 1),
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'MetadataProperties': {
'CommitId': 'string',
'Repository': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string'
}
}
Response Structure
The name of the artifact.
The Amazon Resource Name (ARN) of the artifact.
The source of the artifact.
The URI of the source.
A list of source types.
The ID and ID type of an artifact source.
The type of ID.
The ID.
The type of the artifact.
A list of the artifact's properties.
When the artifact was created.
Information about the user who created or modified an experiment, trial, or trial component.
The Amazon Resource Name (ARN) of the user's profile.
The name of the user's profile.
The domain associated with the user.
When the artifact was last modified.
Information about the user who created or modified an experiment, trial, or trial component.
The Amazon Resource Name (ARN) of the user's profile.
The name of the user's profile.
The domain associated with the user.
Metadata properties of the tracking entity, trial, or trial component.
The commit ID.
The repository.
The entity this entity was generated by.
The project ID.
Exceptions
Returns information about an Amazon SageMaker AutoML job.
See also: AWS API Documentation
Request Syntax
response = client.describe_auto_ml_job(
AutoMLJobName='string'
)
[REQUIRED]
Requests information about an AutoML job using its unique name.
{
'AutoMLJobName': 'string',
'AutoMLJobArn': 'string',
'InputDataConfig': [
{
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix',
'S3Uri': 'string'
}
},
'CompressionType': 'None'|'Gzip',
'TargetAttributeName': 'string'
},
],
'OutputDataConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string'
},
'RoleArn': 'string',
'AutoMLJobObjective': {
'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'
},
'ProblemType': 'BinaryClassification'|'MulticlassClassification'|'Regression',
'AutoMLJobConfig': {
'CompletionCriteria': {
'MaxCandidates': 123,
'MaxRuntimePerTrainingJobInSeconds': 123,
'MaxAutoMLJobRuntimeInSeconds': 123
},
'SecurityConfig': {
'VolumeKmsKeyId': 'string',
'EnableInterContainerTrafficEncryption': True|False,
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
}
},
'CreationTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'FailureReason': 'string',
'PartialFailureReasons': [
{
'PartialFailureMessage': 'string'
},
],
'BestCandidate': {
'CandidateName': 'string',
'FinalAutoMLJobObjectiveMetric': {
'Type': 'Maximize'|'Minimize',
'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC',
'Value': ...
},
'ObjectiveStatus': 'Succeeded'|'Pending'|'Failed',
'CandidateSteps': [
{
'CandidateStepType': 'AWS::SageMaker::TrainingJob'|'AWS::SageMaker::TransformJob'|'AWS::SageMaker::ProcessingJob',
'CandidateStepArn': 'string',
'CandidateStepName': 'string'
},
],
'CandidateStatus': 'Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping',
'InferenceContainers': [
{
'Image': 'string',
'ModelDataUrl': 'string',
'Environment': {
'string': 'string'
}
},
],
'CreationTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'FailureReason': 'string',
'CandidateProperties': {
'CandidateArtifactLocations': {
'Explainability': 'string'
}
}
},
'AutoMLJobStatus': 'Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping',
'AutoMLJobSecondaryStatus': 'Starting'|'AnalyzingData'|'FeatureEngineering'|'ModelTuning'|'MaxCandidatesReached'|'Failed'|'Stopped'|'MaxAutoMLJobRuntimeReached'|'Stopping'|'CandidateDefinitionsGenerated'|'GeneratingExplainabilityReport'|'Completed'|'ExplainabilityError'|'DeployingModel'|'ModelDeploymentError',
'GenerateCandidateDefinitionsOnly': True|False,
'AutoMLJobArtifacts': {
'CandidateDefinitionNotebookLocation': 'string',
'DataExplorationNotebookLocation': 'string'
},
'ResolvedAttributes': {
'AutoMLJobObjective': {
'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'
},
'ProblemType': 'BinaryClassification'|'MulticlassClassification'|'Regression',
'CompletionCriteria': {
'MaxCandidates': 123,
'MaxRuntimePerTrainingJobInSeconds': 123,
'MaxAutoMLJobRuntimeInSeconds': 123
}
},
'ModelDeployConfig': {
'AutoGenerateEndpointName': True|False,
'EndpointName': 'string'
},
'ModelDeployResult': {
'EndpointName': 'string'
}
}
Response Structure
Returns the name of the AutoML job.
Returns the ARN of the AutoML job.
Returns the input data configuration for the AutoML job..
A channel is a named input source that training algorithms can consume. For more information, see .
The data source for an AutoML channel.
The Amazon S3 location of the input data.
Note
The input data must be in CSV format and contain at least 500 rows.
The data type.
The URL to the Amazon S3 data source.
You can use Gzip or None . The default value is None .
The name of the target variable in supervised learning, usually represented by 'y'.
Returns the job's output data config.
The Amazon Web Services KMS encryption key ID.
The Amazon S3 output path. Must be 128 characters or less.
The Amazon Resource Name (ARN) of the Amazon Web Services Identity and Access Management (IAM) role that has read permission to the input data location and write permission to the output data location in Amazon S3.
Returns the job's objective.
The name of the objective metric used to measure the predictive quality of a machine learning system. This metric is optimized during training to provide the best estimate for model parameter values from data.
Here are the options:
If you do not specify a metric explicitly, the default behavior is to automatically use:
Returns the job's problem type.
Returns the configuration for the AutoML job.
How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.
The maximum number of times a training job is allowed to run.
The maximum time, in seconds, a training job is allowed to run as part of an AutoML job.
The maximum runtime, in seconds, an AutoML job has to complete.
The security configuration for traffic encryption or Amazon VPC settings.
The key used to encrypt stored data.
Whether to use traffic encryption between the container layers.
The VPC configuration.
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
Returns the creation time of the AutoML job.
Returns the end time of the AutoML job.
Returns the job's last modified time.
Returns the failure reason for an AutoML job, when applicable.
Returns a list of reasons for partial failures within an AutoML job.
The reason for a partial failure of an AutoML job.
The message containing the reason for a partial failure of an AutoML job.
Returns the job's best AutoMLCandidate .
The name of the candidate.
The best candidate result from an AutoML training job.
The type of metric with the best result.
The name of the metric with the best result. For a description of the possible objective metrics, see AutoMLJobObjective$MetricName .
The value of the metric with the best result.
The objective's status.
Information about the candidate's steps.
Information about the steps for a candidate and what step it is working on.
Whether the candidate is at the transform, training, or processing step.
The ARN for the candidate's step.
The name for the candidate's step.
The candidate's status.
Information about the inference container definitions.
A list of container definitions that describe the different containers that make up an AutoML candidate. For more information, see .
The ECR path of the container. For more information, see .
The location of the model artifacts. For more information, see .
The environment variables to set in the container. For more information, see .
The creation time.
The end time.
The last modified time.
The failure reason.
The AutoML candidate's properties.
The Amazon S3 prefix to the artifacts generated for an AutoML candidate.
The Amazon S3 prefix to the explainability artifacts generated for the AutoML candidate.
Returns the status of the AutoML job.
Returns the secondary status of the AutoML job.
Indicates whether the output for an AutoML job generates candidate definitions only.
Returns information on the job's artifacts found in AutoMLJobArtifacts .
The URL of the notebook location.
The URL of the notebook location.
This contains ProblemType , AutoMLJobObjective and CompletionCriteria . If you do not provide these values, they are auto-inferred. If you do provide them, the values used are the ones you provide.
Specifies a metric to minimize or maximize as the objective of a job.
The name of the objective metric used to measure the predictive quality of a machine learning system. This metric is optimized during training to provide the best estimate for model parameter values from data.
Here are the options:
If you do not specify a metric explicitly, the default behavior is to automatically use:
The problem type.
How long a job is allowed to run, or how many candidates a job is allowed to generate.
The maximum number of times a training job is allowed to run.
The maximum time, in seconds, a training job is allowed to run as part of an AutoML job.
The maximum runtime, in seconds, an AutoML job has to complete.
Indicates whether the model was deployed automatically to an endpoint and the name of that endpoint if deployed automatically.
Set to True to automatically generate an endpoint name for a one-click Autopilot model deployment; set to False otherwise. The default value is False .
Note
If you set AutoGenerateEndpointName to True , do not specify the EndpointName ; otherwise a 400 error is thrown.
Specifies the endpoint name to use for a one-click Autopilot model deployment if the endpoint name is not generated automatically.
Note
Specify the EndpointName if and only if you set AutoGenerateEndpointName to False ; otherwise a 400 error is thrown.
Provides information about endpoint for the model deployment.
The name of the endpoint to which the model has been deployed.
Note
If model deployment fails, this field is omitted from the response.
Exceptions
Gets details about the specified Git repository.
See also: AWS API Documentation
Request Syntax
response = client.describe_code_repository(
CodeRepositoryName='string'
)
[REQUIRED]
The name of the Git repository to describe.
{
'CodeRepositoryName': 'string',
'CodeRepositoryArn': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'GitConfig': {
'RepositoryUrl': 'string',
'Branch': 'string',
'SecretArn': 'string'
}
}
Response Structure
The name of the Git repository.
The Amazon Resource Name (ARN) of the Git repository.
The date and time that the repository was created.
The date and time that the repository was last changed.
Configuration details about the repository, including the URL where the repository is located, the default branch, and the Amazon Resource Name (ARN) of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the repository.
The URL where the Git repository is located.
The default branch for the Git repository.
The Amazon Resource Name (ARN) of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the git repository. The secret must have a staging label of AWSCURRENT and must be in the following format:
{"username": *UserName* , "password": *Password* }
Returns information about a model compilation job.
To create a model compilation job, use CreateCompilationJob . To get information about multiple model compilation jobs, use ListCompilationJobs .
See also: AWS API Documentation
Request Syntax
response = client.describe_compilation_job(
CompilationJobName='string'
)
[REQUIRED]
The name of the model compilation job that you want information about.
{
'CompilationJobName': 'string',
'CompilationJobArn': 'string',
'CompilationJobStatus': 'INPROGRESS'|'COMPLETED'|'FAILED'|'STARTING'|'STOPPING'|'STOPPED',
'CompilationStartTime': datetime(2015, 1, 1),
'CompilationEndTime': datetime(2015, 1, 1),
'StoppingCondition': {
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123
},
'InferenceImage': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'FailureReason': 'string',
'ModelArtifacts': {
'S3ModelArtifacts': 'string'
},
'ModelDigests': {
'ArtifactDigest': 'string'
},
'RoleArn': 'string',
'InputConfig': {
'S3Uri': 'string',
'DataInputConfig': 'string',
'Framework': 'TENSORFLOW'|'KERAS'|'MXNET'|'ONNX'|'PYTORCH'|'XGBOOST'|'TFLITE'|'DARKNET'|'SKLEARN',
'FrameworkVersion': 'string'
},
'OutputConfig': {
'S3OutputLocation': 'string',
'TargetDevice': 'lambda'|'ml_m4'|'ml_m5'|'ml_c4'|'ml_c5'|'ml_p2'|'ml_p3'|'ml_g4dn'|'ml_inf1'|'ml_eia2'|'jetson_tx1'|'jetson_tx2'|'jetson_nano'|'jetson_xavier'|'rasp3b'|'imx8qm'|'deeplens'|'rk3399'|'rk3288'|'aisage'|'sbe_c'|'qcs605'|'qcs603'|'sitara_am57x'|'amba_cv22'|'amba_cv25'|'x86_win32'|'x86_win64'|'coreml'|'jacinto_tda4vm',
'TargetPlatform': {
'Os': 'ANDROID'|'LINUX',
'Arch': 'X86_64'|'X86'|'ARM64'|'ARM_EABI'|'ARM_EABIHF',
'Accelerator': 'INTEL_GRAPHICS'|'MALI'|'NVIDIA'
},
'CompilerOptions': 'string',
'KmsKeyId': 'string'
},
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
}
Response Structure
The name of the model compilation job.
The Amazon Resource Name (ARN) of the model compilation job.
The status of the model compilation job.
The time when the model compilation job started the CompilationJob instances.
You are billed for the time between this timestamp and the timestamp in the DescribeCompilationJobResponse$CompilationEndTime field. In Amazon CloudWatch Logs, the start time might be later than this time. That's because it takes time to download the compilation job, which depends on the size of the compilation job container.
The time when the model compilation job on a compilation job instance ended. For a successful or stopped job, this is when the job's model artifacts have finished uploading. For a failed job, this is when Amazon SageMaker detected that the job failed.
Specifies a limit to how long a model compilation job can run. When the job reaches the time limit, Amazon SageMaker ends the compilation job. Use this API to cap model training costs.
The maximum length of time, in seconds, that a training or compilation job can run. If the job does not complete during this time, Amazon SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
The default value is 1 day. The maximum value is 28 days.
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, Amazon SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
The inference image to use when compiling a model. Specify an image only if the target device is a cloud instance.
The time that the model compilation job was created.
The time that the status of the model compilation job was last modified.
If a model compilation job failed, the reason it failed.
Information about the location in Amazon S3 that has been configured for storing the model artifacts used in the compilation job.
The path of the S3 object that contains the model artifacts. For example, s3://bucket-name/keynameprefix/model.tar.gz .
Provides a BLAKE2 hash value that identifies the compiled model artifacts in Amazon S3.
Provides a hash value that uniquely identifies the stored model artifacts.
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker assumes to perform the model compilation job.
Information about the location in Amazon S3 of the input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.
The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. The data inputs are InputConfig$Framework specific.
DataInputConfig supports the following parameters for CoreML OutputConfig$TargetDevice (ML Model format):
CoreML ClassifierConfig parameters can be specified using OutputConfig$CompilerOptions . CoreML converter supports Tensorflow and PyTorch models. CoreML conversion examples:
Depending on the model format, DataInputConfig requires the following parameters for ml_eia2 OutputConfig:TargetDevice .
Identifies the framework in which the model was trained. For example: TENSORFLOW.
Specifies the framework version to use.
This API field is only supported for PyTorch framework versions 1.4 , 1.5 , and 1.6 for cloud instance target devices: ml_c4 , ml_c5 , ml_m4 , ml_m5 , ml_p2 , ml_p3 , and ml_g4dn .
Information about the output location for the compiled model and the target device that the model runs on.
Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed. Alternatively, you can specify OS, architecture, and accelerator using TargetPlatform fields. It can be used instead of TargetPlatform .
Contains information about a target platform that you want your model to run on, such as OS, architecture, and accelerators. It is an alternative of TargetDevice .
The following examples show how to configure the TargetPlatform and CompilerOptions JSON strings for popular target platforms:
Specifies a target platform OS.
Specifies a target platform architecture.
Specifies a target platform accelerator (optional).
Specifies additional parameters for compiler options in JSON format. The compiler options are TargetPlatform specific. It is required for NVIDIA accelerators and highly recommended for CPU compilations. For any other cases, it is optional to specify CompilerOptions.
For example, {"dtype" : "float32"} .
For example, {'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver': '10.1'}
For example: {"precision_mode": "FP32", "output_names": ["output:0"]}
The Amazon Web Services Key Management Service key (Amazon Web Services KMS) that Amazon SageMaker uses to encrypt your output models with Amazon S3 server-side encryption after compilation job. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KmsKeyId can be any of the following formats:
A VpcConfig object that specifies the VPC that you want your compilation job to connect to. Control access to your models by configuring the VPC. For more information, see Protect Compilation Jobs by Using an Amazon Virtual Private Cloud .
The VPC security group IDs. IDs have the form of sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.
The ID of the subnets in the VPC that you want to connect the compilation job to for accessing the model in Amazon S3.
Exceptions
Describes a context.
See also: AWS API Documentation
Request Syntax
response = client.describe_context(
ContextName='string'
)
[REQUIRED]
The name of the context to describe.
{
'ContextName': 'string',
'ContextArn': 'string',
'Source': {
'SourceUri': 'string',
'SourceType': 'string',
'SourceId': 'string'
},
'ContextType': 'string',
'Description': 'string',
'Properties': {
'string': 'string'
},
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'LastModifiedTime': datetime(2015, 1, 1),
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
}
}
Response Structure
The name of the context.
The Amazon Resource Name (ARN) of the context.
The source of the context.
The URI of the source.
The type of the source.
The ID of the source.
The type of the context.
The description of the context.
A list of the context's properties.
When the context was created.
Information about the user who created or modified an experiment, trial, or trial component.
The Amazon Resource Name (ARN) of the user's profile.
The name of the user's profile.
The domain associated with the user.
When the context was last modified.
Information about the user who created or modified an experiment, trial, or trial component.
The Amazon Resource Name (ARN) of the user's profile.
The name of the user's profile.
The domain associated with the user.
Exceptions
Gets the details of a data quality monitoring job definition.
See also: AWS API Documentation
Request Syntax
response = client.describe_data_quality_job_definition(
JobDefinitionName='string'
)
[REQUIRED]
The name of the data quality monitoring job definition to describe.
{
'JobDefinitionArn': 'string',
'JobDefinitionName': 'string',
'CreationTime': datetime(2015, 1, 1),
'DataQualityBaselineConfig': {
'BaseliningJobName': 'string',
'ConstraintsResource': {
'S3Uri': 'string'
},
'StatisticsResource': {
'S3Uri': 'string'
}
},
'DataQualityAppSpecification': {
'ImageUri': 'string',
'ContainerEntrypoint': [
'string',
],
'ContainerArguments': [
'string',
],
'RecordPreprocessorSourceUri': 'string',
'PostAnalyticsProcessorSourceUri': 'string',
'Environment': {
'string': 'string'
}
},
'DataQualityJobInput': {
'EndpointInput': {
'EndpointName': 'string',
'LocalPath': 'string',
'S3InputMode': 'Pipe'|'File',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'FeaturesAttribute': 'string',
'InferenceAttribute': 'string',
'ProbabilityAttribute': 'string',
'ProbabilityThresholdAttribute': 123.0,
'StartTimeOffset': 'string',
'EndTimeOffset': 'string'
}
},
'DataQualityJobOutputConfig': {
'MonitoringOutputs': [
{
'S3Output': {
'S3Uri': 'string',
'LocalPath': 'string',
'S3UploadMode': 'Continuous'|'EndOfJob'
}
},
],
'KmsKeyId': 'string'
},
'JobResources': {
'ClusterConfig': {
'InstanceCount': 123,
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
}
},
'NetworkConfig': {
'EnableInterContainerTrafficEncryption': True|False,
'EnableNetworkIsolation': True|False,
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
},
'RoleArn': 'string',
'StoppingCondition': {
'MaxRuntimeInSeconds': 123
}
}
Response Structure
The Amazon Resource Name (ARN) of the data quality monitoring job definition.
The name of the data quality monitoring job definition.
The time that the data quality monitoring job definition was created.
The constraints and baselines for the data quality monitoring job definition.
The name of the job that performs baselining for the data quality monitoring job.
The constraints resource for a monitoring job.
The Amazon S3 URI for the constraints resource.
The statistics resource for a monitoring job.
The Amazon S3 URI for the statistics resource.
Information about the container that runs the data quality monitoring job.
The container image that the data quality monitoring job runs.
The entrypoint for a container used to run a monitoring job.
The arguments to send to the container that the monitoring job runs.
An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flatted json so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.
An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.
Sets the environment variables in the container that the monitoring job runs.
The list of inputs for the data quality monitoring job. Currently endpoints are supported.
Input object for the endpoint
An endpoint in customer's account which has enabled DataCaptureConfig enabled.
Path to the filesystem where the endpoint data is available to the container.
Whether the Pipe or File is used as the input mode for transfering data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .
Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated
The attributes of the input data that are the input features.
The attribute of the input data that represents the ground truth label.
In a classification problem, the attribute that represents the class probability.
The threshold for the class probability to be evaluated as a positive result.
If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
The output configuration for monitoring jobs.
Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.
The output object for a monitoring job.
The Amazon S3 storage location where the results of a monitoring job are saved.
A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.
The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.
Whether to upload the results of the monitoring job continuously or after the job completes.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.
Identifies the resources to deploy for a monitoring job.
The configuration for the cluster resources used to run the processing job.
The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
The ML compute instance type for the processing job.
The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job.
The networking configuration for the data quality monitoring job.
Whether to encrypt all communications between the instances used for the monitoring jobs. Choose True to encrypt communications. Encryption provides greater security for distributed jobs, but the processing might take longer.
Whether to allow inbound and outbound network calls to and from the containers used for the monitoring job.
Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud .
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
A time limit for how long the monitoring job is allowed to run before stopping.
The maximum runtime allowed in seconds.
Note
The MaxRuntimeInSeconds cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.
Exceptions
Describes the device.
See also: AWS API Documentation
Request Syntax
response = client.describe_device(
NextToken='string',
DeviceName='string',
DeviceFleetName='string'
)
[REQUIRED]
The unique ID of the device.
[REQUIRED]
The name of the fleet the devices belong to.
dict
Response Syntax
{
'DeviceArn': 'string',
'DeviceName': 'string',
'Description': 'string',
'DeviceFleetName': 'string',
'IotThingName': 'string',
'RegistrationTime': datetime(2015, 1, 1),
'LatestHeartbeat': datetime(2015, 1, 1),
'Models': [
{
'ModelName': 'string',
'ModelVersion': 'string',
'LatestSampleTime': datetime(2015, 1, 1),
'LatestInference': datetime(2015, 1, 1)
},
],
'MaxModels': 123,
'NextToken': 'string'
}
Response Structure
(dict) --
DeviceArn (string) --
The Amazon Resource Name (ARN) of the device.
DeviceName (string) --
The unique identifier of the device.
Description (string) --
A description of the device.
DeviceFleetName (string) --
The name of the fleet the device belongs to.
IotThingName (string) --
The Amazon Web Services Internet of Things (IoT) object thing name associated with the device.
RegistrationTime (datetime) --
The timestamp of the last registration or de-reregistration.
LatestHeartbeat (datetime) --
The last heartbeat received from the device.
Models (list) --
Models on the device.
(dict) --
The model on the edge device.
ModelName (string) --
The name of the model.
ModelVersion (string) --
The model version.
LatestSampleTime (datetime) --
The timestamp of the last data sample taken.
LatestInference (datetime) --
The timestamp of the last inference that was made.
MaxModels (integer) --
The maximum number of models.
NextToken (string) --
The response from the last list when returning a list large enough to need tokening.
Exceptions
A description of the fleet the device belongs to.
See also: AWS API Documentation
Request Syntax
response = client.describe_device_fleet(
DeviceFleetName='string'
)
[REQUIRED]
The name of the fleet.
{
'DeviceFleetName': 'string',
'DeviceFleetArn': 'string',
'OutputConfig': {
'S3OutputLocation': 'string',
'KmsKeyId': 'string',
'PresetDeploymentType': 'GreengrassV2Component',
'PresetDeploymentConfig': 'string'
},
'Description': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'RoleArn': 'string',
'IotRoleAlias': 'string'
}
Response Structure
The name of the fleet.
The The Amazon Resource Name (ARN) of the fleet.
The output configuration for storing sampled data.
The Amazon Simple Storage (S3) bucker URI.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume after compilation job. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account.
The deployment type SageMaker Edge Manager will create. Currently only supports Amazon Web Services IoT Greengrass Version 2 components.
The configuration used to create deployment artifacts. Specify configuration options with a JSON string. The available configuration options for each type are:
Note
Amazon Web Services IoT Greengrass uses semantic versions for components. Semantic versions follow a*major.minor.patch* number system. For example, version 1.0.0 represents the first major release for a component. For more information, see the semantic version specification .
A description of the fleet.
Timestamp of when the device fleet was created.
Timestamp of when the device fleet was last updated.
The Amazon Resource Name (ARN) that has access to Amazon Web Services Internet of Things (IoT).
The Amazon Resource Name (ARN) alias created in Amazon Web Services Internet of Things (IoT).
Exceptions
The description of the domain.
See also: AWS API Documentation
Request Syntax
response = client.describe_domain(
DomainId='string'
)
[REQUIRED]
The domain ID.
{
'DomainArn': 'string',
'DomainId': 'string',
'DomainName': 'string',
'HomeEfsFileSystemId': 'string',
'SingleSignOnManagedApplicationInstanceId': 'string',
'Status': 'Deleting'|'Failed'|'InService'|'Pending'|'Updating'|'Update_Failed'|'Delete_Failed',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'FailureReason': 'string',
'AuthMode': 'SSO'|'IAM',
'DefaultUserSettings': {
'ExecutionRole': 'string',
'SecurityGroups': [
'string',
],
'SharingSettings': {
'NotebookOutputOption': 'Allowed'|'Disabled',
'S3OutputPath': 'string',
'S3KmsKeyId': 'string'
},
'JupyterServerAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'
}
},
'KernelGatewayAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
]
},
'TensorBoardAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'
}
}
},
'AppNetworkAccessType': 'PublicInternetOnly'|'VpcOnly',
'HomeEfsFileSystemKmsKeyId': 'string',
'SubnetIds': [
'string',
],
'Url': 'string',
'VpcId': 'string',
'KmsKeyId': 'string'
}
Response Structure
The domain's Amazon Resource Name (ARN).
The domain ID.
The domain name.
The ID of the Amazon Elastic File System (EFS) managed by this Domain.
The SSO managed application instance ID.
The status.
The creation time.
The last modified time.
The failure reason.
The domain's authentication mode.
Settings which are applied to UserProfiles in this domain if settings are not explicitly specified in a given UserProfile.
The execution role for the user.
The security groups for the Amazon Virtual Private Cloud (VPC) that Studio uses for communication.
Optional when the CreateDomain.AppNetworkAccessType parameter is set to PublicInternetOnly .
Required when the CreateDomain.AppNetworkAccessType parameter is set to VpcOnly .
Amazon SageMaker adds a security group to allow NFS traffic from SageMaker Studio. Therefore, the number of security groups that you can specify is one less than the maximum number shown.
Specifies options for sharing SageMaker Studio notebooks.
Whether to include the notebook cell output when sharing the notebook. The default is Disabled .
When NotebookOutputOption is Allowed , the Amazon S3 bucket used to store the shared notebook snapshots.
When NotebookOutputOption is Allowed , the Amazon Web Services Key Management Service (KMS) encryption key ID used to encrypt the notebook cell output in the Amazon S3 bucket.
The Jupyter server's app settings.
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app.
The ARN of the SageMaker image that the image version belongs to.
The ARN of the image version created on the instance.
The instance type that the image version runs on.
The kernel gateway app settings.
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.
The ARN of the SageMaker image that the image version belongs to.
The ARN of the image version created on the instance.
The instance type that the image version runs on.
A list of custom SageMaker images that are configured to run as a KernelGateway app.
A custom SageMaker image. For more information, see Bring your own SageMaker image .
The name of the CustomImage. Must be unique to your account.
The version number of the CustomImage.
The name of the AppImageConfig.
The TensorBoard app settings.
The default instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.
The ARN of the SageMaker image that the image version belongs to.
The ARN of the image version created on the instance.
The instance type that the image version runs on.
Specifies the VPC used for non-EFS traffic. The default value is PublicInternetOnly .
This member is deprecated and replaced with KmsKeyId .
The VPC subnets that Studio uses for communication.
The domain's URL.
The ID of the Amazon Virtual Private Cloud (VPC) that Studio uses for communication.
The Amazon Web Services KMS customer managed CMK used to encrypt the EFS volume attached to the domain.
Exceptions
A description of edge packaging jobs.
See also: AWS API Documentation
Request Syntax
response = client.describe_edge_packaging_job(
EdgePackagingJobName='string'
)
[REQUIRED]
The name of the edge packaging job.
{
'EdgePackagingJobArn': 'string',
'EdgePackagingJobName': 'string',
'CompilationJobName': 'string',
'ModelName': 'string',
'ModelVersion': 'string',
'RoleArn': 'string',
'OutputConfig': {
'S3OutputLocation': 'string',
'KmsKeyId': 'string',
'PresetDeploymentType': 'GreengrassV2Component',
'PresetDeploymentConfig': 'string'
},
'ResourceKey': 'string',
'EdgePackagingJobStatus': 'STARTING'|'INPROGRESS'|'COMPLETED'|'FAILED'|'STOPPING'|'STOPPED',
'EdgePackagingJobStatusMessage': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'ModelArtifact': 'string',
'ModelSignature': 'string',
'PresetDeploymentOutput': {
'Type': 'GreengrassV2Component',
'Artifact': 'string',
'Status': 'COMPLETED'|'FAILED',
'StatusMessage': 'string'
}
}
Response Structure
The Amazon Resource Name (ARN) of the edge packaging job.
The name of the edge packaging job.
The name of the SageMaker Neo compilation job that is used to locate model artifacts that are being packaged.
The name of the model.
The version of the model.
The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to download and upload the model, and to contact Neo.
The output configuration for the edge packaging job.
The Amazon Simple Storage (S3) bucker URI.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume after compilation job. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account.
The deployment type SageMaker Edge Manager will create. Currently only supports Amazon Web Services IoT Greengrass Version 2 components.
The configuration used to create deployment artifacts. Specify configuration options with a JSON string. The available configuration options for each type are:
Note
Amazon Web Services IoT Greengrass uses semantic versions for components. Semantic versions follow a*major.minor.patch* number system. For example, version 1.0.0 represents the first major release for a component. For more information, see the semantic version specification .
The CMK to use when encrypting the EBS volume the job run on.
The current status of the packaging job.
Returns a message describing the job status and error messages.
The timestamp of when the packaging job was created.
The timestamp of when the job was last updated.
The Amazon Simple Storage (S3) URI where model artifacts ares stored.
The signature document of files in the model artifact.
The output of a SageMaker Edge Manager deployable resource.
The deployment type created by SageMaker Edge Manager. Currently only supports Amazon Web Services IoT Greengrass Version 2 components.
The Amazon Resource Name (ARN) of the generated deployable resource.
The status of the deployable resource.
Returns a message describing the status of the deployed resource.
Exceptions
Returns the description of an endpoint.
See also: AWS API Documentation
Request Syntax
response = client.describe_endpoint(
EndpointName='string'
)
[REQUIRED]
The name of the endpoint.
{
'EndpointName': 'string',
'EndpointArn': 'string',
'EndpointConfigName': 'string',
'ProductionVariants': [
{
'VariantName': 'string',
'DeployedImages': [
{
'SpecifiedImage': 'string',
'ResolvedImage': 'string',
'ResolutionTime': datetime(2015, 1, 1)
},
],
'CurrentWeight': ...,
'DesiredWeight': ...,
'CurrentInstanceCount': 123,
'DesiredInstanceCount': 123
},
],
'DataCaptureConfig': {
'EnableCapture': True|False,
'CaptureStatus': 'Started'|'Stopped',
'CurrentSamplingPercentage': 123,
'DestinationS3Uri': 'string',
'KmsKeyId': 'string'
},
'EndpointStatus': 'OutOfService'|'Creating'|'Updating'|'SystemUpdating'|'RollingBack'|'InService'|'Deleting'|'Failed',
'FailureReason': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'LastDeploymentConfig': {
'BlueGreenUpdatePolicy': {
'TrafficRoutingConfiguration': {
'Type': 'ALL_AT_ONCE'|'CANARY',
'WaitIntervalInSeconds': 123,
'CanarySize': {
'Type': 'INSTANCE_COUNT'|'CAPACITY_PERCENT',
'Value': 123
}
},
'TerminationWaitInSeconds': 123,
'MaximumExecutionTimeoutInSeconds': 123
},
'AutoRollbackConfiguration': {
'Alarms': [
{
'AlarmName': 'string'
},
]
}
}
}
Response Structure
Name of the endpoint.
The Amazon Resource Name (ARN) of the endpoint.
The name of the endpoint configuration associated with this endpoint.
An array of ProductionVariantSummary objects, one for each model hosted behind this endpoint.
Describes weight and capacities for a production variant associated with an endpoint. If you sent a request to the UpdateEndpointWeightsAndCapacities API and the endpoint status is Updating , you get different desired and current values.
The name of the variant.
An array of DeployedImage objects that specify the Amazon EC2 Container Registry paths of the inference images deployed on instances of this ProductionVariant .
Gets the Amazon EC2 Container Registry path of the docker image of the model that is hosted in this ProductionVariant .
If you used the registry/repository[:tag] form to specify the image path of the primary container when you created the model hosted in this ProductionVariant , the path resolves to a path of the form registry/repository[@digest] . A digest is a hash value that identifies a specific version of an image. For information about Amazon ECR paths, see Pulling an Image in the Amazon ECR User Guide .
The image path you specified when you created the model.
The specific digest path of the image hosted in this ProductionVariant .
The date and time when the image path for the model resolved to the ResolvedImage
The weight associated with the variant.
The requested weight, as specified in the UpdateEndpointWeightsAndCapacities request.
The number of instances associated with the variant.
The number of instances requested in the UpdateEndpointWeightsAndCapacities request.
The status of the endpoint.
If the status of the endpoint is Failed , the reason why it failed.
A timestamp that shows when the endpoint was created.
A timestamp that shows when the endpoint was last modified.
The most recent deployment configuration for the endpoint.
This API is not supported.
This API is not supported.
Returns the description of an endpoint configuration created using the CreateEndpointConfig API.
See also: AWS API Documentation
Request Syntax
response = client.describe_endpoint_config(
EndpointConfigName='string'
)
[REQUIRED]
The name of the endpoint configuration.
{
'EndpointConfigName': 'string',
'EndpointConfigArn': 'string',
'ProductionVariants': [
{
'VariantName': 'string',
'ModelName': 'string',
'InitialInstanceCount': 123,
'InstanceType': 'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge',
'InitialVariantWeight': ...,
'AcceleratorType': 'ml.eia1.medium'|'ml.eia1.large'|'ml.eia1.xlarge'|'ml.eia2.medium'|'ml.eia2.large'|'ml.eia2.xlarge',
'CoreDumpConfig': {
'DestinationS3Uri': 'string',
'KmsKeyId': 'string'
}
},
],
'DataCaptureConfig': {
'EnableCapture': True|False,
'InitialSamplingPercentage': 123,
'DestinationS3Uri': 'string',
'KmsKeyId': 'string',
'CaptureOptions': [
{
'CaptureMode': 'Input'|'Output'
},
],
'CaptureContentTypeHeader': {
'CsvContentTypes': [
'string',
],
'JsonContentTypes': [
'string',
]
}
},
'KmsKeyId': 'string',
'CreationTime': datetime(2015, 1, 1)
}
Response Structure
Name of the Amazon SageMaker endpoint configuration.
The Amazon Resource Name (ARN) of the endpoint configuration.
An array of ProductionVariant objects, one for each model that you want to host at this endpoint.
Identifies a model that you want to host and the resources chosen to deploy for hosting it. If you are deploying multiple models, tell Amazon SageMaker how to distribute traffic among the models by specifying variant weights.
The name of the production variant.
The name of the model that you want to host. This is the name that you specified when creating the model.
Number of instances to launch initially.
The ML compute instance type.
Determines initial traffic distribution among all of the models that you specify in the endpoint configuration. The traffic to a production variant is determined by the ratio of the VariantWeight to the sum of all VariantWeight values across all ProductionVariants. If unspecified, it defaults to 1.0.
The size of the Elastic Inference (EI) instance to use for the production variant. EI instances provide on-demand GPU computing for inference. For more information, see Using Elastic Inference in Amazon SageMaker .
Specifies configuration for a core dump from the model container when the process crashes.
The Amazon S3 bucket to send the core dump to.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the core dump data at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
If you use a KMS key ID or an alias of your master key, the Amazon SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. Amazon SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig . If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateEndpoint and UpdateEndpoint requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
Amazon Web Services KMS key ID Amazon SageMaker uses to encrypt data when storing it on the ML storage volume attached to the instance.
A timestamp that shows when the endpoint configuration was created.
Provides a list of an experiment's properties.
See also: AWS API Documentation
Request Syntax
response = client.describe_experiment(
ExperimentName='string'
)
[REQUIRED]
The name of the experiment to describe.
{
'ExperimentName': 'string',
'ExperimentArn': 'string',
'DisplayName': 'string',
'Source': {
'SourceArn': 'string',
'SourceType': 'string'
},
'Description': 'string',
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'LastModifiedTime': datetime(2015, 1, 1),
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
}
}
Response Structure
The name of the experiment.
The Amazon Resource Name (ARN) of the experiment.
The name of the experiment as displayed. If DisplayName isn't specified, ExperimentName is displayed.
The ARN of the source and, optionally, the type.
The Amazon Resource Name (ARN) of the source.
The source type.
The description of the experiment.
When the experiment was created.
Who created the experiment.
The Amazon Resource Name (ARN) of the user's profile.
The name of the user's profile.
The domain associated with the user.
When the experiment was last modified.
Who last modified the experiment.
The Amazon Resource Name (ARN) of the user's profile.
The name of the user's profile.
The domain associated with the user.
Exceptions
Use this operation to describe a FeatureGroup . The response includes information on the creation time, FeatureGroup name, the unique identifier for each FeatureGroup , and more.
See also: AWS API Documentation
Request Syntax
response = client.describe_feature_group(
FeatureGroupName='string',
NextToken='string'
)
[REQUIRED]
The name of the FeatureGroup you want described.
dict
Response Syntax
{
'FeatureGroupArn': 'string',
'FeatureGroupName': 'string',
'RecordIdentifierFeatureName': 'string',
'EventTimeFeatureName': 'string',
'FeatureDefinitions': [
{
'FeatureName': 'string',
'FeatureType': 'Integral'|'Fractional'|'String'
},
],
'CreationTime': datetime(2015, 1, 1),
'OnlineStoreConfig': {
'SecurityConfig': {
'KmsKeyId': 'string'
},
'EnableOnlineStore': True|False
},
'OfflineStoreConfig': {
'S3StorageConfig': {
'S3Uri': 'string',
'KmsKeyId': 'string',
'ResolvedOutputS3Uri': 'string'
},
'DisableGlueTableCreation': True|False,
'DataCatalogConfig': {
'TableName': 'string',
'Catalog': 'string',
'Database': 'string'
}
},
'RoleArn': 'string',
'FeatureGroupStatus': 'Creating'|'Created'|'CreateFailed'|'Deleting'|'DeleteFailed',
'OfflineStoreStatus': {
'Status': 'Active'|'Blocked'|'Disabled',
'BlockedReason': 'string'
},
'FailureReason': 'string',
'Description': 'string',
'NextToken': 'string'
}
Response Structure
(dict) --
FeatureGroupArn (string) --
The Amazon Resource Name (ARN) of the FeatureGroup .
FeatureGroupName (string) --
he name of the FeatureGroup .
RecordIdentifierFeatureName (string) --
The name of the Feature used for RecordIdentifier , whose value uniquely identifies a record stored in the feature store.
EventTimeFeatureName (string) --
The name of the feature that stores the EventTime of a Record in a FeatureGroup .
An EventTime is a point in time when a new event occurs that corresponds to the creation or update of a Record in a FeatureGroup . All Records in the FeatureGroup have a corresponding EventTime .
FeatureDefinitions (list) --
A list of the Features in the FeatureGroup . Each feature is defined by a FeatureName and FeatureType .
(dict) --
A list of features. You must include FeatureName and FeatureType . Valid feature FeatureType s are Integral , Fractional and String .
FeatureName (string) --
The name of a feature. The type must be a string. FeatureName cannot be any of the following: is_deleted , write_time , api_invocation_time .
FeatureType (string) --
The value type of a feature. Valid values are Integral, Fractional, or String.
CreationTime (datetime) --
A timestamp indicating when SageMaker created the FeatureGroup .
OnlineStoreConfig (dict) --
The configuration for the OnlineStore .
SecurityConfig (dict) --
Use to specify KMS Key ID (KMSKeyId ) for at-rest encryption of your OnlineStore .
KmsKeyId (string) --
The ID of the Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker Feature Store uses to encrypt the Amazon S3 objects at rest using Amazon S3 server-side encryption.
The caller (either IAM user or IAM role) of CreateFeatureGroup must have below permissions to the OnlineStore KmsKeyId :
The caller (either IAM user or IAM role) to all DataPlane operations (PutRecord , GetRecord , DeleteRecord ) must have the following permissions to the KmsKeyId :
EnableOnlineStore (boolean) --
Turn OnlineStore off by specifying False for the EnableOnlineStore flag. Turn OnlineStore on by specifying True for the EnableOnlineStore flag.
The default value is False .
OfflineStoreConfig (dict) --
The configuration of the OfflineStore , inducing the S3 location of the OfflineStore , Amazon Web Services Glue or Amazon Web Services Hive data catalogue configurations, and the security configuration.
S3StorageConfig (dict) --
The Amazon Simple Storage (Amazon S3) location of OfflineStore .
S3Uri (string) --
The S3 URI, or location in Amazon S3, of OfflineStore .
S3 URIs have a format similar to the following: s3://example-bucket/prefix/ .
KmsKeyId (string) --
The Amazon Web Services Key Management Service (KMS) key ID of the key used to encrypt any objects written into the OfflineStore S3 location.
The IAM roleARN that is passed as a parameter to CreateFeatureGroup must have below permissions to the KmsKeyId :
ResolvedOutputS3Uri (string) --
The S3 path where offline records are written.
DisableGlueTableCreation (boolean) --
Set to True to disable the automatic creation of an Amazon Web Services Glue table when configuring an OfflineStore .
DataCatalogConfig (dict) --
The meta data of the Glue table that is autogenerated when an OfflineStore is created.
TableName (string) --
The name of the Glue table.
Catalog (string) --
The name of the Glue table catalog.
Database (string) --
The name of the Glue table database.
RoleArn (string) --
The Amazon Resource Name (ARN) of the IAM execution role used to persist data into the OfflineStore if an OfflineStoreConfig is provided.
FeatureGroupStatus (string) --
The status of the feature group.
OfflineStoreStatus (dict) --
The status of the OfflineStore . Notifies you if replicating data into the OfflineStore has failed. Returns either: Active or Blocked
Status (string) --
An OfflineStore status.
BlockedReason (string) --
The justification for why the OfflineStoreStatus is Blocked (if applicable).
FailureReason (string) --
The reason that the FeatureGroup failed to be replicated in the OfflineStore . This is failure can occur because:
Description (string) --
A free form description of the feature group.
NextToken (string) --
A token to resume pagination of the list of Features (FeatureDefinitions ).
Exceptions
Returns information about the specified flow definition.
See also: AWS API Documentation
Request Syntax
response = client.describe_flow_definition(
FlowDefinitionName='string'
)
[REQUIRED]
The name of the flow definition.
{
'FlowDefinitionArn': 'string',
'FlowDefinitionName': 'string',
'FlowDefinitionStatus': 'Initializing'|'Active'|'Failed'|'Deleting',
'CreationTime': datetime(2015, 1, 1),
'HumanLoopRequestSource': {
'AwsManagedHumanLoopRequestSource': 'AWS/Rekognition/DetectModerationLabels/Image/V3'|'AWS/Textract/AnalyzeDocument/Forms/V1'
},
'HumanLoopActivationConfig': {
'HumanLoopActivationConditionsConfig': {
'HumanLoopActivationConditions': 'string'
}
},
'HumanLoopConfig': {
'WorkteamArn': 'string',
'HumanTaskUiArn': 'string',
'TaskTitle': 'string',
'TaskDescription': 'string',
'TaskCount': 123,
'TaskAvailabilityLifetimeInSeconds': 123,
'TaskTimeLimitInSeconds': 123,
'TaskKeywords': [
'string',
],
'PublicWorkforceTaskPrice': {
'AmountInUsd': {
'Dollars': 123,
'Cents': 123,
'TenthFractionsOfACent': 123
}
}
},
'OutputConfig': {
'S3OutputPath': 'string',
'KmsKeyId': 'string'
},
'RoleArn': 'string',
'FailureReason': 'string'
}
Response Structure
The Amazon Resource Name (ARN) of the flow defintion.
The Amazon Resource Name (ARN) of the flow definition.
The status of the flow definition. Valid values are listed below.
The timestamp when the flow definition was created.
Container for configuring the source of human task requests. Used to specify if Amazon Rekognition or Amazon Textract is used as an integration source.
Specifies whether Amazon Rekognition or Amazon Textract are used as the integration source. The default field settings and JSON parsing rules are different based on the integration source. Valid values:
An object containing information about what triggers a human review workflow.
Container structure for defining under what conditions SageMaker creates a human loop.
JSON expressing use-case specific conditions declaratively. If any condition is matched, atomic tasks are created against the configured work team. The set of conditions is different for Rekognition and Textract. For more information about how to structure the JSON, see JSON Schema for Human Loop Activation Conditions in Amazon Augmented AI in the Amazon SageMaker Developer Guide .
An object containing information about who works on the task, the workforce task price, and other task details.
Amazon Resource Name (ARN) of a team of workers. To learn more about the types of workforces and work teams you can create and use with Amazon A2I, see Create and Manage Workforces .
The Amazon Resource Name (ARN) of the human task user interface.
You can use standard HTML and Crowd HTML Elements to create a custom worker task template. You use this template to create a human task UI.
To learn how to create a custom HTML template, see Create Custom Worker Task Template .
To learn how to create a human task UI, which is a worker task template that can be used in a flow definition, see Create and Delete a Worker Task Templates .
A title for the human worker task.
A description for the human worker task.
The number of distinct workers who will perform the same task on each object. For example, if TaskCount is set to 3 for an image classification labeling job, three workers will classify each input image. Increasing TaskCount can improve label accuracy.
The length of time that a task remains available for review by human workers.
The amount of time that a worker has to complete a task. The default value is 3,600 seconds (1 hour).
Keywords used to describe the task so that workers can discover the task.
Defines the amount of money paid to an Amazon Mechanical Turk worker for each task performed.
Use one of the following prices for bounding box tasks. Prices are in US dollars and should be based on the complexity of the task; the longer it takes in your initial testing, the more you should offer.
Use one of the following prices for image classification, text classification, and custom tasks. Prices are in US dollars.
Use one of the following prices for semantic segmentation tasks. Prices are in US dollars.
Use one of the following prices for Textract AnalyzeDocument Important Form Key Amazon Augmented AI review tasks. Prices are in US dollars.
Use one of the following prices for Rekognition DetectModerationLabels Amazon Augmented AI review tasks. Prices are in US dollars.
Use one of the following prices for Amazon Augmented AI custom human review tasks. Prices are in US dollars.
Defines the amount of money paid to an Amazon Mechanical Turk worker in United States dollars.
The whole number of dollars in the amount.
The fractional portion, in cents, of the amount.
Fractions of a cent, in tenths.
An object containing information about the output file.
The Amazon S3 path where the object containing human output will be made available.
To learn more about the format of Amazon A2I output data, see Amazon A2I Output Data .
The Amazon Key Management Service (KMS) key ID for server-side encryption.
The Amazon Resource Name (ARN) of the Amazon Web Services Identity and Access Management (IAM) execution role for the flow definition.
The reason your flow definition failed.
Exceptions
Returns information about the requested human task user interface (worker task template).
See also: AWS API Documentation
Request Syntax
response = client.describe_human_task_ui(
HumanTaskUiName='string'
)
[REQUIRED]
The name of the human task user interface (worker task template) you want information about.
{
'HumanTaskUiArn': 'string',
'HumanTaskUiName': 'string',
'HumanTaskUiStatus': 'Active'|'Deleting',
'CreationTime': datetime(2015, 1, 1),
'UiTemplate': {
'Url': 'string',
'ContentSha256': 'string'
}
}
Response Structure
The Amazon Resource Name (ARN) of the human task user interface (worker task template).
The name of the human task user interface (worker task template).
The status of the human task user interface (worker task template). Valid values are listed below.
The timestamp when the human task user interface was created.
Container for user interface template information.
The URL for the user interface template.
The SHA-256 digest of the contents of the template.
Exceptions
Gets a description of a hyperparameter tuning job.
See also: AWS API Documentation
Request Syntax
response = client.describe_hyper_parameter_tuning_job(
HyperParameterTuningJobName='string'
)
[REQUIRED]
The name of the tuning job.
{
'HyperParameterTuningJobName': 'string',
'HyperParameterTuningJobArn': 'string',
'HyperParameterTuningJobConfig': {
'Strategy': 'Bayesian'|'Random',
'HyperParameterTuningJobObjective': {
'Type': 'Maximize'|'Minimize',
'MetricName': 'string'
},
'ResourceLimits': {
'MaxNumberOfTrainingJobs': 123,
'MaxParallelTrainingJobs': 123
},
'ParameterRanges': {
'IntegerParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string',
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'ContinuousParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string',
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'CategoricalParameterRanges': [
{
'Name': 'string',
'Values': [
'string',
]
},
]
},
'TrainingJobEarlyStoppingType': 'Off'|'Auto',
'TuningJobCompletionCriteria': {
'TargetObjectiveMetricValue': ...
}
},
'TrainingJobDefinition': {
'DefinitionName': 'string',
'TuningObjective': {
'Type': 'Maximize'|'Minimize',
'MetricName': 'string'
},
'HyperParameterRanges': {
'IntegerParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string',
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'ContinuousParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string',
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'CategoricalParameterRanges': [
{
'Name': 'string',
'Values': [
'string',
]
},
]
},
'StaticHyperParameters': {
'string': 'string'
},
'AlgorithmSpecification': {
'TrainingImage': 'string',
'TrainingInputMode': 'Pipe'|'File',
'AlgorithmName': 'string',
'MetricDefinitions': [
{
'Name': 'string',
'Regex': 'string'
},
]
},
'RoleArn': 'string',
'InputDataConfig': [
{
'ChannelName': 'string',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'AttributeNames': [
'string',
]
},
'FileSystemDataSource': {
'FileSystemId': 'string',
'FileSystemAccessMode': 'rw'|'ro',
'FileSystemType': 'EFS'|'FSxLustre',
'DirectoryPath': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File',
'ShuffleConfig': {
'Seed': 123
}
},
],
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
'OutputDataConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string'
},
'ResourceConfig': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123
},
'EnableNetworkIsolation': True|False,
'EnableInterContainerTrafficEncryption': True|False,
'EnableManagedSpotTraining': True|False,
'CheckpointConfig': {
'S3Uri': 'string',
'LocalPath': 'string'
},
'RetryStrategy': {
'MaximumRetryAttempts': 123
}
},
'TrainingJobDefinitions': [
{
'DefinitionName': 'string',
'TuningObjective': {
'Type': 'Maximize'|'Minimize',
'MetricName': 'string'
},
'HyperParameterRanges': {
'IntegerParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string',
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'ContinuousParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string',
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'CategoricalParameterRanges': [
{
'Name': 'string',
'Values': [
'string',
]
},
]
},
'StaticHyperParameters': {
'string': 'string'
},
'AlgorithmSpecification': {
'TrainingImage': 'string',
'TrainingInputMode': 'Pipe'|'File',
'AlgorithmName': 'string',
'MetricDefinitions': [
{
'Name': 'string',
'Regex': 'string'
},
]
},
'RoleArn': 'string',
'InputDataConfig': [
{
'ChannelName': 'string',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'AttributeNames': [
'string',
]
},
'FileSystemDataSource': {
'FileSystemId': 'string',
'FileSystemAccessMode': 'rw'|'ro',
'FileSystemType': 'EFS'|'FSxLustre',
'DirectoryPath': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File',
'ShuffleConfig': {
'Seed': 123
}
},
],
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
'OutputDataConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string'
},
'ResourceConfig': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123
},
'EnableNetworkIsolation': True|False,
'EnableInterContainerTrafficEncryption': True|False,
'EnableManagedSpotTraining': True|False,
'CheckpointConfig': {
'S3Uri': 'string',
'LocalPath': 'string'
},
'RetryStrategy': {
'MaximumRetryAttempts': 123
}
},
],
'HyperParameterTuningJobStatus': 'Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping',
'CreationTime': datetime(2015, 1, 1),
'HyperParameterTuningEndTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'TrainingJobStatusCounters': {
'Completed': 123,
'InProgress': 123,
'RetryableError': 123,
'NonRetryableError': 123,
'Stopped': 123
},
'ObjectiveStatusCounters': {
'Succeeded': 123,
'Pending': 123,
'Failed': 123
},
'BestTrainingJob': {
'TrainingJobDefinitionName': 'string',
'TrainingJobName': 'string',
'TrainingJobArn': 'string',
'TuningJobName': 'string',
'CreationTime': datetime(2015, 1, 1),
'TrainingStartTime': datetime(2015, 1, 1),
'TrainingEndTime': datetime(2015, 1, 1),
'TrainingJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'TunedHyperParameters': {
'string': 'string'
},
'FailureReason': 'string',
'FinalHyperParameterTuningJobObjectiveMetric': {
'Type': 'Maximize'|'Minimize',
'MetricName': 'string',
'Value': ...
},
'ObjectiveStatus': 'Succeeded'|'Pending'|'Failed'
},
'OverallBestTrainingJob': {
'TrainingJobDefinitionName': 'string',
'TrainingJobName': 'string',
'TrainingJobArn': 'string',
'TuningJobName': 'string',
'CreationTime': datetime(2015, 1, 1),
'TrainingStartTime': datetime(2015, 1, 1),
'TrainingEndTime': datetime(2015, 1, 1),
'TrainingJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'TunedHyperParameters': {
'string': 'string'
},
'FailureReason': 'string',
'FinalHyperParameterTuningJobObjectiveMetric': {
'Type': 'Maximize'|'Minimize',
'MetricName': 'string',
'Value': ...
},
'ObjectiveStatus': 'Succeeded'|'Pending'|'Failed'
},
'WarmStartConfig': {
'ParentHyperParameterTuningJobs': [
{
'HyperParameterTuningJobName': 'string'
},
],
'WarmStartType': 'IdenticalDataAndAlgorithm'|'TransferLearning'
},
'FailureReason': 'string'
}
Response Structure
The name of the tuning job.
The Amazon Resource Name (ARN) of the tuning job.
The HyperParameterTuningJobConfig object that specifies the configuration of the tuning job.
Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job it launches. To use the Bayesian search strategy, set this to Bayesian . To randomly search, set it to Random . For information about search strategies, see How Hyperparameter Tuning Works .
The HyperParameterTuningJobObjective object that specifies the objective metric for this tuning job.
Whether to minimize or maximize the objective metric.
The name of the metric to use for the objective metric.
The ResourceLimits object that specifies the maximum number of training jobs and parallel training jobs for this tuning job.
The maximum number of training jobs that a hyperparameter tuning job can launch.
The maximum number of concurrent training jobs that a hyperparameter tuning job can launch.
The ParameterRanges object that specifies the ranges of hyperparameters that this tuning job searches.
The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches.
The name of the hyperparameter to search.
The minimum value of the hyperparameter to search.
The maximum value of the hyperparameter to search.
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
Amazon SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
A list of continuous hyperparameters to tune.
The name of the continuous hyperparameter to tune.
The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValue for tuning.
The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
Amazon SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ReverseLogarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
A list of categorical hyperparameters to tune.
The name of the categorical hyperparameter to tune.
A list of the categories for the hyperparameter.
Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job. This can be one of the following values (the default value is OFF ):
OFF
Training jobs launched by the hyperparameter tuning job do not use early stopping.
AUTO
Amazon SageMaker stops training jobs launched by the hyperparameter tuning job when they are unlikely to perform better than previously completed training jobs. For more information, see Stop Training Jobs Early .
The tuning job's completion criteria.
The value of the objective metric.
The HyperParameterTrainingJobDefinition object that specifies the definition of the training jobs that this tuning job launches.
The job definition name.
Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the Type parameter.
Whether to minimize or maximize the objective metric.
The name of the metric to use for the objective metric.
Specifies ranges of integer, continuous, and categorical hyperparameters that a hyperparameter tuning job searches. The hyperparameter tuning job launches training jobs with hyperparameter values within these ranges to find the combination of values that result in the training job with the best performance as measured by the objective metric of the hyperparameter tuning job.
Note
You can specify a maximum of 20 hyperparameters that a hyperparameter tuning job can search over. Every possible value of a categorical parameter range counts against this limit.
The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches.
The name of the hyperparameter to search.
The minimum value of the hyperparameter to search.
The maximum value of the hyperparameter to search.
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
Amazon SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
A list of continuous hyperparameters to tune.
The name of the continuous hyperparameter to tune.
The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValue for tuning.
The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
Amazon SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ReverseLogarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
A list of categorical hyperparameters to tune.
The name of the categorical hyperparameter to tune.
A list of the categories for the hyperparameter.
Specifies the values of hyperparameters that do not change for the tuning job.
The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.
The registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters . Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
The input mode that the algorithm supports: File or Pipe. In File input mode, Amazon SageMaker downloads the training data from Amazon S3 to the storage volume that is attached to the training instance and mounts the directory to the Docker volume for the training container. In Pipe input mode, Amazon SageMaker streams data directly from Amazon S3 to the container.
If you specify File mode, make sure that you provision the storage volume that is attached to the training instance with enough capacity to accommodate the training data downloaded from Amazon S3, the model artifacts, and intermediate information.
For more information about input modes, see Algorithms .
The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this parameter, do not specify a value for TrainingImage .
An array of MetricDefinition objects that specify the metrics that the algorithm emits.
Specifies a metric that the training algorithm writes to stderr or stdout . Amazon SageMakerhyperparameter tuning captures all defined metrics. You specify one metric that a hyperparameter tuning job uses as its objective metric to choose the best training job.
The name of the metric.
A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining Objective Metrics .
The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
An array of Channel objects that specify the input for the training jobs that the tuning job launches.
A channel is a named input source that training algorithms can consume.
The name of the channel.
The location of the channel data.
The S3 location of the data source that is associated with a channel.
If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects that match the specified key name prefix for model training.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for model training.
If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
If you want Amazon SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .
If you want Amazon SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.
A list of one or more attribute names to use that are found in a specified augmented manifest file.
The file system that is associated with a channel.
The file system id.
The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.
The file system type.
The full path to the directory to associate with the channel.
The MIME type of the data.
If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, Amazon SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO .
In File mode, leave this field unset or set it to None.
(Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , Amazon SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
To use a model for incremental training, choose File input model.
A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
Determines the shuffling order in ShuffleConfig value.
The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud .
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
If you use a KMS key ID or an alias of your master key, the Amazon SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. Amazon SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig . If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
Identifies the S3 path where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want Amazon SageMaker to use the storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.
The ML compute instance type.
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.
You must specify sufficient ML storage for your scenario.
Note
Amazon SageMaker supports only the General Purpose SSD (gp2) ML storage volume type.
Note
Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for training, Amazon SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can't request a VolumeSizeInGB greater than the total size of the local instance storage.
For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes .
The Amazon Web Services KMS key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be in any of the following formats:
Specifies a limit to how long a model hyperparameter training job can run. It also specifies how long a managed spot training job has to complete. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.
The maximum length of time, in seconds, that a training or compilation job can run. If the job does not complete during this time, Amazon SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
The default value is 1 day. The maximum value is 28 days.
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, Amazon SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
To encrypt all communications between ML compute instances in distributed training, choose True . Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.
A Boolean indicating whether managed spot training is enabled (True ) or not (False ).
Contains information about the output location for managed spot training checkpoint data.
Identifies the S3 path where you want Amazon SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix .
(Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/ .
The number of times to retry the job when the job fails due to an InternalServerError .
The number of times to retry the job. When the job is retried, it's SecondaryStatus is changed to STARTING .
A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.
Defines the training jobs launched by a hyperparameter tuning job.
The job definition name.
Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the Type parameter.
Whether to minimize or maximize the objective metric.
The name of the metric to use for the objective metric.
Specifies ranges of integer, continuous, and categorical hyperparameters that a hyperparameter tuning job searches. The hyperparameter tuning job launches training jobs with hyperparameter values within these ranges to find the combination of values that result in the training job with the best performance as measured by the objective metric of the hyperparameter tuning job.
Note
You can specify a maximum of 20 hyperparameters that a hyperparameter tuning job can search over. Every possible value of a categorical parameter range counts against this limit.
The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches.
The name of the hyperparameter to search.
The minimum value of the hyperparameter to search.
The maximum value of the hyperparameter to search.
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
Amazon SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
A list of continuous hyperparameters to tune.
The name of the continuous hyperparameter to tune.
The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValue for tuning.
The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
Amazon SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ReverseLogarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
A list of categorical hyperparameters to tune.
The name of the categorical hyperparameter to tune.
A list of the categories for the hyperparameter.
Specifies the values of hyperparameters that do not change for the tuning job.
The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.
The registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters . Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
The input mode that the algorithm supports: File or Pipe. In File input mode, Amazon SageMaker downloads the training data from Amazon S3 to the storage volume that is attached to the training instance and mounts the directory to the Docker volume for the training container. In Pipe input mode, Amazon SageMaker streams data directly from Amazon S3 to the container.
If you specify File mode, make sure that you provision the storage volume that is attached to the training instance with enough capacity to accommodate the training data downloaded from Amazon S3, the model artifacts, and intermediate information.
For more information about input modes, see Algorithms .
The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this parameter, do not specify a value for TrainingImage .
An array of MetricDefinition objects that specify the metrics that the algorithm emits.
Specifies a metric that the training algorithm writes to stderr or stdout . Amazon SageMakerhyperparameter tuning captures all defined metrics. You specify one metric that a hyperparameter tuning job uses as its objective metric to choose the best training job.
The name of the metric.
A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining Objective Metrics .
The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
An array of Channel objects that specify the input for the training jobs that the tuning job launches.
A channel is a named input source that training algorithms can consume.
The name of the channel.
The location of the channel data.
The S3 location of the data source that is associated with a channel.
If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects that match the specified key name prefix for model training.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for model training.
If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
If you want Amazon SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .
If you want Amazon SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.
A list of one or more attribute names to use that are found in a specified augmented manifest file.
The file system that is associated with a channel.
The file system id.
The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.
The file system type.
The full path to the directory to associate with the channel.
The MIME type of the data.
If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, Amazon SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO .
In File mode, leave this field unset or set it to None.
(Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , Amazon SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
To use a model for incremental training, choose File input model.
A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
Determines the shuffling order in ShuffleConfig value.
The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud .
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
If you use a KMS key ID or an alias of your master key, the Amazon SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. Amazon SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig . If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
Identifies the S3 path where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want Amazon SageMaker to use the storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.
The ML compute instance type.
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.
You must specify sufficient ML storage for your scenario.
Note
Amazon SageMaker supports only the General Purpose SSD (gp2) ML storage volume type.
Note
Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for training, Amazon SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can't request a VolumeSizeInGB greater than the total size of the local instance storage.
For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes .
The Amazon Web Services KMS key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be in any of the following formats:
Specifies a limit to how long a model hyperparameter training job can run. It also specifies how long a managed spot training job has to complete. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.
The maximum length of time, in seconds, that a training or compilation job can run. If the job does not complete during this time, Amazon SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
The default value is 1 day. The maximum value is 28 days.
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, Amazon SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
To encrypt all communications between ML compute instances in distributed training, choose True . Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.
A Boolean indicating whether managed spot training is enabled (True ) or not (False ).
Contains information about the output location for managed spot training checkpoint data.
Identifies the S3 path where you want Amazon SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix .
(Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/ .
The number of times to retry the job when the job fails due to an InternalServerError .
The number of times to retry the job. When the job is retried, it's SecondaryStatus is changed to STARTING .
The status of the tuning job: InProgress, Completed, Failed, Stopping, or Stopped.
The date and time that the tuning job started.
The date and time that the tuning job ended.
The date and time that the status of the tuning job was modified.
The TrainingJobStatusCounters object that specifies the number of training jobs, categorized by status, that this tuning job launched.
The number of completed training jobs launched by the hyperparameter tuning job.
The number of in-progress training jobs launched by a hyperparameter tuning job.
The number of training jobs that failed, but can be retried. A failed training job can be retried only if it failed because an internal service error occurred.
The number of training jobs that failed and can't be retried. A failed training job can't be retried if it failed because a client error occurred.
The number of training jobs launched by a hyperparameter tuning job that were manually stopped.
The ObjectiveStatusCounters object that specifies the number of training jobs, categorized by the status of their final objective metric, that this tuning job launched.
The number of training jobs whose final objective metric was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.
The number of training jobs that are in progress and pending evaluation of their final objective metric.
The number of training jobs whose final objective metric was not evaluated and used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.
A TrainingJobSummary object that describes the training job that completed with the best current HyperParameterTuningJobObjective .
The training job definition name.
The name of the training job.
The Amazon Resource Name (ARN) of the training job.
The HyperParameter tuning job that launched the training job.
The date and time that the training job was created.
The date and time that the training job started.
Specifies the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when Amazon SageMaker detects a job failure.
The status of the training job.
A list of the hyperparameters for which you specified ranges to search.
The reason that the training job failed.
The FinalHyperParameterTuningJobObjectiveMetric object that specifies the value of the objective metric of the tuning job that launched this training job.
Whether to minimize or maximize the objective metric. Valid values are Minimize and Maximize.
The name of the objective metric.
The value of the objective metric.
The status of the objective metric for the training job:
If the hyperparameter tuning job is an warm start tuning job with a WarmStartType of IDENTICAL_DATA_AND_ALGORITHM , this is the TrainingJobSummary for the training job with the best objective metric value of all training jobs launched by this tuning job and all parent jobs specified for the warm start tuning job.
The training job definition name.
The name of the training job.
The Amazon Resource Name (ARN) of the training job.
The HyperParameter tuning job that launched the training job.
The date and time that the training job was created.
The date and time that the training job started.
Specifies the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when Amazon SageMaker detects a job failure.
The status of the training job.
A list of the hyperparameters for which you specified ranges to search.
The reason that the training job failed.
The FinalHyperParameterTuningJobObjectiveMetric object that specifies the value of the objective metric of the tuning job that launched this training job.
Whether to minimize or maximize the objective metric. Valid values are Minimize and Maximize.
The name of the objective metric.
The value of the objective metric.
The status of the objective metric for the training job:
The configuration for starting the hyperparameter parameter tuning job using one or more previous tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.
An array of hyperparameter tuning jobs that are used as the starting point for the new hyperparameter tuning job. For more information about warm starting a hyperparameter tuning job, see Using a Previous Hyperparameter Tuning Job as a Starting Point .
Hyperparameter tuning jobs created before October 1, 2018 cannot be used as parent jobs for warm start tuning jobs.
A previously completed or stopped hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job.
The name of the hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job.
Specifies one of the following:
IDENTICAL_DATA_AND_ALGORITHM
The new hyperparameter tuning job uses the same input data and training image as the parent tuning jobs. You can change the hyperparameter ranges to search and the maximum number of training jobs that the hyperparameter tuning job launches. You cannot use a new version of the training algorithm, unless the changes in the new version do not affect the algorithm itself. For example, changes that improve logging or adding support for a different data format are allowed. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.
TRANSFER_LEARNING
The new hyperparameter tuning job can include input data, hyperparameter ranges, maximum number of concurrent training jobs, and maximum number of training jobs that are different than those of its parent hyperparameter tuning jobs. The training image can also be a different version from the version used in the parent hyperparameter tuning job. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.
If the tuning job failed, the reason it failed.
Exceptions
Describes a SageMaker image.
See also: AWS API Documentation
Request Syntax
response = client.describe_image(
ImageName='string'
)
[REQUIRED]
The name of the image to describe.
{
'CreationTime': datetime(2015, 1, 1),
'Description': 'string',
'DisplayName': 'string',
'FailureReason': 'string',
'ImageArn': 'string',
'ImageName': 'string',
'ImageStatus': 'CREATING'|'CREATED'|'CREATE_FAILED'|'UPDATING'|'UPDATE_FAILED'|'DELETING'|'DELETE_FAILED',
'LastModifiedTime': datetime(2015, 1, 1),
'RoleArn': 'string'
}
Response Structure
When the image was created.
The description of the image.
The name of the image as displayed.
When a create, update, or delete operation fails, the reason for the failure.
The Amazon Resource Name (ARN) of the image.
The name of the image.
The status of the image.
When the image was last modified.
The Amazon Resource Name (ARN) of the IAM role that enables Amazon SageMaker to perform tasks on your behalf.
Exceptions
Describes a version of a SageMaker image.
See also: AWS API Documentation
Request Syntax
response = client.describe_image_version(
ImageName='string',
Version=123
)
[REQUIRED]
The name of the image.
dict
Response Syntax
{
'BaseImage': 'string',
'ContainerImage': 'string',
'CreationTime': datetime(2015, 1, 1),
'FailureReason': 'string',
'ImageArn': 'string',
'ImageVersionArn': 'string',
'ImageVersionStatus': 'CREATING'|'CREATED'|'CREATE_FAILED'|'DELETING'|'DELETE_FAILED',
'LastModifiedTime': datetime(2015, 1, 1),
'Version': 123
}
Response Structure
(dict) --
BaseImage (string) --
The registry path of the container image on which this image version is based.
ContainerImage (string) --
The registry path of the container image that contains this image version.
CreationTime (datetime) --
When the version was created.
FailureReason (string) --
When a create or delete operation fails, the reason for the failure.
ImageArn (string) --
The Amazon Resource Name (ARN) of the image the version is based on.
ImageVersionArn (string) --
The ARN of the version.
ImageVersionStatus (string) --
The status of the version.
LastModifiedTime (datetime) --
When the version was last modified.
Version (integer) --
The version number.
Exceptions
Gets information about a labeling job.
See also: AWS API Documentation
Request Syntax
response = client.describe_labeling_job(
LabelingJobName='string'
)
[REQUIRED]
The name of the labeling job to return information for.
{
'LabelingJobStatus': 'Initializing'|'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'LabelCounters': {
'TotalLabeled': 123,
'HumanLabeled': 123,
'MachineLabeled': 123,
'FailedNonRetryableError': 123,
'Unlabeled': 123
},
'FailureReason': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'JobReferenceCode': 'string',
'LabelingJobName': 'string',
'LabelingJobArn': 'string',
'LabelAttributeName': 'string',
'InputConfig': {
'DataSource': {
'S3DataSource': {
'ManifestS3Uri': 'string'
},
'SnsDataSource': {
'SnsTopicArn': 'string'
}
},
'DataAttributes': {
'ContentClassifiers': [
'FreeOfPersonallyIdentifiableInformation'|'FreeOfAdultContent',
]
}
},
'OutputConfig': {
'S3OutputPath': 'string',
'KmsKeyId': 'string',
'SnsTopicArn': 'string'
},
'RoleArn': 'string',
'LabelCategoryConfigS3Uri': 'string',
'StoppingConditions': {
'MaxHumanLabeledObjectCount': 123,
'MaxPercentageOfInputDatasetLabeled': 123
},
'LabelingJobAlgorithmsConfig': {
'LabelingJobAlgorithmSpecificationArn': 'string',
'InitialActiveLearningModelArn': 'string',
'LabelingJobResourceConfig': {
'VolumeKmsKeyId': 'string'
}
},
'HumanTaskConfig': {
'WorkteamArn': 'string',
'UiConfig': {
'UiTemplateS3Uri': 'string',
'HumanTaskUiArn': 'string'
},
'PreHumanTaskLambdaArn': 'string',
'TaskKeywords': [
'string',
],
'TaskTitle': 'string',
'TaskDescription': 'string',
'NumberOfHumanWorkersPerDataObject': 123,
'TaskTimeLimitInSeconds': 123,
'TaskAvailabilityLifetimeInSeconds': 123,
'MaxConcurrentTaskCount': 123,
'AnnotationConsolidationConfig': {
'AnnotationConsolidationLambdaArn': 'string'
},
'PublicWorkforceTaskPrice': {
'AmountInUsd': {
'Dollars': 123,
'Cents': 123,
'TenthFractionsOfACent': 123
}
}
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
],
'LabelingJobOutput': {
'OutputDatasetS3Uri': 'string',
'FinalActiveLearningModelArn': 'string'
}
}
Response Structure
The processing status of the labeling job.
Provides a breakdown of the number of data objects labeled by humans, the number of objects labeled by machine, the number of objects than couldn't be labeled, and the total number of objects labeled.
The total number of objects labeled.
The total number of objects labeled by a human worker.
The total number of objects labeled by automated data labeling.
The total number of objects that could not be labeled due to an error.
The total number of objects not yet labeled.
If the job failed, the reason that it failed.
The date and time that the labeling job was created.
The date and time that the labeling job was last updated.
A unique identifier for work done as part of a labeling job.
The name assigned to the labeling job when it was created.
The Amazon Resource Name (ARN) of the labeling job.
The attribute used as the label in the output manifest file.
Input configuration information for the labeling job, such as the Amazon S3 location of the data objects and the location of the manifest file that describes the data objects.
The location of the input data.
The Amazon S3 location of the input data objects.
The Amazon S3 location of the manifest file that describes the input data objects.
The input manifest file referenced in ManifestS3Uri must contain one of the following keys: source-ref or source . The value of the keys are interpreted as follows:
If you are a new user of Ground Truth, it is recommended you review Use an Input Manifest File in the Amazon SageMaker Developer Guide to learn how to create an input manifest file.
An Amazon SNS data source used for streaming labeling jobs. To learn more, see Send Data to a Streaming Labeling Job .
The Amazon SNS input topic Amazon Resource Name (ARN). Specify the ARN of the input topic you will use to send new data objects to a streaming labeling job.
Attributes of the data specified by the customer.
Declares that your content is free of personally identifiable information or adult content. Amazon SageMaker may restrict the Amazon Mechanical Turk workers that can view your task based on this information.
The location of the job's output data and the Amazon Web Services Key Management Service key ID for the key used to encrypt the output data, if any.
The Amazon S3 location to write output data.
The Amazon Web Services Key Management Service ID of the key used to encrypt the output data, if any.
If you provide your own KMS key ID, you must add the required permissions to your KMS key described in Encrypt Output Data and Storage Volume with Amazon Web Services KMS .
If you don't provide a KMS key ID, Amazon SageMaker uses the default Amazon Web Services KMS key for Amazon S3 for your role's account to encrypt your output data.
If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
An Amazon Simple Notification Service (Amazon SNS) output topic ARN. Provide a SnsTopicArn if you want to do real time chaining to another streaming job and receive an Amazon SNS notifications each time a data object is submitted by a worker.
If you provide an SnsTopicArn in OutputConfig , when workers complete labeling tasks, Ground Truth will send labeling task output data to the SNS output topic you specify here.
To learn more, see Receive Output Data from a Streaming Labeling Job .
The Amazon Resource Name (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during data labeling.
The S3 location of the JSON file that defines the categories used to label data objects. Please note the following label-category limits:
The file is a JSON structure in the following format:
{"document-version": "2018-11-28"
"labels": [
{
"label": "*label 1* "
},
{
"label": "*label 2* "
},
...
{
"label": "*label n* "
}
]
}
A set of conditions for stopping a labeling job. If any of the conditions are met, the job is automatically stopped.
The maximum number of objects that can be labeled by human workers.
The maximum number of input data objects that should be labeled.
Configuration information for automated data labeling.
Specifies the Amazon Resource Name (ARN) of the algorithm used for auto-labeling. You must select one of the following ARNs:
At the end of an auto-label job Ground Truth sends the Amazon Resource Name (ARN) of the final model used for auto-labeling. You can use this model as the starting point for subsequent similar jobs by providing the ARN of the model here.
Provides configuration information for a labeling job.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training and inference jobs used for automated data labeling.
You can only specify a VolumeKmsKeyId when you create a labeling job with automated data labeling enabled using the API operation CreateLabelingJob . You cannot specify an Amazon Web Services KMS customer managed CMK to encrypt the storage volume used for automated data labeling model training and inference when you create a labeling job using the console. To learn more, see Output Data and Storage Volume Encryption .
The VolumeKmsKeyId can be any of the following formats:
Configuration information required for human workers to complete a labeling task.
The Amazon Resource Name (ARN) of the work team assigned to complete the tasks.
Information about the user interface that workers use to complete the labeling task.
The Amazon S3 bucket location of the UI template, or worker task template. This is the template used to render the worker UI and tools for labeling job tasks. For more information about the contents of a UI template, see Creating Your Custom Labeling Task Template .
The ARN of the worker task template used to render the worker UI and tools for labeling job tasks.
Use this parameter when you are creating a labeling job for 3D point cloud and video fram labeling jobs. Use your labeling job task type to select one of the following ARNs and use it with this parameter when you create a labeling job. Replace aws-region with the Amazon Web Services region you are creating your labeling job in.
3D Point Cloud HumanTaskUiArns
Use this HumanTaskUiArn for 3D point cloud object detection and 3D point cloud object detection adjustment labeling jobs.
Use this HumanTaskUiArn for 3D point cloud object tracking and 3D point cloud object tracking adjustment labeling jobs.
Use this HumanTaskUiArn for 3D point cloud semantic segmentation and 3D point cloud semantic segmentation adjustment labeling jobs.
Video Frame HumanTaskUiArns
Use this HumanTaskUiArn for video frame object detection and video frame object detection adjustment labeling jobs.
Use this HumanTaskUiArn for video frame object tracking and video frame object tracking adjustment labeling jobs.
The Amazon Resource Name (ARN) of a Lambda function that is run before a data object is sent to a human worker. Use this function to provide input to a custom labeling job.
For built-in task types , use one of the following Amazon SageMaker Ground Truth Lambda function ARNs for PreHumanTaskLambdaArn . For custom labeling workflows, see Pre-annotation Lambda .
Bounding box - Finds the most similar boxes from different workers based on the Jaccard index of the boxes.
Image classification - Uses a variant of the Expectation Maximization approach to estimate the true class of an image based on annotations from individual workers.
Multi-label image classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of an image based on annotations from individual workers.
Semantic segmentation - Treats each pixel in an image as a multi-class classification and treats pixel annotations from workers as "votes" for the correct label.
Text classification - Uses a variant of the Expectation Maximization approach to estimate the true class of text based on annotations from individual workers.
Multi-label text classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of text based on annotations from individual workers.
Named entity recognition - Groups similar selections and calculates aggregate boundaries, resolving to most-assigned label.
Video Classification - Use this task type when you need workers to classify videos using predefined labels that you specify. Workers are shown videos and are asked to choose one label for each video.
Video Frame Object Detection - Use this task type to have workers identify and locate objects in a sequence of video frames (images extracted from a video) using bounding boxes. For example, you can use this task to ask workers to identify and localize various objects in a series of video frames, such as cars, bikes, and pedestrians.
Video Frame Object Tracking - Use this task type to have workers track the movement of objects in a sequence of video frames (images extracted from a video) using bounding boxes. For example, you can use this task to ask workers to track the movement of objects, such as cars, bikes, and pedestrians.
3D Point Cloud Modalities
Use the following pre-annotation lambdas for 3D point cloud labeling modality tasks. See 3D Point Cloud Task types to learn more.
3D Point Cloud Object Detection - Use this task type when you want workers to classify objects in a 3D point cloud by drawing 3D cuboids around objects. For example, you can use this task type to ask workers to identify different types of objects in a point cloud, such as cars, bikes, and pedestrians.
3D Point Cloud Object Tracking - Use this task type when you want workers to draw 3D cuboids around objects that appear in a sequence of 3D point cloud frames. For example, you can use this task type to ask workers to track the movement of vehicles across multiple point cloud frames.
3D Point Cloud Semantic Segmentation - Use this task type when you want workers to create a point-level semantic segmentation masks by painting objects in a 3D point cloud using different colors where each color is assigned to one of the classes you specify.
Use the following ARNs for Label Verification and Adjustment Jobs
Use label verification and adjustment jobs to review and adjust labels. To learn more, see Verify and Adjust Labels .
Bounding box verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgement for bounding box labels based on annotations from individual workers.
Bounding box adjustment - Finds the most similar boxes from different workers based on the Jaccard index of the adjusted annotations.
Semantic segmentation verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgment for semantic segmentation labels based on annotations from individual workers.
Semantic segmentation adjustment - Treats each pixel in an image as a multi-class classification and treats pixel adjusted annotations from workers as "votes" for the correct label.
Video Frame Object Detection Adjustment - Use this task type when you want workers to adjust bounding boxes that workers have added to video frames to classify and localize objects in a sequence of video frames.
Video Frame Object Tracking Adjustment - Use this task type when you want workers to adjust bounding boxes that workers have added to video frames to track object movement across a sequence of video frames.
3D point cloud object detection adjustment - Adjust 3D cuboids in a point cloud frame.
3D point cloud object tracking adjustment - Adjust 3D cuboids across a sequence of point cloud frames.
3D point cloud semantic segmentation adjustment - Adjust semantic segmentation masks in a 3D point cloud.
Keywords used to describe the task so that workers on Amazon Mechanical Turk can discover the task.
A title for the task for your human workers.
A description of the task for your human workers.
The number of human workers that will label an object.
The amount of time that a worker has to complete a task.
If you create a custom labeling job, the maximum value for this parameter is 8 hours (28,800 seconds).
If you create a labeling job using a built-in task type the maximum for this parameter depends on the task type you use:
The length of time that a task remains available for labeling by human workers. The default and maximum values for this parameter depend on the type of workforce you use.
Defines the maximum number of data objects that can be labeled by human workers at the same time. Also referred to as batch size. Each object may have more than one worker at one time. The default value is 1000 objects.
Configures how labels are consolidated across human workers.
The Amazon Resource Name (ARN) of a Lambda function implements the logic for annotation consolidation and to process output data.
This parameter is required for all labeling jobs. For built-in task types , use one of the following Amazon SageMaker Ground Truth Lambda function ARNs for AnnotationConsolidationLambdaArn . For custom labeling workflows, see Post-annotation Lambda .
Bounding box - Finds the most similar boxes from different workers based on the Jaccard index of the boxes.
Image classification - Uses a variant of the Expectation Maximization approach to estimate the true class of an image based on annotations from individual workers.
Multi-label image classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of an image based on annotations from individual workers.
Semantic segmentation - Treats each pixel in an image as a multi-class classification and treats pixel annotations from workers as "votes" for the correct label.
Text classification - Uses a variant of the Expectation Maximization approach to estimate the true class of text based on annotations from individual workers.
Multi-label text classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of text based on annotations from individual workers.
Named entity recognition - Groups similar selections and calculates aggregate boundaries, resolving to most-assigned label.
Video Classification - Use this task type when you need workers to classify videos using predefined labels that you specify. Workers are shown videos and are asked to choose one label for each video.
Video Frame Object Detection - Use this task type to have workers identify and locate objects in a sequence of video frames (images extracted from a video) using bounding boxes. For example, you can use this task to ask workers to identify and localize various objects in a series of video frames, such as cars, bikes, and pedestrians.
Video Frame Object Tracking - Use this task type to have workers track the movement of objects in a sequence of video frames (images extracted from a video) using bounding boxes. For example, you can use this task to ask workers to track the movement of objects, such as cars, bikes, and pedestrians.
3D Point Cloud Object Detection - Use this task type when you want workers to classify objects in a 3D point cloud by drawing 3D cuboids around objects. For example, you can use this task type to ask workers to identify different types of objects in a point cloud, such as cars, bikes, and pedestrians.
3D Point Cloud Object Tracking - Use this task type when you want workers to draw 3D cuboids around objects that appear in a sequence of 3D point cloud frames. For example, you can use this task type to ask workers to track the movement of vehicles across multiple point cloud frames.
3D Point Cloud Semantic Segmentation - Use this task type when you want workers to create a point-level semantic segmentation masks by painting objects in a 3D point cloud using different colors where each color is assigned to one of the classes you specify.
Use the following ARNs for Label Verification and Adjustment Jobs
Use label verification and adjustment jobs to review and adjust labels. To learn more, see Verify and Adjust Labels .
Semantic Segmentation Adjustment - Treats each pixel in an image as a multi-class classification and treats pixel adjusted annotations from workers as "votes" for the correct label.
Semantic Segmentation Verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgment for semantic segmentation labels based on annotations from individual workers.
Bounding Box Adjustment - Finds the most similar boxes from different workers based on the Jaccard index of the adjusted annotations.
Bounding Box Verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgement for bounding box labels based on annotations from individual workers.
Video Frame Object Detection Adjustment - Use this task type when you want workers to adjust bounding boxes that workers have added to video frames to classify and localize objects in a sequence of video frames.
Video Frame Object Tracking Adjustment - Use this task type when you want workers to adjust bounding boxes that workers have added to video frames to track object movement across a sequence of video frames.
3D Point Cloud Object Detection Adjustment - Use this task type when you want workers to adjust 3D cuboids around objects in a 3D point cloud.
3D Point Cloud Object Tracking Adjustment - Use this task type when you want workers to adjust 3D cuboids around objects that appear in a sequence of 3D point cloud frames.
3D Point Cloud Semantic Segmentation Adjustment - Use this task type when you want workers to adjust a point-level semantic segmentation masks using a paint tool.
The price that you pay for each task performed by an Amazon Mechanical Turk worker.
Defines the amount of money paid to an Amazon Mechanical Turk worker in United States dollars.
The whole number of dollars in the amount.
The fractional portion, in cents, of the amount.
Fractions of a cent, in tenths.
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources .
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
The location of the output produced by the labeling job.
The Amazon S3 bucket location of the manifest file for labeled data.
The Amazon Resource Name (ARN) for the most recent Amazon SageMaker model trained as part of automated data labeling.
Exceptions
Describes a model that you created using the CreateModel API.
See also: AWS API Documentation
Request Syntax
response = client.describe_model(
ModelName='string'
)
[REQUIRED]
The name of the model.
{
'ModelName': 'string',
'PrimaryContainer': {
'ContainerHostname': 'string',
'Image': 'string',
'ImageConfig': {
'RepositoryAccessMode': 'Platform'|'Vpc',
'RepositoryAuthConfig': {
'RepositoryCredentialsProviderArn': 'string'
}
},
'Mode': 'SingleModel'|'MultiModel',
'ModelDataUrl': 'string',
'Environment': {
'string': 'string'
},
'ModelPackageName': 'string',
'MultiModelConfig': {
'ModelCacheSetting': 'Enabled'|'Disabled'
}
},
'Containers': [
{
'ContainerHostname': 'string',
'Image': 'string',
'ImageConfig': {
'RepositoryAccessMode': 'Platform'|'Vpc',
'RepositoryAuthConfig': {
'RepositoryCredentialsProviderArn': 'string'
}
},
'Mode': 'SingleModel'|'MultiModel',
'ModelDataUrl': 'string',
'Environment': {
'string': 'string'
},
'ModelPackageName': 'string',
'MultiModelConfig': {
'ModelCacheSetting': 'Enabled'|'Disabled'
}
},
],
'InferenceExecutionConfig': {
'Mode': 'Serial'|'Direct'
},
'ExecutionRoleArn': 'string',
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
'CreationTime': datetime(2015, 1, 1),
'ModelArn': 'string',
'EnableNetworkIsolation': True|False
}
Response Structure
Name of the Amazon SageMaker model.
The location of the primary inference code, associated artifacts, and custom environment map that the inference code uses when it is deployed in production.
This parameter is ignored for models that contain only a PrimaryContainer .
When a ContainerDefinition is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline . If you don't specify a value for this parameter for a ContainerDefinition that is part of an inference pipeline, a unique name is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value for the ContainerHostName for any ContainerDefinition that is part of an inference pipeline, you must specify a value for the ContainerHostName parameter of every ContainerDefinition in that pipeline.
The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. If you are using your own custom algorithm instead of an algorithm provided by Amazon SageMaker, the inference code must meet Amazon SageMaker requirements. Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker
Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers
Set this to one of the following values:
(Optional) Specifies an authentication configuration for the private docker registry where your model image is hosted. Specify a value for this property only if you specified Vpc as the value for the RepositoryAccessMode field, and the private Docker registry where the model image is hosted requires authentication.
The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function that provides credentials to authenticate to the private Docker registry where your model image is hosted. For information about how to create an Amazon Web Services Lambda function, see Create a Lambda function with the console in the Amazon Web Services Lambda Developer Guide .
Whether the container hosts a single model or multiple models.
The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). The S3 path is required for Amazon SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters .
Note
The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.
If you provide a value for this parameter, Amazon SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provide. Amazon Web Services STS is activated in your IAM user account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide .
Warning
If you use a built-in algorithm to create a model, Amazon SageMaker requires that you provide a S3 path to the model artifacts in ModelDataUrl .
The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
The name or Amazon Resource Name (ARN) of the model package to use to create the model.
Specifies additional configuration for multi-model endpoints.
Whether to cache models for a multi-model endpoint. By default, multi-model endpoints cache models so that a model does not have to be loaded into memory each time it is invoked. Some use cases do not benefit from model caching. For example, if an endpoint hosts a large number of models that are each invoked infrequently, the endpoint might perform better if you disable model caching. To disable model caching, set the value of this parameter to Disabled .
The containers in the inference pipeline.
Describes the container, as part of model definition.
This parameter is ignored for models that contain only a PrimaryContainer .
When a ContainerDefinition is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline . If you don't specify a value for this parameter for a ContainerDefinition that is part of an inference pipeline, a unique name is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value for the ContainerHostName for any ContainerDefinition that is part of an inference pipeline, you must specify a value for the ContainerHostName parameter of every ContainerDefinition in that pipeline.
The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. If you are using your own custom algorithm instead of an algorithm provided by Amazon SageMaker, the inference code must meet Amazon SageMaker requirements. Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker
Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers
Set this to one of the following values:
(Optional) Specifies an authentication configuration for the private docker registry where your model image is hosted. Specify a value for this property only if you specified Vpc as the value for the RepositoryAccessMode field, and the private Docker registry where the model image is hosted requires authentication.
The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function that provides credentials to authenticate to the private Docker registry where your model image is hosted. For information about how to create an Amazon Web Services Lambda function, see Create a Lambda function with the console in the Amazon Web Services Lambda Developer Guide .
Whether the container hosts a single model or multiple models.
The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). The S3 path is required for Amazon SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters .
Note
The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.
If you provide a value for this parameter, Amazon SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provide. Amazon Web Services STS is activated in your IAM user account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide .
Warning
If you use a built-in algorithm to create a model, Amazon SageMaker requires that you provide a S3 path to the model artifacts in ModelDataUrl .
The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
The name or Amazon Resource Name (ARN) of the model package to use to create the model.
Specifies additional configuration for multi-model endpoints.
Whether to cache models for a multi-model endpoint. By default, multi-model endpoints cache models so that a model does not have to be loaded into memory each time it is invoked. Some use cases do not benefit from model caching. For example, if an endpoint hosts a large number of models that are each invoked infrequently, the endpoint might perform better if you disable model caching. To disable model caching, set the value of this parameter to Disabled .
Specifies details of how containers in a multi-container endpoint are called.
How containers in a multi-container are run. The following values are valid.
The Amazon Resource Name (ARN) of the IAM role that you specified for the model.
A VpcConfig object that specifies the VPC that this model has access to. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
A timestamp that shows when the model was created.
The Amazon Resource Name (ARN) of the model.
If True , no inbound or outbound network calls can be made to or from the model container.
Returns a description of a model bias job definition.
See also: AWS API Documentation
Request Syntax
response = client.describe_model_bias_job_definition(
JobDefinitionName='string'
)
[REQUIRED]
The name of the model bias job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
{
'JobDefinitionArn': 'string',
'JobDefinitionName': 'string',
'CreationTime': datetime(2015, 1, 1),
'ModelBiasBaselineConfig': {
'BaseliningJobName': 'string',
'ConstraintsResource': {
'S3Uri': 'string'
}
},
'ModelBiasAppSpecification': {
'ImageUri': 'string',
'ConfigUri': 'string',
'Environment': {
'string': 'string'
}
},
'ModelBiasJobInput': {
'EndpointInput': {
'EndpointName': 'string',
'LocalPath': 'string',
'S3InputMode': 'Pipe'|'File',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'FeaturesAttribute': 'string',
'InferenceAttribute': 'string',
'ProbabilityAttribute': 'string',
'ProbabilityThresholdAttribute': 123.0,
'StartTimeOffset': 'string',
'EndTimeOffset': 'string'
},
'GroundTruthS3Input': {
'S3Uri': 'string'
}
},
'ModelBiasJobOutputConfig': {
'MonitoringOutputs': [
{
'S3Output': {
'S3Uri': 'string',
'LocalPath': 'string',
'S3UploadMode': 'Continuous'|'EndOfJob'
}
},
],
'KmsKeyId': 'string'
},
'JobResources': {
'ClusterConfig': {
'InstanceCount': 123,
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
}
},
'NetworkConfig': {
'EnableInterContainerTrafficEncryption': True|False,
'EnableNetworkIsolation': True|False,
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
},
'RoleArn': 'string',
'StoppingCondition': {
'MaxRuntimeInSeconds': 123
}
}
Response Structure
The Amazon Resource Name (ARN) of the model bias job.
The name of the bias job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
The time at which the model bias job was created.
The baseline configuration for a model bias job.
The name of the baseline model bias job.
The constraints resource for a monitoring job.
The Amazon S3 URI for the constraints resource.
Configures the model bias job to run a specified Docker container image.
The container image to be run by the model bias job.
JSON formatted S3 file that defines bias parameters. For more information on this JSON configuration file, see Configure bias parameters .
Sets the environment variables in the Docker container.
Inputs for the model bias job.
Input object for the endpoint
An endpoint in customer's account which has enabled DataCaptureConfig enabled.
Path to the filesystem where the endpoint data is available to the container.
Whether the Pipe or File is used as the input mode for transfering data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .
Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated
The attributes of the input data that are the input features.
The attribute of the input data that represents the ground truth label.
In a classification problem, the attribute that represents the class probability.
The threshold for the class probability to be evaluated as a positive result.
If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
Location of ground truth labels to use in model bias job.
The address of the Amazon S3 location of the ground truth labels.
The output configuration for monitoring jobs.
Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.
The output object for a monitoring job.
The Amazon S3 storage location where the results of a monitoring job are saved.
A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.
The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.
Whether to upload the results of the monitoring job continuously or after the job completes.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.
Identifies the resources to deploy for a monitoring job.
The configuration for the cluster resources used to run the processing job.
The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
The ML compute instance type for the processing job.
The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job.
Networking options for a model bias job.
Whether to encrypt all communications between the instances used for the monitoring jobs. Choose True to encrypt communications. Encryption provides greater security for distributed jobs, but the processing might take longer.
Whether to allow inbound and outbound network calls to and from the containers used for the monitoring job.
Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud .
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
The Amazon Resource Name (ARN) of the Amazon Web Services Identity and Access Management (IAM) role that has read permission to the input data location and write permission to the output data location in Amazon S3.
A time limit for how long the monitoring job is allowed to run before stopping.
The maximum runtime allowed in seconds.
Note
The MaxRuntimeInSeconds cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.
Exceptions
Returns a description of a model explainability job definition.
See also: AWS API Documentation
Request Syntax
response = client.describe_model_explainability_job_definition(
JobDefinitionName='string'
)
[REQUIRED]
The name of the model explainability job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
{
'JobDefinitionArn': 'string',
'JobDefinitionName': 'string',
'CreationTime': datetime(2015, 1, 1),
'ModelExplainabilityBaselineConfig': {
'BaseliningJobName': 'string',
'ConstraintsResource': {
'S3Uri': 'string'
}
},
'ModelExplainabilityAppSpecification': {
'ImageUri': 'string',
'ConfigUri': 'string',
'Environment': {
'string': 'string'
}
},
'ModelExplainabilityJobInput': {
'EndpointInput': {
'EndpointName': 'string',
'LocalPath': 'string',
'S3InputMode': 'Pipe'|'File',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'FeaturesAttribute': 'string',
'InferenceAttribute': 'string',
'ProbabilityAttribute': 'string',
'ProbabilityThresholdAttribute': 123.0,
'StartTimeOffset': 'string',
'EndTimeOffset': 'string'
}
},
'ModelExplainabilityJobOutputConfig': {
'MonitoringOutputs': [
{
'S3Output': {
'S3Uri': 'string',
'LocalPath': 'string',
'S3UploadMode': 'Continuous'|'EndOfJob'
}
},
],
'KmsKeyId': 'string'
},
'JobResources': {
'ClusterConfig': {
'InstanceCount': 123,
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
}
},
'NetworkConfig': {
'EnableInterContainerTrafficEncryption': True|False,
'EnableNetworkIsolation': True|False,
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
},
'RoleArn': 'string',
'StoppingCondition': {
'MaxRuntimeInSeconds': 123
}
}
Response Structure
The Amazon Resource Name (ARN) of the model explainability job.
The name of the explainability job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
The time at which the model explainability job was created.
The baseline configuration for a model explainability job.
The name of the baseline model explainability job.
The constraints resource for a monitoring job.
The Amazon S3 URI for the constraints resource.
Configures the model explainability job to run a specified Docker container image.
The container image to be run by the model explainability job.
JSON formatted S3 file that defines explainability parameters. For more information on this JSON configuration file, see Configure model explainability parameters .
Sets the environment variables in the Docker container.
Inputs for the model explainability job.
Input object for the endpoint
An endpoint in customer's account which has enabled DataCaptureConfig enabled.
Path to the filesystem where the endpoint data is available to the container.
Whether the Pipe or File is used as the input mode for transfering data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .
Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated
The attributes of the input data that are the input features.
The attribute of the input data that represents the ground truth label.
In a classification problem, the attribute that represents the class probability.
The threshold for the class probability to be evaluated as a positive result.
If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
The output configuration for monitoring jobs.
Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.
The output object for a monitoring job.
The Amazon S3 storage location where the results of a monitoring job are saved.
A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.
The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.
Whether to upload the results of the monitoring job continuously or after the job completes.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.
Identifies the resources to deploy for a monitoring job.
The configuration for the cluster resources used to run the processing job.
The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
The ML compute instance type for the processing job.
The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job.
Networking options for a model explainability job.
Whether to encrypt all communications between the instances used for the monitoring jobs. Choose True to encrypt communications. Encryption provides greater security for distributed jobs, but the processing might take longer.
Whether to allow inbound and outbound network calls to and from the containers used for the monitoring job.
Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud .
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
The Amazon Resource Name (ARN) of the Amazon Web Services Identity and Access Management (IAM) role that has read permission to the input data location and write permission to the output data location in Amazon S3.
A time limit for how long the monitoring job is allowed to run before stopping.
The maximum runtime allowed in seconds.
Note
The MaxRuntimeInSeconds cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.
Exceptions
Returns a description of the specified model package, which is used to create Amazon SageMaker models or list them on Amazon Web Services Marketplace.
To create models in Amazon SageMaker, buyers can subscribe to model packages listed on Amazon Web Services Marketplace.
See also: AWS API Documentation
Request Syntax
response = client.describe_model_package(
ModelPackageName='string'
)
[REQUIRED]
The name or Amazon Resource Name (ARN) of the model package to describe.
When you specify a name, the name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
{
'ModelPackageName': 'string',
'ModelPackageGroupName': 'string',
'ModelPackageVersion': 123,
'ModelPackageArn': 'string',
'ModelPackageDescription': 'string',
'CreationTime': datetime(2015, 1, 1),
'InferenceSpecification': {
'Containers': [
{
'ContainerHostname': 'string',
'Image': 'string',
'ImageDigest': 'string',
'ModelDataUrl': 'string',
'ProductId': 'string',
'Environment': {
'string': 'string'
}
},
],
'SupportedTransformInstanceTypes': [
'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
],
'SupportedRealtimeInferenceInstanceTypes': [
'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge',
],
'SupportedContentTypes': [
'string',
],
'SupportedResponseMIMETypes': [
'string',
]
},
'SourceAlgorithmSpecification': {
'SourceAlgorithms': [
{
'ModelDataUrl': 'string',
'AlgorithmName': 'string'
},
]
},
'ValidationSpecification': {
'ValidationRole': 'string',
'ValidationProfiles': [
{
'ProfileName': 'string',
'TransformJobDefinition': {
'MaxConcurrentTransforms': 123,
'MaxPayloadInMB': 123,
'BatchStrategy': 'MultiRecord'|'SingleRecord',
'Environment': {
'string': 'string'
},
'TransformInput': {
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'SplitType': 'None'|'Line'|'RecordIO'|'TFRecord'
},
'TransformOutput': {
'S3OutputPath': 'string',
'Accept': 'string',
'AssembleWith': 'None'|'Line',
'KmsKeyId': 'string'
},
'TransformResources': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'InstanceCount': 123,
'VolumeKmsKeyId': 'string'
}
}
},
]
},
'ModelPackageStatus': 'Pending'|'InProgress'|'Completed'|'Failed'|'Deleting',
'ModelPackageStatusDetails': {
'ValidationStatuses': [
{
'Name': 'string',
'Status': 'NotStarted'|'InProgress'|'Completed'|'Failed',
'FailureReason': 'string'
},
],
'ImageScanStatuses': [
{
'Name': 'string',
'Status': 'NotStarted'|'InProgress'|'Completed'|'Failed',
'FailureReason': 'string'
},
]
},
'CertifyForMarketplace': True|False,
'ModelApprovalStatus': 'Approved'|'Rejected'|'PendingManualApproval',
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'MetadataProperties': {
'CommitId': 'string',
'Repository': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string'
},
'ModelMetrics': {
'ModelQuality': {
'Statistics': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'Constraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'ModelDataQuality': {
'Statistics': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'Constraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'Bias': {
'Report': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'Explainability': {
'Report': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
}
},
'LastModifiedTime': datetime(2015, 1, 1),
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'ApprovalDescription': 'string'
}
Response Structure
The name of the model package being described.
If the model is a versioned model, the name of the model group that the versioned model belongs to.
The version of the model package.
The Amazon Resource Name (ARN) of the model package.
A brief summary of the model package.
A timestamp specifying when the model package was created.
Details about inference jobs that can be run with models based on this model package.
The Amazon ECR registry path of the Docker image that contains the inference code.
Describes the Docker container for the model package.
The DNS host name for the Docker container.
The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by Amazon SageMaker, the inference code must meet Amazon SageMaker requirements. Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
An MD5 hash of the training algorithm that identifies the Docker image used for training.
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
Note
The model artifacts must be in an S3 bucket that is in the same region as the model package.
The Amazon Web Services Marketplace product ID of the model package.
The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
This parameter is required for unversioned models, and optional for versioned models.
A list of the instance types that are used to generate inferences in real-time.
This parameter is required for unversioned models, and optional for versioned models.
The supported MIME types for the input data.
The supported MIME types for the output data.
Details about the algorithm that was used to create the model package.
A list of the algorithms that were used to create a model package.
Specifies an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your Amazon SageMaker account or an algorithm in Amazon Web Services Marketplace that you are subscribed to.
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
Note
The model artifacts must be in an S3 bucket that is in the same region as the algorithm.
The name of an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your Amazon SageMaker account or an algorithm in Amazon Web Services Marketplace that you are subscribed to.
Configurations for one or more transform jobs that Amazon SageMaker runs to test the model package.
The IAM roles to be used for the validation of the model package.
An array of ModelPackageValidationProfile objects, each of which specifies a batch transform job that Amazon SageMaker runs to validate your model package.
Contains data, such as the inputs and targeted instance types that are used in the process of validating the model package.
The data provided in the validation profile is made available to your buyers on Amazon Web Services Marketplace.
The name of the profile for the model package.
The TransformJobDefinition object that describes the transform job used for the validation of the model package.
The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1.
The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata).
A string that determines the number of records included in a single mini-batch.
SingleRecord means only one record is used per mini-batch. MultiRecord means a mini-batch is set to contain as many records that can fit within the MaxPayloadInMB limit.
The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
A description of the input source and the way the transform job consumes it.
Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
The S3 location of the data source that is associated with a channel.
If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.
The following values are compatible: ManifestFile , S3Prefix
The following value is not compatible: AugmentedManifestFile
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None .
The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None , which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:
When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord , Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord , Amazon SageMaker sends individual records in each request.
Note
Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord . Padding is not removed if the value of BatchStrategy is set to MultiRecord .
For more information about RecordIO , see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord , see Consuming TFRecord data in the TensorFlow documentation.
Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, s3://bucket-name/key-name-prefix .
For every S3 object used as input for the transform job, batch transform stores the transformed data with an .``out`` suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at s3://bucket-name/input-name-prefix/dataset01/data.csv , batch transform stores the transformed data at s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out . Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .``out`` file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.
The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify None . To add a newline character at the end of every transformed record, specify Line .
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
Identifies the ML compute instances for the transform job.
The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or ml.m5.large instance types.
The number of ML compute instances to use in the transform job. For distributed transform jobs, specify a value greater than 1. The default value is 1 .
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be any of the following formats:
The current status of the model package.
Details about the current status of the model package.
The validation status of the model package.
Represents the overall status of a model package.
The name of the model package for which the overall status is being reported.
The current status.
if the overall status is Failed , the reason for the failure.
The status of the scan of the Docker image container for the model package.
Represents the overall status of a model package.
The name of the model package for which the overall status is being reported.
The current status.
if the overall status is Failed , the reason for the failure.
Whether the model package is certified for listing on Amazon Web Services Marketplace.
The approval status of the model package.
Information about the user who created or modified an experiment, trial, or trial component.
The Amazon Resource Name (ARN) of the user's profile.
The name of the user's profile.
The domain associated with the user.
Metadata properties of the tracking entity, trial, or trial component.
The commit ID.
The repository.
The entity this entity was generated by.
The project ID.
Metrics for the model.
Metrics that measure the quality of a model.
Model quality statistics.
Model quality constraints.
Metrics that measure the quality of the input data for a model.
Data quality statistics for a model.
Data quality constraints for a model.
Metrics that measure bais in a model.
The bias report for a model
Metrics that help explain a model.
The explainability report for a model.
The last time the model package was modified.
Information about the user who created or modified an experiment, trial, or trial component.
The Amazon Resource Name (ARN) of the user's profile.
The name of the user's profile.
The domain associated with the user.
A description provided for the model approval.
Gets a description for the specified model group.
See also: AWS API Documentation
Request Syntax
response = client.describe_model_package_group(
ModelPackageGroupName='string'
)
[REQUIRED]
The name of the model group to describe.
{
'ModelPackageGroupName': 'string',
'ModelPackageGroupArn': 'string',
'ModelPackageGroupDescription': 'string',
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'ModelPackageGroupStatus': 'Pending'|'InProgress'|'Completed'|'Failed'|'Deleting'|'DeleteFailed'
}
Response Structure
The name of the model group.
The Amazon Resource Name (ARN) of the model group.
A description of the model group.
The time that the model group was created.
Information about the user who created or modified an experiment, trial, or trial component.
The Amazon Resource Name (ARN) of the user's profile.
The name of the user's profile.
The domain associated with the user.
The status of the model group.
Returns a description of a model quality job definition.
See also: AWS API Documentation
Request Syntax
response = client.describe_model_quality_job_definition(
JobDefinitionName='string'
)
[REQUIRED]
The name of the model quality job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
{
'JobDefinitionArn': 'string',
'JobDefinitionName': 'string',
'CreationTime': datetime(2015, 1, 1),
'ModelQualityBaselineConfig': {
'BaseliningJobName': 'string',
'ConstraintsResource': {
'S3Uri': 'string'
}
},
'ModelQualityAppSpecification': {
'ImageUri': 'string',
'ContainerEntrypoint': [
'string',
],
'ContainerArguments': [
'string',
],
'RecordPreprocessorSourceUri': 'string',
'PostAnalyticsProcessorSourceUri': 'string',
'ProblemType': 'BinaryClassification'|'MulticlassClassification'|'Regression',
'Environment': {
'string': 'string'
}
},
'ModelQualityJobInput': {
'EndpointInput': {
'EndpointName': 'string',
'LocalPath': 'string',
'S3InputMode': 'Pipe'|'File',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'FeaturesAttribute': 'string',
'InferenceAttribute': 'string',
'ProbabilityAttribute': 'string',
'ProbabilityThresholdAttribute': 123.0,
'StartTimeOffset': 'string',
'EndTimeOffset': 'string'
},
'GroundTruthS3Input': {
'S3Uri': 'string'
}
},
'ModelQualityJobOutputConfig': {
'MonitoringOutputs': [
{
'S3Output': {
'S3Uri': 'string',
'LocalPath': 'string',
'S3UploadMode': 'Continuous'|'EndOfJob'
}
},
],
'KmsKeyId': 'string'
},
'JobResources': {
'ClusterConfig': {
'InstanceCount': 123,
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
}
},
'NetworkConfig': {
'EnableInterContainerTrafficEncryption': True|False,
'EnableNetworkIsolation': True|False,
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
},
'RoleArn': 'string',
'StoppingCondition': {
'MaxRuntimeInSeconds': 123
}
}
Response Structure
The Amazon Resource Name (ARN) of the model quality job.
The name of the quality job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
The time at which the model quality job was created.
The baseline configuration for a model quality job.
The name of the job that performs baselining for the monitoring job.
The constraints resource for a monitoring job.
The Amazon S3 URI for the constraints resource.
Configures the model quality job to run a specified Docker container image.
The address of the container image that the monitoring job runs.
Specifies the entrypoint for a container that the monitoring job runs.
An array of arguments for the container used to run the monitoring job.
An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flatted json so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.
An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.
The machine learning problem type of the model that the monitoring job monitors.
Sets the environment variables in the container that the monitoring job runs.
Inputs for the model quality job.
Input object for the endpoint
An endpoint in customer's account which has enabled DataCaptureConfig enabled.
Path to the filesystem where the endpoint data is available to the container.
Whether the Pipe or File is used as the input mode for transfering data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .
Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated
The attributes of the input data that are the input features.
The attribute of the input data that represents the ground truth label.
In a classification problem, the attribute that represents the class probability.
The threshold for the class probability to be evaluated as a positive result.
If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
The ground truth label provided for the model.
The address of the Amazon S3 location of the ground truth labels.
The output configuration for monitoring jobs.
Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.
The output object for a monitoring job.
The Amazon S3 storage location where the results of a monitoring job are saved.
A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.
The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.
Whether to upload the results of the monitoring job continuously or after the job completes.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.
Identifies the resources to deploy for a monitoring job.
The configuration for the cluster resources used to run the processing job.
The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
The ML compute instance type for the processing job.
The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job.
Networking options for a model quality job.
Whether to encrypt all communications between the instances used for the monitoring jobs. Choose True to encrypt communications. Encryption provides greater security for distributed jobs, but the processing might take longer.
Whether to allow inbound and outbound network calls to and from the containers used for the monitoring job.
Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud .
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
A time limit for how long the monitoring job is allowed to run before stopping.
The maximum runtime allowed in seconds.
Note
The MaxRuntimeInSeconds cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.
Exceptions
Describes the schedule for a monitoring job.
See also: AWS API Documentation
Request Syntax
response = client.describe_monitoring_schedule(
MonitoringScheduleName='string'
)
[REQUIRED]
Name of a previously created monitoring schedule.
{
'MonitoringScheduleArn': 'string',
'MonitoringScheduleName': 'string',
'MonitoringScheduleStatus': 'Pending'|'Failed'|'Scheduled'|'Stopped',
'MonitoringType': 'DataQuality'|'ModelQuality'|'ModelBias'|'ModelExplainability',
'FailureReason': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'MonitoringScheduleConfig': {
'ScheduleConfig': {
'ScheduleExpression': 'string'
},
'MonitoringJobDefinition': {
'BaselineConfig': {
'BaseliningJobName': 'string',
'ConstraintsResource': {
'S3Uri': 'string'
},
'StatisticsResource': {
'S3Uri': 'string'
}
},
'MonitoringInputs': [
{
'EndpointInput': {
'EndpointName': 'string',
'LocalPath': 'string',
'S3InputMode': 'Pipe'|'File',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'FeaturesAttribute': 'string',
'InferenceAttribute': 'string',
'ProbabilityAttribute': 'string',
'ProbabilityThresholdAttribute': 123.0,
'StartTimeOffset': 'string',
'EndTimeOffset': 'string'
}
},
],
'MonitoringOutputConfig': {
'MonitoringOutputs': [
{
'S3Output': {
'S3Uri': 'string',
'LocalPath': 'string',
'S3UploadMode': 'Continuous'|'EndOfJob'
}
},
],
'KmsKeyId': 'string'
},
'MonitoringResources': {
'ClusterConfig': {
'InstanceCount': 123,
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
}
},
'MonitoringAppSpecification': {
'ImageUri': 'string',
'ContainerEntrypoint': [
'string',
],
'ContainerArguments': [
'string',
],
'RecordPreprocessorSourceUri': 'string',
'PostAnalyticsProcessorSourceUri': 'string'
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123
},
'Environment': {
'string': 'string'
},
'NetworkConfig': {
'EnableInterContainerTrafficEncryption': True|False,
'EnableNetworkIsolation': True|False,
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
},
'RoleArn': 'string'
},
'MonitoringJobDefinitionName': 'string',
'MonitoringType': 'DataQuality'|'ModelQuality'|'ModelBias'|'ModelExplainability'
},
'EndpointName': 'string',
'LastMonitoringExecutionSummary': {
'MonitoringScheduleName': 'string',
'ScheduledTime': datetime(2015, 1, 1),
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'MonitoringExecutionStatus': 'Pending'|'Completed'|'CompletedWithViolations'|'InProgress'|'Failed'|'Stopping'|'Stopped',
'ProcessingJobArn': 'string',
'EndpointName': 'string',
'FailureReason': 'string',
'MonitoringJobDefinitionName': 'string',
'MonitoringType': 'DataQuality'|'ModelQuality'|'ModelBias'|'ModelExplainability'
}
}
Response Structure
The Amazon Resource Name (ARN) of the monitoring schedule.
Name of the monitoring schedule.
The status of an monitoring job.
The type of the monitoring job that this schedule runs. This is one of the following values.
A string, up to one KB in size, that contains the reason a monitoring job failed, if it failed.
The time at which the monitoring job was created.
The time at which the monitoring job was last modified.
The configuration object that specifies the monitoring schedule and defines the monitoring job.
Configures the monitoring schedule.
A cron expression that describes details about the monitoring schedule.
Currently the only supported cron expressions are:
For example, the following are valid cron expressions:
To support running every 6, 12 hours, the following are also supported:
cron(0 [00-23]/[01-24] ? * * *)
For example, the following are valid cron expressions:
Note
Defines the monitoring job.
Baseline configuration used to validate that the data conforms to the specified constraints and statistics
The name of the job that performs baselining for the monitoring job.
The baseline constraint file in Amazon S3 that the current monitoring job should validated against.
The Amazon S3 URI for the constraints resource.
The baseline statistics file in Amazon S3 that the current monitoring job should be validated against.
The Amazon S3 URI for the statistics resource.
The array of inputs for the monitoring job. Currently we support monitoring an Amazon SageMaker Endpoint.
The inputs for a monitoring job.
The endpoint for a monitoring job.
An endpoint in customer's account which has enabled DataCaptureConfig enabled.
Path to the filesystem where the endpoint data is available to the container.
Whether the Pipe or File is used as the input mode for transfering data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .
Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated
The attributes of the input data that are the input features.
The attribute of the input data that represents the ground truth label.
In a classification problem, the attribute that represents the class probability.
The threshold for the class probability to be evaluated as a positive result.
If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
The array of outputs from the monitoring job to be uploaded to Amazon Simple Storage Service (Amazon S3).
Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.
The output object for a monitoring job.
The Amazon S3 storage location where the results of a monitoring job are saved.
A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.
The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.
Whether to upload the results of the monitoring job continuously or after the job completes.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.
Identifies the resources, ML compute instances, and ML storage volumes to deploy for a monitoring job. In distributed processing, you specify more than one instance.
The configuration for the cluster resources used to run the processing job.
The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
The ML compute instance type for the processing job.
The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job.
Configures the monitoring job to run a specified Docker container image.
The container image to be run by the monitoring job.
Specifies the entrypoint for a container used to run the monitoring job.
An array of arguments for the container used to run the monitoring job.
An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flatted json so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.
An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.
Specifies a time limit for how long the monitoring job is allowed to run.
The maximum runtime allowed in seconds.
Note
The MaxRuntimeInSeconds cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.
Sets the environment variables in the Docker container.
Specifies networking options for an monitoring job.
Whether to encrypt all communications between distributed processing jobs. Choose True to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer.
Whether to allow inbound and outbound network calls to and from the containers used for the processing job.
Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud .
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
The name of the monitoring job definition to schedule.
The type of the monitoring job definition to schedule.
The name of the endpoint for the monitoring job.
Describes metadata on the last execution to run, if there was one.
The name of the monitoring schedule.
The time the monitoring job was scheduled.
The time at which the monitoring job was created.
A timestamp that indicates the last time the monitoring job was modified.
The status of the monitoring job.
The Amazon Resource Name (ARN) of the monitoring job.
The name of the endpoint used to run the monitoring job.
Contains the reason a monitoring job failed, if it failed.
The name of the monitoring job.
The type of the monitoring job.
Exceptions
Returns information about a notebook instance.
See also: AWS API Documentation
Request Syntax
response = client.describe_notebook_instance(
NotebookInstanceName='string'
)
[REQUIRED]
The name of the notebook instance that you want information about.
{
'NotebookInstanceArn': 'string',
'NotebookInstanceName': 'string',
'NotebookInstanceStatus': 'Pending'|'InService'|'Stopping'|'Stopped'|'Failed'|'Deleting'|'Updating',
'FailureReason': 'string',
'Url': 'string',
'InstanceType': 'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge',
'SubnetId': 'string',
'SecurityGroups': [
'string',
],
'RoleArn': 'string',
'KmsKeyId': 'string',
'NetworkInterfaceId': 'string',
'LastModifiedTime': datetime(2015, 1, 1),
'CreationTime': datetime(2015, 1, 1),
'NotebookInstanceLifecycleConfigName': 'string',
'DirectInternetAccess': 'Enabled'|'Disabled',
'VolumeSizeInGB': 123,
'AcceleratorTypes': [
'ml.eia1.medium'|'ml.eia1.large'|'ml.eia1.xlarge'|'ml.eia2.medium'|'ml.eia2.large'|'ml.eia2.xlarge',
],
'DefaultCodeRepository': 'string',
'AdditionalCodeRepositories': [
'string',
],
'RootAccess': 'Enabled'|'Disabled'
}
Response Structure
The Amazon Resource Name (ARN) of the notebook instance.
The name of the Amazon SageMaker notebook instance.
The status of the notebook instance.
If status is Failed , the reason it failed.
The URL that you use to connect to the Jupyter notebook that is running in your notebook instance.
The type of ML compute instance running on the notebook instance.
The ID of the VPC subnet.
The IDs of the VPC security groups.
The Amazon Resource Name (ARN) of the IAM role associated with the instance.
The Amazon Web Services KMS key ID Amazon SageMaker uses to encrypt data when storing it on the ML storage volume attached to the instance.
The network interface IDs that Amazon SageMaker created at the time of creating the instance.
A timestamp. Use this parameter to retrieve the time when the notebook instance was last modified.
A timestamp. Use this parameter to return the time when the notebook instance was created
Returns the name of a notebook instance lifecycle configuration.
For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance
Describes whether Amazon SageMaker provides internet access to the notebook instance. If this value is set to Disabled , the notebook instance does not have internet access, and cannot connect to Amazon SageMaker training and endpoint services.
For more information, see Notebook Instances Are Internet-Enabled by Default .
The size, in GB, of the ML storage volume attached to the notebook instance.
A list of the Elastic Inference (EI) instance types associated with this notebook instance. Currently only one EI instance type can be associated with a notebook instance. For more information, see Using Elastic Inference in Amazon SageMaker .
The Git repository associated with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in Amazon Web Services CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances .
An array of up to three Git repositories associated with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in Amazon Web Services CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances .
Whether root access is enabled or disabled for users of the notebook instance.
Note
Lifecycle configurations need root access to be able to set up a notebook instance. Because of this, lifecycle configurations associated with a notebook instance always run with root access even if you disable root access for users.
Returns a description of a notebook instance lifecycle configuration.
For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance .
See also: AWS API Documentation
Request Syntax
response = client.describe_notebook_instance_lifecycle_config(
NotebookInstanceLifecycleConfigName='string'
)
[REQUIRED]
The name of the lifecycle configuration to describe.
{
'NotebookInstanceLifecycleConfigArn': 'string',
'NotebookInstanceLifecycleConfigName': 'string',
'OnCreate': [
{
'Content': 'string'
},
],
'OnStart': [
{
'Content': 'string'
},
],
'LastModifiedTime': datetime(2015, 1, 1),
'CreationTime': datetime(2015, 1, 1)
}
Response Structure
The Amazon Resource Name (ARN) of the lifecycle configuration.
The name of the lifecycle configuration.
The shell script that runs only once, when you create a notebook instance.
Contains the notebook instance lifecycle configuration script.
Each lifecycle configuration script has a limit of 16384 characters.
The value of the $PATH environment variable that is available to both scripts is /sbin:bin:/usr/sbin:/usr/bin .
View CloudWatch Logs for notebook instance lifecycle configurations in log group /aws/sagemaker/NotebookInstances in log stream [notebook-instance-name]/[LifecycleConfigHook] .
Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or started.
For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance .
A base64-encoded string that contains a shell script for a notebook instance lifecycle configuration.
The shell script that runs every time you start a notebook instance, including when you create the notebook instance.
Contains the notebook instance lifecycle configuration script.
Each lifecycle configuration script has a limit of 16384 characters.
The value of the $PATH environment variable that is available to both scripts is /sbin:bin:/usr/sbin:/usr/bin .
View CloudWatch Logs for notebook instance lifecycle configurations in log group /aws/sagemaker/NotebookInstances in log stream [notebook-instance-name]/[LifecycleConfigHook] .
Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or started.
For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance .
A base64-encoded string that contains a shell script for a notebook instance lifecycle configuration.
A timestamp that tells when the lifecycle configuration was last modified.
A timestamp that tells when the lifecycle configuration was created.
Describes the details of a pipeline.
See also: AWS API Documentation
Request Syntax
response = client.describe_pipeline(
PipelineName='string'
)
[REQUIRED]
The name of the pipeline to describe.
{
'PipelineArn': 'string',
'PipelineName': 'string',
'PipelineDisplayName': 'string',
'PipelineDefinition': 'string',
'PipelineDescription': 'string',
'RoleArn': 'string',
'PipelineStatus': 'Active',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'LastRunTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
}
}
Response Structure
The Amazon Resource Name (ARN) of the pipeline.
The name of the pipeline.
The display name of the pipeline.
The JSON pipeline definition.
The description of the pipeline.
The Amazon Resource Name (ARN) that the pipeline uses to execute.
The status of the pipeline execution.
The time when the pipeline was created.
The time when the pipeline was last modified.
The time when the pipeline was last run.
Information about the user who created or modified an experiment, trial, or trial component.
The Amazon Resource Name (ARN) of the user's profile.
The name of the user's profile.
The domain associated with the user.
Information about the user who created or modified an experiment, trial, or trial component.
The Amazon Resource Name (ARN) of the user's profile.
The name of the user's profile.
The domain associated with the user.
Exceptions
Describes the details of an execution's pipeline definition.
See also: AWS API Documentation
Request Syntax
response = client.describe_pipeline_definition_for_execution(
PipelineExecutionArn='string'
)
[REQUIRED]
The Amazon Resource Name (ARN) of the pipeline execution.
{
'PipelineDefinition': 'string',
'CreationTime': datetime(2015, 1, 1)
}
Response Structure
The JSON pipeline definition.
The time when the pipeline was created.
Exceptions
Describes the details of a pipeline execution.
See also: AWS API Documentation
Request Syntax
response = client.describe_pipeline_execution(
PipelineExecutionArn='string'
)
[REQUIRED]
The Amazon Resource Name (ARN) of the pipeline execution.
{
'PipelineArn': 'string',
'PipelineExecutionArn': 'string',
'PipelineExecutionDisplayName': 'string',
'PipelineExecutionStatus': 'Executing'|'Stopping'|'Stopped'|'Failed'|'Succeeded',
'PipelineExecutionDescription': 'string',
'PipelineExperimentConfig': {
'ExperimentName': 'string',
'TrialName': 'string'
},
'FailureReason': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
}
}
Response Structure
The Amazon Resource Name (ARN) of the pipeline.
The Amazon Resource Name (ARN) of the pipeline execution.
The display name of the pipeline execution.
The status of the pipeline execution.
The description of the pipeline execution.
Specifies the names of the experiment and trial created by a pipeline.
The name of the experiment.
The name of the trial.
If the execution failed, a message describing why.
The time when the pipeline execution was created.
The time when the pipeline execution was modified last.
Information about the user who created or modified an experiment, trial, or trial component.
The Amazon Resource Name (ARN) of the user's profile.
The name of the user's profile.
The domain associated with the user.
Information about the user who created or modified an experiment, trial, or trial component.
The Amazon Resource Name (ARN) of the user's profile.
The name of the user's profile.
The domain associated with the user.
Exceptions
Returns a description of a processing job.
See also: AWS API Documentation
Request Syntax
response = client.describe_processing_job(
ProcessingJobName='string'
)
[REQUIRED]
The name of the processing job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
{
'ProcessingInputs': [
{
'InputName': 'string',
'AppManaged': True|False,
'S3Input': {
'S3Uri': 'string',
'LocalPath': 'string',
'S3DataType': 'ManifestFile'|'S3Prefix',
'S3InputMode': 'Pipe'|'File',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'S3CompressionType': 'None'|'Gzip'
},
'DatasetDefinition': {
'AthenaDatasetDefinition': {
'Catalog': 'string',
'Database': 'string',
'QueryString': 'string',
'WorkGroup': 'string',
'OutputS3Uri': 'string',
'KmsKeyId': 'string',
'OutputFormat': 'PARQUET'|'ORC'|'AVRO'|'JSON'|'TEXTFILE',
'OutputCompression': 'GZIP'|'SNAPPY'|'ZLIB'
},
'RedshiftDatasetDefinition': {
'ClusterId': 'string',
'Database': 'string',
'DbUser': 'string',
'QueryString': 'string',
'ClusterRoleArn': 'string',
'OutputS3Uri': 'string',
'KmsKeyId': 'string',
'OutputFormat': 'PARQUET'|'CSV',
'OutputCompression': 'None'|'GZIP'|'BZIP2'|'ZSTD'|'SNAPPY'
},
'LocalPath': 'string',
'DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'InputMode': 'Pipe'|'File'
}
},
],
'ProcessingOutputConfig': {
'Outputs': [
{
'OutputName': 'string',
'S3Output': {
'S3Uri': 'string',
'LocalPath': 'string',
'S3UploadMode': 'Continuous'|'EndOfJob'
},
'FeatureStoreOutput': {
'FeatureGroupName': 'string'
},
'AppManaged': True|False
},
],
'KmsKeyId': 'string'
},
'ProcessingJobName': 'string',
'ProcessingResources': {
'ClusterConfig': {
'InstanceCount': 123,
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
}
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123
},
'AppSpecification': {
'ImageUri': 'string',
'ContainerEntrypoint': [
'string',
],
'ContainerArguments': [
'string',
]
},
'Environment': {
'string': 'string'
},
'NetworkConfig': {
'EnableInterContainerTrafficEncryption': True|False,
'EnableNetworkIsolation': True|False,
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
},
'RoleArn': 'string',
'ExperimentConfig': {
'ExperimentName': 'string',
'TrialName': 'string',
'TrialComponentDisplayName': 'string'
},
'ProcessingJobArn': 'string',
'ProcessingJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'ExitMessage': 'string',
'FailureReason': 'string',
'ProcessingEndTime': datetime(2015, 1, 1),
'ProcessingStartTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'CreationTime': datetime(2015, 1, 1),
'MonitoringScheduleArn': 'string',
'AutoMLJobArn': 'string',
'TrainingJobArn': 'string'
}
Response Structure
The inputs for a processing job.
The inputs for a processing job. The processing input must specify exactly one of either S3Input or DatasetDefinition types.
The name for the processing job input.
When True , input operations such as data download are managed natively by the processing job application. When False (default), input operations are managed by Amazon SageMaker.
Configuration for downloading input data from Amazon S3 into the processing container.
The URI of the Amazon S3 prefix Amazon SageMaker downloads data required to run a processing job.
The local path in your container where you want Amazon SageMaker to write input data to. LocalPath is an absolute path to the input data and must begin with /opt/ml/processing/ . LocalPath is a required parameter when AppManaged is False (default).
Whether you use an S3Prefix or a ManifestFile for the data type. If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for the processing job. If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for the processing job.
Whether to use File or Pipe input mode. In File mode, Amazon SageMaker copies the data from the input source onto the local ML storage volume before starting your processing container. This is the most commonly used input mode. In Pipe mode, Amazon SageMaker streams input data from the source directly to your processing container into named pipes without using the ML storage volume.
Whether to distribute the data from Amazon S3 to all processing instances with FullyReplicated , or whether the data from Amazon S3 is shared by Amazon S3 key, downloading one shard of data to each processing instance.
Whether to GZIP-decompress the data in Amazon S3 as it is streamed into the processing container. Gzip can only be used when Pipe mode is specified as the S3InputMode . In Pipe mode, Amazon SageMaker streams input data from the source directly to your container without using the EBS volume.
Configuration for a Dataset Definition input.
Configuration for Athena Dataset Definition input.
The name of the data catalog used in Athena query execution.
The name of the database used in the Athena query execution.
The SQL query statements, to be executed.
The name of the workgroup in which the Athena query is being started.
The location in Amazon S3 where Athena query results are stored.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data generated from an Athena query execution.
The data storage format for Athena query results.
The compression used for Athena query results.
Configuration for Redshift Dataset Definition input.
The Redshift cluster Identifier.
The name of the Redshift database used in Redshift query execution.
The database user name used in Redshift query execution.
The SQL query statements to be executed.
The IAM role attached to your Redshift cluster that Amazon SageMaker uses to generate datasets.
The location in Amazon S3 where the Redshift query results are stored.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data from a Redshift execution.
The data storage format for Redshift query results.
The compression used for Redshift query results.
The local path where you want Amazon SageMaker to download the Dataset Definition inputs to run a processing job. LocalPath is an absolute path to the input data. This is a required parameter when AppManaged is False (default).
Whether the generated dataset is FullyReplicated or ShardedByS3Key (default).
Whether to use File or Pipe input mode. In File (default) mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode. In Pipe mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume.
Output configuration for the processing job.
An array of outputs configuring the data to upload from the processing container.
Describes the results of a processing job. The processing output must specify exactly one of either S3Output or FeatureStoreOutput types.
The name for the processing job output.
Configuration for processing job outputs in Amazon S3.
A URI that identifies the Amazon S3 bucket where you want Amazon SageMaker to save the results of a processing job.
The local path of a directory where you want Amazon SageMaker to upload its contents to Amazon S3. LocalPath is an absolute path to a directory containing output files. This directory will be created by the platform and exist when your container's entrypoint is invoked.
Whether to upload the results of the processing job continuously or after the job completes.
Configuration for processing job outputs in Amazon SageMaker Feature Store. This processing output type is only supported when AppManaged is specified.
The name of the Amazon SageMaker FeatureGroup to use as the destination for processing job output. Note that your processing script is responsible for putting records into your Feature Store.
When True , output operations such as data upload are managed natively by the processing job application. When False (default), output operations are managed by Amazon SageMaker.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the processing job output. KmsKeyId can be an ID of a KMS key, ARN of a KMS key, alias of a KMS key, or alias of a KMS key. The KmsKeyId is applied to all outputs.
The name of the processing job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job. In distributed training, you specify more than one instance.
The configuration for the resources in a cluster used to run the processing job.
The number of ML compute instances to use in the processing job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
The ML compute instance type for the processing job.
The size of the ML storage volume in gigabytes that you want to provision. You must specify sufficient ML storage for your scenario.
Note
Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for processing, Amazon SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can't request a VolumeSizeInGB greater than the total size of the local instance storage.
For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes .
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the processing job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The time limit for how long the processing job is allowed to run.
Specifies the maximum runtime in seconds.
Configures the processing job to run a specified container image.
The container image to be run by the processing job.
The entrypoint for a container used to run a processing job.
The arguments for a container used to run a processing job.
The environment variables set in the Docker container.
Networking options for a processing job.
Whether to encrypt all communications between distributed processing jobs. Choose True to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer.
Whether to allow inbound and outbound network calls to and from the containers used for the processing job.
Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud .
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
The configuration information used to create an experiment.
The name of an existing experiment to associate the trial component with.
The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
The display name for the trial component. If this key isn't specified, the display name is the trial component name.
The Amazon Resource Name (ARN) of the processing job.
Provides the status of a processing job.
An optional string, up to one KB in size, that contains metadata from the processing container when the processing job exits.
A string, up to one KB in size, that contains the reason a processing job failed, if it failed.
The time at which the processing job completed.
The time at which the processing job started.
The time at which the processing job was last modified.
The time at which the processing job was created.
The ARN of a monitoring schedule for an endpoint associated with this processing job.
The ARN of an AutoML job associated with this processing job.
The ARN of a training job associated with this processing job.
Exceptions
Describes the details of a project.
See also: AWS API Documentation
Request Syntax
response = client.describe_project(
ProjectName='string'
)
[REQUIRED]
The name of the project to describe.
{
'ProjectArn': 'string',
'ProjectName': 'string',
'ProjectId': 'string',
'ProjectDescription': 'string',
'ServiceCatalogProvisioningDetails': {
'ProductId': 'string',
'ProvisioningArtifactId': 'string',
'PathId': 'string',
'ProvisioningParameters': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'ServiceCatalogProvisionedProductDetails': {
'ProvisionedProductId': 'string',
'ProvisionedProductStatusMessage': 'string'
},
'ProjectStatus': 'Pending'|'CreateInProgress'|'CreateCompleted'|'CreateFailed'|'DeleteInProgress'|'DeleteFailed'|'DeleteCompleted',
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'CreationTime': datetime(2015, 1, 1)
}
Response Structure
The Amazon Resource Name (ARN) of the project.
The name of the project.
The ID of the project.
The description of the project.
Information used to provision a service catalog product. For information, see What is Amazon Web Services Service Catalog .
The ID of the product to provision.
The ID of the provisioning artifact.
The path identifier of the product. This value is optional if the product has a default path, and required if the product has more than one path.
A list of key value pairs that you specify when you provision a product.
A key value pair used when you provision a project as a service catalog product. For information, see What is Amazon Web Services Service Catalog .
The key that identifies a provisioning parameter.
The value of the provisioning parameter.
Information about a provisioned service catalog product.
The ID of the provisioned product.
The current status of the product.
The status of the project.
Information about the user who created or modified an experiment, trial, or trial component.
The Amazon Resource Name (ARN) of the user's profile.
The name of the user's profile.
The domain associated with the user.
The time when the project was created.
Gets information about a work team provided by a vendor. It returns details about the subscription with a vendor in the Amazon Web Services Marketplace.
See also: AWS API Documentation
Request Syntax
response = client.describe_subscribed_workteam(
WorkteamArn='string'
)
[REQUIRED]
The Amazon Resource Name (ARN) of the subscribed work team to describe.
{
'SubscribedWorkteam': {
'WorkteamArn': 'string',
'MarketplaceTitle': 'string',
'SellerName': 'string',
'MarketplaceDescription': 'string',
'ListingId': 'string'
}
}
Response Structure
A Workteam instance that contains information about the work team.
The Amazon Resource Name (ARN) of the vendor that you have subscribed.
The title of the service provided by the vendor in the Amazon Marketplace.
The name of the vendor in the Amazon Marketplace.
The description of the vendor from the Amazon Marketplace.
Marketplace product listing ID.
Returns information about a training job.
Some of the attributes below only appear if the training job successfully starts. If the training job fails, TrainingJobStatus is Failed and, depending on the FailureReason , attributes like TrainingStartTime , TrainingTimeInSeconds , TrainingEndTime , and BillableTimeInSeconds may not be present in the response.
See also: AWS API Documentation
Request Syntax
response = client.describe_training_job(
TrainingJobName='string'
)
[REQUIRED]
The name of the training job.
{
'TrainingJobName': 'string',
'TrainingJobArn': 'string',
'TuningJobArn': 'string',
'LabelingJobArn': 'string',
'AutoMLJobArn': 'string',
'ModelArtifacts': {
'S3ModelArtifacts': 'string'
},
'TrainingJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'SecondaryStatus': 'Starting'|'LaunchingMLInstances'|'PreparingTrainingStack'|'Downloading'|'DownloadingTrainingImage'|'Training'|'Uploading'|'Stopping'|'Stopped'|'MaxRuntimeExceeded'|'Completed'|'Failed'|'Interrupted'|'MaxWaitTimeExceeded'|'Updating'|'Restarting',
'FailureReason': 'string',
'HyperParameters': {
'string': 'string'
},
'AlgorithmSpecification': {
'TrainingImage': 'string',
'AlgorithmName': 'string',
'TrainingInputMode': 'Pipe'|'File',
'MetricDefinitions': [
{
'Name': 'string',
'Regex': 'string'
},
],
'EnableSageMakerMetricsTimeSeries': True|False
},
'RoleArn': 'string',
'InputDataConfig': [
{
'ChannelName': 'string',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'AttributeNames': [
'string',
]
},
'FileSystemDataSource': {
'FileSystemId': 'string',
'FileSystemAccessMode': 'rw'|'ro',
'FileSystemType': 'EFS'|'FSxLustre',
'DirectoryPath': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File',
'ShuffleConfig': {
'Seed': 123
}
},
],
'OutputDataConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string'
},
'ResourceConfig': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
},
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123
},
'CreationTime': datetime(2015, 1, 1),
'TrainingStartTime': datetime(2015, 1, 1),
'TrainingEndTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'SecondaryStatusTransitions': [
{
'Status': 'Starting'|'LaunchingMLInstances'|'PreparingTrainingStack'|'Downloading'|'DownloadingTrainingImage'|'Training'|'Uploading'|'Stopping'|'Stopped'|'MaxRuntimeExceeded'|'Completed'|'Failed'|'Interrupted'|'MaxWaitTimeExceeded'|'Updating'|'Restarting',
'StartTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1),
'StatusMessage': 'string'
},
],
'FinalMetricDataList': [
{
'MetricName': 'string',
'Value': ...,
'Timestamp': datetime(2015, 1, 1)
},
],
'EnableNetworkIsolation': True|False,
'EnableInterContainerTrafficEncryption': True|False,
'EnableManagedSpotTraining': True|False,
'CheckpointConfig': {
'S3Uri': 'string',
'LocalPath': 'string'
},
'TrainingTimeInSeconds': 123,
'BillableTimeInSeconds': 123,
'DebugHookConfig': {
'LocalPath': 'string',
'S3OutputPath': 'string',
'HookParameters': {
'string': 'string'
},
'CollectionConfigurations': [
{
'CollectionName': 'string',
'CollectionParameters': {
'string': 'string'
}
},
]
},
'ExperimentConfig': {
'ExperimentName': 'string',
'TrialName': 'string',
'TrialComponentDisplayName': 'string'
},
'DebugRuleConfigurations': [
{
'RuleConfigurationName': 'string',
'LocalPath': 'string',
'S3OutputPath': 'string',
'RuleEvaluatorImage': 'string',
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'RuleParameters': {
'string': 'string'
}
},
],
'TensorBoardOutputConfig': {
'LocalPath': 'string',
'S3OutputPath': 'string'
},
'DebugRuleEvaluationStatuses': [
{
'RuleConfigurationName': 'string',
'RuleEvaluationJobArn': 'string',
'RuleEvaluationStatus': 'InProgress'|'NoIssuesFound'|'IssuesFound'|'Error'|'Stopping'|'Stopped',
'StatusDetails': 'string',
'LastModifiedTime': datetime(2015, 1, 1)
},
],
'ProfilerConfig': {
'S3OutputPath': 'string',
'ProfilingIntervalInMilliseconds': 123,
'ProfilingParameters': {
'string': 'string'
}
},
'ProfilerRuleConfigurations': [
{
'RuleConfigurationName': 'string',
'LocalPath': 'string',
'S3OutputPath': 'string',
'RuleEvaluatorImage': 'string',
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'RuleParameters': {
'string': 'string'
}
},
],
'ProfilerRuleEvaluationStatuses': [
{
'RuleConfigurationName': 'string',
'RuleEvaluationJobArn': 'string',
'RuleEvaluationStatus': 'InProgress'|'NoIssuesFound'|'IssuesFound'|'Error'|'Stopping'|'Stopped',
'StatusDetails': 'string',
'LastModifiedTime': datetime(2015, 1, 1)
},
],
'ProfilingStatus': 'Enabled'|'Disabled',
'RetryStrategy': {
'MaximumRetryAttempts': 123
},
'Environment': {
'string': 'string'
}
}
Response Structure
Name of the model training job.
The Amazon Resource Name (ARN) of the training job.
The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.
The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the transform or training job.
The Amazon Resource Name (ARN) of an AutoML job.
Information about the Amazon S3 location that is configured for storing model artifacts.
The path of the S3 object that contains the model artifacts. For example, s3://bucket-name/keynameprefix/model.tar.gz .
The status of the training job.
Amazon SageMaker provides the following training job statuses:
For more detailed information, see SecondaryStatus .
Provides detailed information about the state of the training job. For detailed information on the secondary status of the training job, see StatusMessage under SecondaryStatusTransition .
Amazon SageMaker provides primary statuses and secondary statuses that apply to each of them:
InProgress
Completed
Failed
Stopped
Stopping
Warning
Valid values for SecondaryStatus are subject to change.
We no longer support the following secondary statuses:
If the training job failed, the reason it failed.
Algorithm-specific parameters.
Information about the algorithm used for training, and algorithm metadata.
The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters . Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
The name of the algorithm resource to use for the training job. This must be an algorithm resource that you created or subscribe to on Amazon Web Services Marketplace. If you specify a value for this parameter, you can't specify a value for TrainingImage .
The input mode that the algorithm supports. For the input modes that Amazon SageMaker algorithms support, see Algorithms . If an algorithm supports the File input mode, Amazon SageMaker downloads the training data from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an algorithm supports the Pipe input mode, Amazon SageMaker streams data directly from S3 to the container.
In File mode, make sure you provision ML storage volume with sufficient capacity to accommodate the data download from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container use ML storage volume to also store intermediate information, if any.
For distributed algorithms using File mode, training data is distributed uniformly, and your training duration is predictable if the input data objects size is approximately same. Amazon SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed where one host in a training cluster is overloaded, thus becoming bottleneck in training.
A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. Amazon SageMaker publishes each metric to Amazon CloudWatch.
Specifies a metric that the training algorithm writes to stderr or stdout . Amazon SageMakerhyperparameter tuning captures all defined metrics. You specify one metric that a hyperparameter tuning job uses as its objective metric to choose the best training job.
The name of the metric.
A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining Objective Metrics .
To generate and save time-series metrics during training, set to true . The default is false and time-series metrics aren't generated except in the following cases:
The Amazon Web Services Identity and Access Management (IAM) role configured for the training job.
An array of Channel objects that describes each data input channel.
A channel is a named input source that training algorithms can consume.
The name of the channel.
The location of the channel data.
The S3 location of the data source that is associated with a channel.
If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects that match the specified key name prefix for model training.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for model training.
If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
If you want Amazon SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .
If you want Amazon SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.
A list of one or more attribute names to use that are found in a specified augmented manifest file.
The file system that is associated with a channel.
The file system id.
The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.
The file system type.
The full path to the directory to associate with the channel.
The MIME type of the data.
If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, Amazon SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO .
In File mode, leave this field unset or set it to None.
(Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , Amazon SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
To use a model for incremental training, choose File input model.
A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
Determines the shuffling order in ShuffleConfig value.
The S3 path where model artifacts that you configured when creating the job are stored. Amazon SageMaker creates subfolders for model artifacts.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
If you use a KMS key ID or an alias of your master key, the Amazon SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. Amazon SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig . If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
Identifies the S3 path where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
Resources, including ML compute instances and ML storage volumes, that are configured for model training.
The ML compute instance type.
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.
You must specify sufficient ML storage for your scenario.
Note
Amazon SageMaker supports only the General Purpose SSD (gp2) ML storage volume type.
Note
Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for training, Amazon SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can't request a VolumeSizeInGB greater than the total size of the local instance storage.
For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes .
The Amazon Web Services KMS key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be in any of the following formats:
A VpcConfig object that specifies the VPC that this training job has access to. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud .
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.
The maximum length of time, in seconds, that a training or compilation job can run. If the job does not complete during this time, Amazon SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
The default value is 1 day. The maximum value is 28 days.
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, Amazon SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
A timestamp that indicates when the training job was created.
Indicates the time when the training job starts on training instances. You are billed for the time interval between this time and the value of TrainingEndTime . The start time in CloudWatch Logs might be later than this time. The difference is due to the time it takes to download the training data and to the size of the training container.
Indicates the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when Amazon SageMaker detects a job failure.
A timestamp that indicates when the status of the training job was last modified.
A history of all of the secondary statuses that the training job has transitioned through.
An array element of DescribeTrainingJobResponse$SecondaryStatusTransitions . It provides additional details about a status that the training job has transitioned through. A training job can be in one of several states, for example, starting, downloading, training, or uploading. Within each state, there are a number of intermediate states. For example, within the starting state, Amazon SageMaker could be starting the training job or launching the ML instances. These transitional states are referred to as the job's secondary status.
Contains a secondary status information from a training job.
Status might be one of the following secondary statuses:
InProgress
Completed
Failed
Stopped
Stopping
We no longer support the following secondary statuses:
A timestamp that shows when the training job transitioned to the current secondary status state.
A timestamp that shows when the training job transitioned out of this secondary status state into another secondary status state or when the training job has ended.
A detailed description of the progress within a secondary status.
Amazon SageMaker provides secondary statuses and status messages that apply to each of them:
Starting
Training
Warning
Status messages are subject to change. Therefore, we recommend not including them in code that programmatically initiates actions. For examples, don't use status messages in if statements.
To have an overview of your training job's progress, view TrainingJobStatus and SecondaryStatus in DescribeTrainingJob , and StatusMessage together. For example, at the start of a training job, you might see the following:
A collection of MetricData objects that specify the names, values, and dates and times that the training algorithm emitted to Amazon CloudWatch.
The name, value, and date and time of a metric that was emitted to Amazon CloudWatch.
The name of the metric.
The value of the metric.
The date and time that the algorithm emitted the metric.
If you want to allow inbound or outbound network calls, except for calls between peers within a training cluster for distributed training, choose True . If you enable network isolation for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
To encrypt all communications between ML compute instances in distributed training, choose True . Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithms in distributed training.
A Boolean indicating whether managed spot training is enabled (True ) or not (False ).
Contains information about the output location for managed spot training checkpoint data.
Identifies the S3 path where you want Amazon SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix .
(Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/ .
The training time in seconds.
The billable time in seconds. Billable time refers to the absolute wall-clock time.
Multiply BillableTimeInSeconds by the number of instances (InstanceCount ) in your training cluster to get the total compute time Amazon SageMaker will bill you if you run distributed training. The formula is as follows: BillableTimeInSeconds * InstanceCount .
You can calculate the savings from using managed spot training using the formula (1 - BillableTimeInSeconds / TrainingTimeInSeconds) * 100 . For example, if BillableTimeInSeconds is 100 and TrainingTimeInSeconds is 500, the savings is 80%.
Configuration information for the Debugger hook parameters, metric and tensor collections, and storage paths. To learn more about how to configure the DebugHookConfig parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
Path to local storage location for metrics and tensors. Defaults to /opt/ml/output/tensors/ .
Path to Amazon S3 storage location for metrics and tensors.
Configuration information for the Debugger hook parameters.
Configuration information for Debugger tensor collections. To learn more about how to configure the CollectionConfiguration parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
Configuration information for the Debugger output tensor collections.
The name of the tensor collection. The name must be unique relative to other rule configuration names.
Parameter values for the tensor collection. The allowed parameters are "name" , "include_regex" , "reduction_config" , "save_config" , "tensor_names" , and "save_histogram" .
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
The name of an existing experiment to associate the trial component with.
The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
The display name for the trial component. If this key isn't specified, the display name is the trial component name.
Configuration information for Debugger rules for debugging output tensors.
Configuration information for SageMaker Debugger rules for debugging. To learn more about how to configure the DebugRuleConfiguration parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
The name of the rule configuration. It must be unique relative to other rule configuration names.
Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/ .
Path to Amazon S3 storage location for rules.
The Amazon Elastic Container (ECR) Image for the managed rule evaluation.
The instance type to deploy a Debugger custom rule for debugging a training job.
The size, in GB, of the ML storage volume attached to the processing instance.
Runtime configuration for rule container.
Configuration of storage locations for the Debugger TensorBoard output data.
Path to local storage location for tensorBoard output. Defaults to /opt/ml/output/tensorboard .
Path to Amazon S3 storage location for TensorBoard output.
Evaluation status of Debugger rules for debugging on a training job.
Information about the status of the rule evaluation.
The name of the rule configuration.
The Amazon Resource Name (ARN) of the rule evaluation job.
Status of the rule evaluation.
Details from the rule evaluation.
Timestamp when the rule evaluation status was last modified.
Configuration information for Debugger system monitoring, framework profiling, and storage paths.
Path to Amazon S3 storage location for system and framework metrics.
A time interval for capturing system metrics in milliseconds. Available values are 100, 200, 500, 1000 (1 second), 5000 (5 seconds), and 60000 (1 minute) milliseconds. The default value is 500 milliseconds.
Configuration information for capturing framework metrics. Available key strings for different profiling options are DetailedProfilingConfig , PythonProfilingConfig , and DataLoaderProfilingConfig . The following codes are configuration structures for the ProfilingParameters parameter. To learn more about how to configure the ProfilingParameters parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
Configuration information for Debugger rules for profiling system and framework metrics.
Configuration information for profiling rules.
The name of the rule configuration. It must be unique relative to other rule configuration names.
Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/ .
Path to Amazon S3 storage location for rules.
The Amazon Elastic Container (ECR) Image for the managed rule evaluation.
The instance type to deploy a Debugger custom rule for profiling a training job.
The size, in GB, of the ML storage volume attached to the processing instance.
Runtime configuration for rule container.
Evaluation status of Debugger rules for profiling on a training job.
Information about the status of the rule evaluation.
The name of the rule configuration.
The Amazon Resource Name (ARN) of the rule evaluation job.
Status of the rule evaluation.
Details from the rule evaluation.
Timestamp when the rule evaluation status was last modified.
Profiling status of a training job.
The number of times to retry the job when the job fails due to an InternalServerError .
The number of times to retry the job. When the job is retried, it's SecondaryStatus is changed to STARTING .
The environment variables to set in the Docker container.
Exceptions
Returns information about a transform job.
See also: AWS API Documentation
Request Syntax
response = client.describe_transform_job(
TransformJobName='string'
)
[REQUIRED]
The name of the transform job that you want to view details of.
{
'TransformJobName': 'string',
'TransformJobArn': 'string',
'TransformJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'FailureReason': 'string',
'ModelName': 'string',
'MaxConcurrentTransforms': 123,
'ModelClientConfig': {
'InvocationsTimeoutInSeconds': 123,
'InvocationsMaxRetries': 123
},
'MaxPayloadInMB': 123,
'BatchStrategy': 'MultiRecord'|'SingleRecord',
'Environment': {
'string': 'string'
},
'TransformInput': {
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'SplitType': 'None'|'Line'|'RecordIO'|'TFRecord'
},
'TransformOutput': {
'S3OutputPath': 'string',
'Accept': 'string',
'AssembleWith': 'None'|'Line',
'KmsKeyId': 'string'
},
'TransformResources': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'InstanceCount': 123,
'VolumeKmsKeyId': 'string'
},
'CreationTime': datetime(2015, 1, 1),
'TransformStartTime': datetime(2015, 1, 1),
'TransformEndTime': datetime(2015, 1, 1),
'LabelingJobArn': 'string',
'AutoMLJobArn': 'string',
'DataProcessing': {
'InputFilter': 'string',
'OutputFilter': 'string',
'JoinSource': 'Input'|'None'
},
'ExperimentConfig': {
'ExperimentName': 'string',
'TrialName': 'string',
'TrialComponentDisplayName': 'string'
}
}
Response Structure
The name of the transform job.
The Amazon Resource Name (ARN) of the transform job.
The status of the transform job. If the transform job failed, the reason is returned in the FailureReason field.
If the transform job failed, FailureReason describes why it failed. A transform job creates a log file, which includes error messages, and stores it as an Amazon S3 object. For more information, see Log Amazon SageMaker Events with Amazon CloudWatch .
The name of the model used in the transform job.
The maximum number of parallel requests on each instance node that can be launched in a transform job. The default value is 1.
The timeout and maximum number of retries for processing a transform job invocation.
The timeout value in seconds for an invocation request.
The maximum number of retries when invocation requests are failing.
The maximum payload size, in MB, used in the transform job.
Specifies the number of records to include in a mini-batch for an HTTP inference request. A record is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record.
To enable the batch strategy, you must set SplitType to Line , RecordIO , or TFRecord .
The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
Describes the dataset to be transformed and the Amazon S3 location where it is stored.
Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
The S3 location of the data source that is associated with a channel.
If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.
The following values are compatible: ManifestFile , S3Prefix
The following value is not compatible: AugmentedManifestFile
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None .
The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None , which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:
When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord , Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord , Amazon SageMaker sends individual records in each request.
Note
Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord . Padding is not removed if the value of BatchStrategy is set to MultiRecord .
For more information about RecordIO , see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord , see Consuming TFRecord data in the TensorFlow documentation.
Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, s3://bucket-name/key-name-prefix .
For every S3 object used as input for the transform job, batch transform stores the transformed data with an .``out`` suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at s3://bucket-name/input-name-prefix/dataset01/data.csv , batch transform stores the transformed data at s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out . Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .``out`` file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.
The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify None . To add a newline character at the end of every transformed record, specify Line .
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
Describes the resources, including ML instance types and ML instance count, to use for the transform job.
The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or ml.m5.large instance types.
The number of ML compute instances to use in the transform job. For distributed transform jobs, specify a value greater than 1. The default value is 1 .
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be any of the following formats:
A timestamp that shows when the transform Job was created.
Indicates when the transform job starts on ML instances. You are billed for the time interval between this time and the value of TransformEndTime .
Indicates when the transform job has been completed, or has stopped or failed. You are billed for the time interval between this time and the value of TransformStartTime .
The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the transform or training job.
The Amazon Resource Name (ARN) of the AutoML transform job.
The data structure used to specify the data to be used for inference in a batch transform job and to associate the data that is relevant to the prediction results in the output. The input filter provided allows you to exclude input data that is not needed for inference in a batch transform job. The output filter provided allows you to include input data relevant to interpreting the predictions in the output from the job. For more information, see Associate Prediction Results with their Corresponding Input Records .
A JSONPath expression used to select a portion of the input data to pass to the algorithm. Use the InputFilter parameter to exclude fields, such as an ID column, from the input. If you want Amazon SageMaker to pass the entire input dataset to the algorithm, accept the default value $ .
Examples: "$" , "$[1:]" , "$.features"
A JSONPath expression used to select a portion of the joined dataset to save in the output file for a batch transform job. If you want Amazon SageMaker to store the entire input dataset in the output file, leave the default value, $ . If you specify indexes that aren't within the dimension size of the joined dataset, you get an error.
Examples: "$" , "$[0,5:]" , "$['id','SageMakerOutput']"
Specifies the source of the data to join with the transformed data. The valid values are None and Input . The default value is None , which specifies not to join the input with the transformed data. If you want the batch transform job to join the original input data with the transformed data, set JoinSource to Input . You can specify OutputFilter as an additional filter to select a portion of the joined dataset and store it in the output file.
For JSON or JSONLines objects, such as a JSON array, Amazon SageMaker adds the transformed data to the input JSON object in an attribute called SageMakerOutput . The joined result for JSON must be a key-value pair object. If the input is not a key-value pair object, Amazon SageMaker creates a new JSON file. In the new JSON file, and the input data is stored under the SageMakerInput key and the results are stored in SageMakerOutput .
For CSV data, Amazon SageMaker takes each row as a JSON array and joins the transformed data with the input by appending each transformed row to the end of the input. The joined data has the original input data followed by the transformed data and the output is a CSV file.
For information on how joining in applied, see Workflow for Associating Inferences with Input Records .
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
The name of an existing experiment to associate the trial component with.
The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
The display name for the trial component. If this key isn't specified, the display name is the trial component name.
Exceptions
Provides a list of a trial's properties.
See also: AWS API Documentation
Request Syntax
response = client.describe_trial(
TrialName='string'
)
[REQUIRED]
The name of the trial to describe.
{
'TrialName': 'string',
'TrialArn': 'string',
'DisplayName': 'string',
'ExperimentName': 'string',
'Source': {
'SourceArn': 'string',
'SourceType': 'string'
},
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'LastModifiedTime': datetime(2015, 1, 1),
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'MetadataProperties': {
'CommitId': 'string',
'Repository': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string'
}
}
Response Structure
The name of the trial.
The Amazon Resource Name (ARN) of the trial.
The name of the trial as displayed. If DisplayName isn't specified, TrialName is displayed.
The name of the experiment the trial is part of.
The Amazon Resource Name (ARN) of the source and, optionally, the job type.
The Amazon Resource Name (ARN) of the source.
The source job type.
When the trial was created.
Who created the trial.
The Amazon Resource Name (ARN) of the user's profile.
The name of the user's profile.
The domain associated with the user.
When the trial was last modified.
Who last modified the trial.
The Amazon Resource Name (ARN) of the user's profile.
The name of the user's profile.
The domain associated with the user.
Metadata properties of the tracking entity, trial, or trial component.
The commit ID.
The repository.
The entity this entity was generated by.
The project ID.
Exceptions
Provides a list of a trials component's properties.
See also: AWS API Documentation
Request Syntax
response = client.describe_trial_component(
TrialComponentName='string'
)
[REQUIRED]
The name of the trial component to describe.
{
'TrialComponentName': 'string',
'TrialComponentArn': 'string',
'DisplayName': 'string',
'Source': {
'SourceArn': 'string',
'SourceType': 'string'
},
'Status': {
'PrimaryStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'Message': 'string'
},
'StartTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1),
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'LastModifiedTime': datetime(2015, 1, 1),
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'Parameters': {
'string': {
'StringValue': 'string',
'NumberValue': 123.0
}
},
'InputArtifacts': {
'string': {
'MediaType': 'string',
'Value': 'string'
}
},
'OutputArtifacts': {
'string': {
'MediaType': 'string',
'Value': 'string'
}
},
'MetadataProperties': {
'CommitId': 'string',
'Repository': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string'
},
'Metrics': [
{
'MetricName': 'string',
'SourceArn': 'string',
'TimeStamp': datetime(2015, 1, 1),
'Max': 123.0,
'Min': 123.0,
'Last': 123.0,
'Count': 123,
'Avg': 123.0,
'StdDev': 123.0
},
]
}
Response Structure
The name of the trial component.
The Amazon Resource Name (ARN) of the trial component.
The name of the component as displayed. If DisplayName isn't specified, TrialComponentName is displayed.
The Amazon Resource Name (ARN) of the source and, optionally, the job type.
The source ARN.
The source job type.
The status of the component. States include:
The status of the trial component.
If the component failed, a message describing why.
When the component started.
When the component ended.
When the component was created.
Who created the component.
The Amazon Resource Name (ARN) of the user's profile.
The name of the user's profile.
The domain associated with the user.
When the component was last modified.
Who last modified the component.
The Amazon Resource Name (ARN) of the user's profile.
The name of the user's profile.
The domain associated with the user.
The hyperparameters of the component.
The value of a hyperparameter. Only one of NumberValue or StringValue can be specified.
This object is specified in the CreateTrialComponent request.
The string value of a categorical hyperparameter. If you specify a value for this parameter, you can't specify the NumberValue parameter.
The numeric value of a numeric hyperparameter. If you specify a value for this parameter, you can't specify the StringValue parameter.
The input artifacts of the component.
Represents an input or output artifact of a trial component. You specify TrialComponentArtifact as part of the InputArtifacts and OutputArtifacts parameters in the CreateTrialComponent request.
Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and instance types. Examples of output artifacts are metrics, snapshots, logs, and images.
The media type of the artifact, which indicates the type of data in the artifact file. The media type consists of a type and a subtype concatenated with a slash (/) character, for example, text/csv, image/jpeg, and s3/uri. The type specifies the category of the media. The subtype specifies the kind of data.
The location of the artifact.
The output artifacts of the component.
Represents an input or output artifact of a trial component. You specify TrialComponentArtifact as part of the InputArtifacts and OutputArtifacts parameters in the CreateTrialComponent request.
Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and instance types. Examples of output artifacts are metrics, snapshots, logs, and images.
The media type of the artifact, which indicates the type of data in the artifact file. The media type consists of a type and a subtype concatenated with a slash (/) character, for example, text/csv, image/jpeg, and s3/uri. The type specifies the category of the media. The subtype specifies the kind of data.
The location of the artifact.
Metadata properties of the tracking entity, trial, or trial component.
The commit ID.
The repository.
The entity this entity was generated by.
The project ID.
The metrics for the component.
A summary of the metrics of a trial component.
The name of the metric.
The Amazon Resource Name (ARN) of the source.
When the metric was last updated.
The maximum value of the metric.
The minimum value of the metric.
The most recent value of the metric.
The number of samples used to generate the metric.
The average value of the metric.
The standard deviation of the metric.
Exceptions
Describes a user profile. For more information, see CreateUserProfile .
See also: AWS API Documentation
Request Syntax
response = client.describe_user_profile(
DomainId='string',
UserProfileName='string'
)
[REQUIRED]
The domain ID.
[REQUIRED]
The user profile name. This value is not case sensitive.
dict
Response Syntax
{
'DomainId': 'string',
'UserProfileArn': 'string',
'UserProfileName': 'string',
'HomeEfsFileSystemUid': 'string',
'Status': 'Deleting'|'Failed'|'InService'|'Pending'|'Updating'|'Update_Failed'|'Delete_Failed',
'LastModifiedTime': datetime(2015, 1, 1),
'CreationTime': datetime(2015, 1, 1),
'FailureReason': 'string',
'SingleSignOnUserIdentifier': 'string',
'SingleSignOnUserValue': 'string',
'UserSettings': {
'ExecutionRole': 'string',
'SecurityGroups': [
'string',
],
'SharingSettings': {
'NotebookOutputOption': 'Allowed'|'Disabled',
'S3OutputPath': 'string',
'S3KmsKeyId': 'string'
},
'JupyterServerAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'
}
},
'KernelGatewayAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
]
},
'TensorBoardAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'
}
}
}
}
Response Structure
(dict) --
DomainId (string) --
The ID of the domain that contains the profile.
UserProfileArn (string) --
The user profile Amazon Resource Name (ARN).
UserProfileName (string) --
The user profile name.
HomeEfsFileSystemUid (string) --
The ID of the user's profile in the Amazon Elastic File System (EFS) volume.
Status (string) --
The status.
LastModifiedTime (datetime) --
The last modified time.
CreationTime (datetime) --
The creation time.
FailureReason (string) --
The failure reason.
SingleSignOnUserIdentifier (string) --
The SSO user identifier.
SingleSignOnUserValue (string) --
The SSO user value.
UserSettings (dict) --
A collection of settings.
ExecutionRole (string) --
The execution role for the user.
SecurityGroups (list) --
The security groups for the Amazon Virtual Private Cloud (VPC) that Studio uses for communication.
Optional when the CreateDomain.AppNetworkAccessType parameter is set to PublicInternetOnly .
Required when the CreateDomain.AppNetworkAccessType parameter is set to VpcOnly .
Amazon SageMaker adds a security group to allow NFS traffic from SageMaker Studio. Therefore, the number of security groups that you can specify is one less than the maximum number shown.
SharingSettings (dict) --
Specifies options for sharing SageMaker Studio notebooks.
NotebookOutputOption (string) --
Whether to include the notebook cell output when sharing the notebook. The default is Disabled .
S3OutputPath (string) --
When NotebookOutputOption is Allowed , the Amazon S3 bucket used to store the shared notebook snapshots.
S3KmsKeyId (string) --
When NotebookOutputOption is Allowed , the Amazon Web Services Key Management Service (KMS) encryption key ID used to encrypt the notebook cell output in the Amazon S3 bucket.
JupyterServerAppSettings (dict) --
The Jupyter server's app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
InstanceType (string) --
The instance type that the image version runs on.
KernelGatewayAppSettings (dict) --
The kernel gateway app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
InstanceType (string) --
The instance type that the image version runs on.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a KernelGateway app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker image .
ImageName (string) --
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) --
The name of the AppImageConfig.
TensorBoardAppSettings (dict) --
The TensorBoard app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
InstanceType (string) --
The instance type that the image version runs on.
Exceptions
Lists private workforce information, including workforce name, Amazon Resource Name (ARN), and, if applicable, allowed IP address ranges (CIDRs ). Allowable IP address ranges are the IP addresses that workers can use to access tasks.
Warning
This operation applies only to private workforces.
See also: AWS API Documentation
Request Syntax
response = client.describe_workforce(
WorkforceName='string'
)
[REQUIRED]
The name of the private workforce whose access you want to restrict. WorkforceName is automatically set to default when a workforce is created and cannot be modified.
{
'Workforce': {
'WorkforceName': 'string',
'WorkforceArn': 'string',
'LastUpdatedDate': datetime(2015, 1, 1),
'SourceIpConfig': {
'Cidrs': [
'string',
]
},
'SubDomain': 'string',
'CognitoConfig': {
'UserPool': 'string',
'ClientId': 'string'
},
'OidcConfig': {
'ClientId': 'string',
'Issuer': 'string',
'AuthorizationEndpoint': 'string',
'TokenEndpoint': 'string',
'UserInfoEndpoint': 'string',
'LogoutEndpoint': 'string',
'JwksUri': 'string'
},
'CreateDate': datetime(2015, 1, 1)
}
}
Response Structure
A single private workforce, which is automatically created when you create your first private work team. You can create one private work force in each Amazon Web Services Region. By default, any workforce-related API operation used in a specific region will apply to the workforce created in that region. To learn how to create a private workforce, see Create a Private Workforce .
The name of the private workforce.
The Amazon Resource Name (ARN) of the private workforce.
The most recent date that was used to successfully add one or more IP address ranges (CIDRs ) to a private workforce's allow list.
A list of one to ten IP address ranges (CIDRs ) to be added to the workforce allow list. By default, a workforce isn't restricted to specific IP addresses.
A list of one to ten Classless Inter-Domain Routing (CIDR) values.
Maximum: Ten CIDR values
Note
The following Length Constraints apply to individual CIDR values in the CIDR value list.
The subdomain for your OIDC Identity Provider.
The configuration of an Amazon Cognito workforce. A single Cognito workforce is created using and corresponds to a single Amazon Cognito user pool .
A user pool is a user directory in Amazon Cognito. With a user pool, your users can sign in to your web or mobile app through Amazon Cognito. Your users can also sign in through social identity providers like Google, Facebook, Amazon, or Apple, and through SAML identity providers.
The client ID for your Amazon Cognito user pool.
The configuration of an OIDC Identity Provider (IdP) private workforce.
The OIDC IdP client ID used to configure your private workforce.
The OIDC IdP issuer used to configure your private workforce.
The OIDC IdP authorization endpoint used to configure your private workforce.
The OIDC IdP token endpoint used to configure your private workforce.
The OIDC IdP user information endpoint used to configure your private workforce.
The OIDC IdP logout endpoint used to configure your private workforce.
The OIDC IdP JSON Web Key Set (Jwks) URI used to configure your private workforce.
The date that the workforce is created.
Gets information about a specific work team. You can see information such as the create date, the last updated date, membership information, and the work team's Amazon Resource Name (ARN).
See also: AWS API Documentation
Request Syntax
response = client.describe_workteam(
WorkteamName='string'
)
[REQUIRED]
The name of the work team to return a description of.
{
'Workteam': {
'WorkteamName': 'string',
'MemberDefinitions': [
{
'CognitoMemberDefinition': {
'UserPool': 'string',
'UserGroup': 'string',
'ClientId': 'string'
},
'OidcMemberDefinition': {
'Groups': [
'string',
]
}
},
],
'WorkteamArn': 'string',
'WorkforceArn': 'string',
'ProductListingIds': [
'string',
],
'Description': 'string',
'SubDomain': 'string',
'CreateDate': datetime(2015, 1, 1),
'LastUpdatedDate': datetime(2015, 1, 1),
'NotificationConfiguration': {
'NotificationTopicArn': 'string'
}
}
}
Response Structure
A Workteam instance that contains information about the work team.
The name of the work team.
A list of MemberDefinition objects that contains objects that identify the workers that make up the work team.
Workforces can be created using Amazon Cognito or your own OIDC Identity Provider (IdP). For private workforces created using Amazon Cognito use CognitoMemberDefinition . For workforces created using your own OIDC identity provider (IdP) use OidcMemberDefinition .
Defines an Amazon Cognito or your own OIDC IdP user group that is part of a work team.
The Amazon Cognito user group that is part of the work team.
An identifier for a user pool. The user pool must be in the same region as the service that you are calling.
An identifier for a user group.
An identifier for an application client. You must create the app client ID using Amazon Cognito.
A list user groups that exist in your OIDC Identity Provider (IdP). One to ten groups can be used to create a single private work team. When you add a user group to the list of Groups , you can add that user group to one or more private work teams. If you add a user group to a private work team, all workers in that user group are added to the work team.
A list of comma seperated strings that identifies user groups in your OIDC IdP. Each user group is made up of a group of private workers.
The Amazon Resource Name (ARN) that identifies the work team.
The Amazon Resource Name (ARN) of the workforce.
The Amazon Marketplace identifier for a vendor's work team.
A description of the work team.
The URI of the labeling job's user interface. Workers open this URI to start labeling your data objects.
The date and time that the work team was created (timestamp).
The date and time that the work team was last updated (timestamp).
Configures SNS notifications of available or expiring work items for work teams.
The ARN for the SNS topic to which notifications should be published.
Disables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.
See also: AWS API Documentation
Request Syntax
response = client.disable_sagemaker_servicecatalog_portfolio()
{}
Response Structure
Disassociates a trial component from a trial. This doesn't effect other trials the component is associated with. Before you can delete a component, you must disassociate the component from all trials it is associated with. To associate a trial component with a trial, call the AssociateTrialComponent API.
To get a list of the trials a component is associated with, use the Search API. Specify ExperimentTrialComponent for the Resource parameter. The list appears in the response under Results.TrialComponent.Parents .
See also: AWS API Documentation
Request Syntax
response = client.disassociate_trial_component(
TrialComponentName='string',
TrialName='string'
)
[REQUIRED]
The name of the component to disassociate from the trial.
[REQUIRED]
The name of the trial to disassociate from.
dict
Response Syntax
{
'TrialComponentArn': 'string',
'TrialArn': 'string'
}
Response Structure
(dict) --
TrialComponentArn (string) --
The ARN of the trial component.
TrialArn (string) --
The Amazon Resource Name (ARN) of the trial.
Exceptions
Enables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.
See also: AWS API Documentation
Request Syntax
response = client.enable_sagemaker_servicecatalog_portfolio()
{}
Response Structure
Generate a presigned url given a client, its method, and arguments
The presigned url
Describes a fleet.
See also: AWS API Documentation
Request Syntax
response = client.get_device_fleet_report(
DeviceFleetName='string'
)
[REQUIRED]
The name of the fleet.
{
'DeviceFleetArn': 'string',
'DeviceFleetName': 'string',
'OutputConfig': {
'S3OutputLocation': 'string',
'KmsKeyId': 'string',
'PresetDeploymentType': 'GreengrassV2Component',
'PresetDeploymentConfig': 'string'
},
'Description': 'string',
'ReportGenerated': datetime(2015, 1, 1),
'DeviceStats': {
'ConnectedDeviceCount': 123,
'RegisteredDeviceCount': 123
},
'AgentVersions': [
{
'Version': 'string',
'AgentCount': 123
},
],
'ModelStats': [
{
'ModelName': 'string',
'ModelVersion': 'string',
'OfflineDeviceCount': 123,
'ConnectedDeviceCount': 123,
'ActiveDeviceCount': 123,
'SamplingDeviceCount': 123
},
]
}
Response Structure
The Amazon Resource Name (ARN) of the device.
The name of the fleet.
The output configuration for storing sample data collected by the fleet.
The Amazon Simple Storage (S3) bucker URI.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume after compilation job. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account.
The deployment type SageMaker Edge Manager will create. Currently only supports Amazon Web Services IoT Greengrass Version 2 components.
The configuration used to create deployment artifacts. Specify configuration options with a JSON string. The available configuration options for each type are:
Note
Amazon Web Services IoT Greengrass uses semantic versions for components. Semantic versions follow a*major.minor.patch* number system. For example, version 1.0.0 represents the first major release for a component. For more information, see the semantic version specification .
Description of the fleet.
Timestamp of when the report was generated.
Status of devices.
The number of devices connected with a heartbeat.
The number of registered devices.
The versions of Edge Manager agent deployed on the fleet.
Edge Manager agent version.
Version of the agent.
The number of Edge Manager agents.
Status of model on device.
Status of edge devices with this model.
The name of the model.
The model version.
The number of devices that have this model version and do not have a heart beat.
The number of devices that have this model version and have a heart beat.
The number of devices that have this model version, a heart beat, and are currently running.
The number of devices with this model version and are producing sample data.
Gets a resource policy that manages access for a model group. For information about resource policies, see Identity-based policies and resource-based policies in the Amazon Web Services Identity and Access Management User Guide. .
See also: AWS API Documentation
Request Syntax
response = client.get_model_package_group_policy(
ModelPackageGroupName='string'
)
[REQUIRED]
The name of the model group for which to get the resource policy.
{
'ResourcePolicy': 'string'
}
Response Structure
The resource policy for the model group.
Create a paginator for an operation.
Gets the status of Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.
See also: AWS API Documentation
Request Syntax
response = client.get_sagemaker_servicecatalog_portfolio_status()
{
'Status': 'Enabled'|'Disabled'
}
Response Structure
Whether Service Catalog is enabled or disabled in SageMaker.
An auto-complete API for the search functionality in the Amazon SageMaker console. It returns suggestions of possible matches for the property name to use in Search queries. Provides suggestions for HyperParameters , Tags , and Metrics .
See also: AWS API Documentation
Request Syntax
response = client.get_search_suggestions(
Resource='TrainingJob'|'Experiment'|'ExperimentTrial'|'ExperimentTrialComponent'|'Endpoint'|'ModelPackage'|'ModelPackageGroup'|'Pipeline'|'PipelineExecution'|'FeatureGroup',
SuggestionQuery={
'PropertyNameQuery': {
'PropertyNameHint': 'string'
}
}
)
[REQUIRED]
The name of the Amazon SageMaker resource to search for.
Limits the property names that are included in the response.
Defines a property name hint. Only property names that begin with the specified hint are included in the response.
Text that begins a property's name.
dict
Response Syntax
{
'PropertyNameSuggestions': [
{
'PropertyName': 'string'
},
]
}
Response Structure
(dict) --
PropertyNameSuggestions (list) --
A list of property names for a Resource that match a SuggestionQuery .
(dict) --
A property name returned from a GetSearchSuggestions call that specifies a value in the PropertyNameQuery field.
PropertyName (string) --
A suggested property name based on what you entered in the search textbox in the Amazon SageMaker console.
Returns an object that can wait for some condition.
Lists the actions in your account and their properties.
See also: AWS API Documentation
Request Syntax
response = client.list_actions(
SourceUri='string',
ActionType='string',
CreatedAfter=datetime(2015, 1, 1),
CreatedBefore=datetime(2015, 1, 1),
SortBy='Name'|'CreationTime',
SortOrder='Ascending'|'Descending',
NextToken='string',
MaxResults=123
)
dict
Response Syntax
{
'ActionSummaries': [
{
'ActionArn': 'string',
'ActionName': 'string',
'Source': {
'SourceUri': 'string',
'SourceType': 'string',
'SourceId': 'string'
},
'ActionType': 'string',
'Status': 'Unknown'|'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1)
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
ActionSummaries (list) --
A list of actions and their properties.
(dict) --
Lists the properties of an action . An action represents an action or activity. Some examples are a workflow step and a model deployment. Generally, an action involves at least one input artifact or output artifact.
ActionArn (string) --
The Amazon Resource Name (ARN) of the action.
ActionName (string) --
The name of the action.
Source (dict) --
The source of the action.
SourceUri (string) --
The URI of the source.
SourceType (string) --
The type of the source.
SourceId (string) --
The ID of the source.
ActionType (string) --
The type of the action.
Status (string) --
The status of the action.
CreationTime (datetime) --
When the action was created.
LastModifiedTime (datetime) --
When the action was last modified.
NextToken (string) --
A token for getting the next set of actions, if there are any.
Exceptions
Lists the machine learning algorithms that have been created.
See also: AWS API Documentation
Request Syntax
response = client.list_algorithms(
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
MaxResults=123,
NameContains='string',
NextToken='string',
SortBy='Name'|'CreationTime',
SortOrder='Ascending'|'Descending'
)
dict
Response Syntax
{
'AlgorithmSummaryList': [
{
'AlgorithmName': 'string',
'AlgorithmArn': 'string',
'AlgorithmDescription': 'string',
'CreationTime': datetime(2015, 1, 1),
'AlgorithmStatus': 'Pending'|'InProgress'|'Completed'|'Failed'|'Deleting'
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
AlgorithmSummaryList (list) --
>An array of AlgorithmSummary objects, each of which lists an algorithm.
(dict) --
Provides summary information about an algorithm.
AlgorithmName (string) --
The name of the algorithm that is described by the summary.
AlgorithmArn (string) --
The Amazon Resource Name (ARN) of the algorithm.
AlgorithmDescription (string) --
A brief description of the algorithm.
CreationTime (datetime) --
A timestamp that shows when the algorithm was created.
AlgorithmStatus (string) --
The overall status of the algorithm.
NextToken (string) --
If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of algorithms, use it in the subsequent request.
Lists the AppImageConfigs in your account and their properties. The list can be filtered by creation time or modified time, and whether the AppImageConfig name contains a specified string.
See also: AWS API Documentation
Request Syntax
response = client.list_app_image_configs(
MaxResults=123,
NextToken='string',
NameContains='string',
CreationTimeBefore=datetime(2015, 1, 1),
CreationTimeAfter=datetime(2015, 1, 1),
ModifiedTimeBefore=datetime(2015, 1, 1),
ModifiedTimeAfter=datetime(2015, 1, 1),
SortBy='CreationTime'|'LastModifiedTime'|'Name',
SortOrder='Ascending'|'Descending'
)
dict
Response Syntax
{
'NextToken': 'string',
'AppImageConfigs': [
{
'AppImageConfigArn': 'string',
'AppImageConfigName': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'KernelGatewayImageConfig': {
'KernelSpecs': [
{
'Name': 'string',
'DisplayName': 'string'
},
],
'FileSystemConfig': {
'MountPath': 'string',
'DefaultUid': 123,
'DefaultGid': 123
}
}
},
]
}
Response Structure
(dict) --
NextToken (string) --
A token for getting the next set of AppImageConfigs, if there are any.
AppImageConfigs (list) --
A list of AppImageConfigs and their properties.
(dict) --
The configuration for running a SageMaker image as a KernelGateway app.
AppImageConfigArn (string) --
The Amazon Resource Name (ARN) of the AppImageConfig.
AppImageConfigName (string) --
The name of the AppImageConfig. Must be unique to your account.
CreationTime (datetime) --
When the AppImageConfig was created.
LastModifiedTime (datetime) --
When the AppImageConfig was last modified.
KernelGatewayImageConfig (dict) --
The configuration for the file system and kernels in the SageMaker image.
KernelSpecs (list) --
The specification of the Jupyter kernels in the image.
(dict) --
The specification of a Jupyter kernel.
Name (string) --
The name of the Jupyter kernel in the image. This value is case sensitive.
DisplayName (string) --
The display name of the kernel.
FileSystemConfig (dict) --
The Amazon Elastic File System (EFS) storage configuration for a SageMaker image.
MountPath (string) --
The path within the image to mount the user's EFS home directory. The directory should be empty. If not specified, defaults to /home/sagemaker-user .
DefaultUid (integer) --
The default POSIX user ID (UID). If not specified, defaults to 1000 .
DefaultGid (integer) --
The default POSIX group ID (GID). If not specified, defaults to 100 .
Lists apps.
See also: AWS API Documentation
Request Syntax
response = client.list_apps(
NextToken='string',
MaxResults=123,
SortOrder='Ascending'|'Descending',
SortBy='CreationTime',
DomainIdEquals='string',
UserProfileNameEquals='string'
)
dict
Response Syntax
{
'Apps': [
{
'DomainId': 'string',
'UserProfileName': 'string',
'AppType': 'JupyterServer'|'KernelGateway'|'TensorBoard',
'AppName': 'string',
'Status': 'Deleted'|'Deleting'|'Failed'|'InService'|'Pending',
'CreationTime': datetime(2015, 1, 1)
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
Apps (list) --
The list of apps.
(dict) --
Details about an Amazon SageMaker app.
DomainId (string) --
The domain ID.
UserProfileName (string) --
The user profile name.
AppType (string) --
The type of app.
AppName (string) --
The name of the app.
Status (string) --
The status.
CreationTime (datetime) --
The creation time.
NextToken (string) --
If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
Lists the artifacts in your account and their properties.
See also: AWS API Documentation
Request Syntax
response = client.list_artifacts(
SourceUri='string',
ArtifactType='string',
CreatedAfter=datetime(2015, 1, 1),
CreatedBefore=datetime(2015, 1, 1),
SortBy='CreationTime',
SortOrder='Ascending'|'Descending',
NextToken='string',
MaxResults=123
)
dict
Response Syntax
{
'ArtifactSummaries': [
{
'ArtifactArn': 'string',
'ArtifactName': 'string',
'Source': {
'SourceUri': 'string',
'SourceTypes': [
{
'SourceIdType': 'MD5Hash'|'S3ETag'|'S3Version'|'Custom',
'Value': 'string'
},
]
},
'ArtifactType': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1)
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
ArtifactSummaries (list) --
A list of artifacts and their properties.
(dict) --
Lists a summary of the properties of an artifact. An artifact represents a URI addressable object or data. Some examples are a dataset and a model.
ArtifactArn (string) --
The Amazon Resource Name (ARN) of the artifact.
ArtifactName (string) --
The name of the artifact.
Source (dict) --
The source of the artifact.
SourceUri (string) --
The URI of the source.
SourceTypes (list) --
A list of source types.
(dict) --
The ID and ID type of an artifact source.
SourceIdType (string) --
The type of ID.
Value (string) --
The ID.
ArtifactType (string) --
The type of the artifact.
CreationTime (datetime) --
When the artifact was created.
LastModifiedTime (datetime) --
When the artifact was last modified.
NextToken (string) --
A token for getting the next set of artifacts, if there are any.
Exceptions
Lists the associations in your account and their properties.
See also: AWS API Documentation
Request Syntax
response = client.list_associations(
SourceArn='string',
DestinationArn='string',
SourceType='string',
DestinationType='string',
AssociationType='ContributedTo'|'AssociatedWith'|'DerivedFrom'|'Produced',
CreatedAfter=datetime(2015, 1, 1),
CreatedBefore=datetime(2015, 1, 1),
SortBy='SourceArn'|'DestinationArn'|'SourceType'|'DestinationType'|'CreationTime',
SortOrder='Ascending'|'Descending',
NextToken='string',
MaxResults=123
)
dict
Response Syntax
{
'AssociationSummaries': [
{
'SourceArn': 'string',
'DestinationArn': 'string',
'SourceType': 'string',
'DestinationType': 'string',
'AssociationType': 'ContributedTo'|'AssociatedWith'|'DerivedFrom'|'Produced',
'SourceName': 'string',
'DestinationName': 'string',
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
}
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
AssociationSummaries (list) --
A list of associations and their properties.
(dict) --
Lists a summary of the properties of an association. An association is an entity that links other lineage or experiment entities. An example would be an association between a training job and a model.
SourceArn (string) --
The ARN of the source.
DestinationArn (string) --
The Amazon Resource Name (ARN) of the destination.
SourceType (string) --
The source type.
DestinationType (string) --
The destination type.
AssociationType (string) --
The type of the association.
SourceName (string) --
The name of the source.
DestinationName (string) --
The name of the destination.
CreationTime (datetime) --
When the association was created.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
NextToken (string) --
A token for getting the next set of associations, if there are any.
Exceptions
Request a list of jobs.
See also: AWS API Documentation
Request Syntax
response = client.list_auto_ml_jobs(
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
LastModifiedTimeAfter=datetime(2015, 1, 1),
LastModifiedTimeBefore=datetime(2015, 1, 1),
NameContains='string',
StatusEquals='Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping',
SortOrder='Ascending'|'Descending',
SortBy='Name'|'CreationTime'|'Status',
MaxResults=123,
NextToken='string'
)
dict
Response Syntax
{
'AutoMLJobSummaries': [
{
'AutoMLJobName': 'string',
'AutoMLJobArn': 'string',
'AutoMLJobStatus': 'Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping',
'AutoMLJobSecondaryStatus': 'Starting'|'AnalyzingData'|'FeatureEngineering'|'ModelTuning'|'MaxCandidatesReached'|'Failed'|'Stopped'|'MaxAutoMLJobRuntimeReached'|'Stopping'|'CandidateDefinitionsGenerated'|'GeneratingExplainabilityReport'|'Completed'|'ExplainabilityError'|'DeployingModel'|'ModelDeploymentError',
'CreationTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'FailureReason': 'string',
'PartialFailureReasons': [
{
'PartialFailureMessage': 'string'
},
]
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
AutoMLJobSummaries (list) --
Returns a summary list of jobs.
(dict) --
Provides a summary about an AutoML job.
AutoMLJobName (string) --
The name of the AutoML you are requesting.
AutoMLJobArn (string) --
The ARN of the AutoML job.
AutoMLJobStatus (string) --
The status of the AutoML job.
AutoMLJobSecondaryStatus (string) --
The secondary status of the AutoML job.
CreationTime (datetime) --
When the AutoML job was created.
EndTime (datetime) --
The end time of an AutoML job.
LastModifiedTime (datetime) --
When the AutoML job was last modified.
FailureReason (string) --
The failure reason of an AutoML job.
PartialFailureReasons (list) --
The list of reasons for partial failures within an AutoML job.
(dict) --
The reason for a partial failure of an AutoML job.
PartialFailureMessage (string) --
The message containing the reason for a partial failure of an AutoML job.
NextToken (string) --
If the previous response was truncated, you receive this token. Use it in your next request to receive the next set of results.
List the candidates created for the job.
See also: AWS API Documentation
Request Syntax
response = client.list_candidates_for_auto_ml_job(
AutoMLJobName='string',
StatusEquals='Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping',
CandidateNameEquals='string',
SortOrder='Ascending'|'Descending',
SortBy='CreationTime'|'Status'|'FinalObjectiveMetricValue',
MaxResults=123,
NextToken='string'
)
[REQUIRED]
List the candidates created for the job by providing the job's name.
dict
Response Syntax
{
'Candidates': [
{
'CandidateName': 'string',
'FinalAutoMLJobObjectiveMetric': {
'Type': 'Maximize'|'Minimize',
'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC',
'Value': ...
},
'ObjectiveStatus': 'Succeeded'|'Pending'|'Failed',
'CandidateSteps': [
{
'CandidateStepType': 'AWS::SageMaker::TrainingJob'|'AWS::SageMaker::TransformJob'|'AWS::SageMaker::ProcessingJob',
'CandidateStepArn': 'string',
'CandidateStepName': 'string'
},
],
'CandidateStatus': 'Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping',
'InferenceContainers': [
{
'Image': 'string',
'ModelDataUrl': 'string',
'Environment': {
'string': 'string'
}
},
],
'CreationTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'FailureReason': 'string',
'CandidateProperties': {
'CandidateArtifactLocations': {
'Explainability': 'string'
}
}
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
Candidates (list) --
Summaries about the AutoMLCandidates .
(dict) --
An Autopilot job returns recommendations, or candidates. Each candidate has futher details about the steps involved and the status.
CandidateName (string) --
The name of the candidate.
FinalAutoMLJobObjectiveMetric (dict) --
The best candidate result from an AutoML training job.
Type (string) --
The type of metric with the best result.
MetricName (string) --
The name of the metric with the best result. For a description of the possible objective metrics, see AutoMLJobObjective$MetricName .
Value (float) --
The value of the metric with the best result.
ObjectiveStatus (string) --
The objective's status.
CandidateSteps (list) --
Information about the candidate's steps.
(dict) --
Information about the steps for a candidate and what step it is working on.
CandidateStepType (string) --
Whether the candidate is at the transform, training, or processing step.
CandidateStepArn (string) --
The ARN for the candidate's step.
CandidateStepName (string) --
The name for the candidate's step.
CandidateStatus (string) --
The candidate's status.
InferenceContainers (list) --
Information about the inference container definitions.
(dict) --
A list of container definitions that describe the different containers that make up an AutoML candidate. For more information, see .
Image (string) --
The ECR path of the container. For more information, see .
ModelDataUrl (string) --
The location of the model artifacts. For more information, see .
Environment (dict) --
The environment variables to set in the container. For more information, see .
CreationTime (datetime) --
The creation time.
EndTime (datetime) --
The end time.
LastModifiedTime (datetime) --
The last modified time.
FailureReason (string) --
The failure reason.
CandidateProperties (dict) --
The AutoML candidate's properties.
CandidateArtifactLocations (dict) --
The Amazon S3 prefix to the artifacts generated for an AutoML candidate.
Explainability (string) --
The Amazon S3 prefix to the explainability artifacts generated for the AutoML candidate.
NextToken (string) --
If the previous response was truncated, you receive this token. Use it in your next request to receive the next set of results.
Exceptions
Gets a list of the Git repositories in your account.
See also: AWS API Documentation
Request Syntax
response = client.list_code_repositories(
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
LastModifiedTimeAfter=datetime(2015, 1, 1),
LastModifiedTimeBefore=datetime(2015, 1, 1),
MaxResults=123,
NameContains='string',
NextToken='string',
SortBy='Name'|'CreationTime'|'LastModifiedTime',
SortOrder='Ascending'|'Descending'
)
dict
Response Syntax
{
'CodeRepositorySummaryList': [
{
'CodeRepositoryName': 'string',
'CodeRepositoryArn': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'GitConfig': {
'RepositoryUrl': 'string',
'Branch': 'string',
'SecretArn': 'string'
}
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
CodeRepositorySummaryList (list) --
Gets a list of summaries of the Git repositories. Each summary specifies the following values for the repository:
(dict) --
Specifies summary information about a Git repository.
CodeRepositoryName (string) --
The name of the Git repository.
CodeRepositoryArn (string) --
The Amazon Resource Name (ARN) of the Git repository.
CreationTime (datetime) --
The date and time that the Git repository was created.
LastModifiedTime (datetime) --
The date and time that the Git repository was last modified.
GitConfig (dict) --
Configuration details for the Git repository, including the URL where it is located and the ARN of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the repository.
RepositoryUrl (string) --
The URL where the Git repository is located.
Branch (string) --
The default branch for the Git repository.
SecretArn (string) --
The Amazon Resource Name (ARN) of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the git repository. The secret must have a staging label of AWSCURRENT and must be in the following format:
{"username": *UserName* , "password": *Password* }
NextToken (string) --
If the result of a ListCodeRepositoriesOutput request was truncated, the response includes a NextToken . To get the next set of Git repositories, use the token in the next request.
Lists model compilation jobs that satisfy various filters.
To create a model compilation job, use CreateCompilationJob . To get information about a particular model compilation job you have created, use DescribeCompilationJob .
See also: AWS API Documentation
Request Syntax
response = client.list_compilation_jobs(
NextToken='string',
MaxResults=123,
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
LastModifiedTimeAfter=datetime(2015, 1, 1),
LastModifiedTimeBefore=datetime(2015, 1, 1),
NameContains='string',
StatusEquals='INPROGRESS'|'COMPLETED'|'FAILED'|'STARTING'|'STOPPING'|'STOPPED',
SortBy='Name'|'CreationTime'|'Status',
SortOrder='Ascending'|'Descending'
)
dict
Response Syntax
{
'CompilationJobSummaries': [
{
'CompilationJobName': 'string',
'CompilationJobArn': 'string',
'CreationTime': datetime(2015, 1, 1),
'CompilationStartTime': datetime(2015, 1, 1),
'CompilationEndTime': datetime(2015, 1, 1),
'CompilationTargetDevice': 'lambda'|'ml_m4'|'ml_m5'|'ml_c4'|'ml_c5'|'ml_p2'|'ml_p3'|'ml_g4dn'|'ml_inf1'|'ml_eia2'|'jetson_tx1'|'jetson_tx2'|'jetson_nano'|'jetson_xavier'|'rasp3b'|'imx8qm'|'deeplens'|'rk3399'|'rk3288'|'aisage'|'sbe_c'|'qcs605'|'qcs603'|'sitara_am57x'|'amba_cv22'|'amba_cv25'|'x86_win32'|'x86_win64'|'coreml'|'jacinto_tda4vm',
'CompilationTargetPlatformOs': 'ANDROID'|'LINUX',
'CompilationTargetPlatformArch': 'X86_64'|'X86'|'ARM64'|'ARM_EABI'|'ARM_EABIHF',
'CompilationTargetPlatformAccelerator': 'INTEL_GRAPHICS'|'MALI'|'NVIDIA',
'LastModifiedTime': datetime(2015, 1, 1),
'CompilationJobStatus': 'INPROGRESS'|'COMPLETED'|'FAILED'|'STARTING'|'STOPPING'|'STOPPED'
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
CompilationJobSummaries (list) --
An array of CompilationJobSummary objects, each describing a model compilation job.
(dict) --
A summary of a model compilation job.
CompilationJobName (string) --
The name of the model compilation job that you want a summary for.
CompilationJobArn (string) --
The Amazon Resource Name (ARN) of the model compilation job.
CreationTime (datetime) --
The time when the model compilation job was created.
CompilationStartTime (datetime) --
The time when the model compilation job started.
CompilationEndTime (datetime) --
The time when the model compilation job completed.
CompilationTargetDevice (string) --
The type of device that the model will run on after the compilation job has completed.
CompilationTargetPlatformOs (string) --
The type of OS that the model will run on after the compilation job has completed.
CompilationTargetPlatformArch (string) --
The type of architecture that the model will run on after the compilation job has completed.
CompilationTargetPlatformAccelerator (string) --
The type of accelerator that the model will run on after the compilation job has completed.
LastModifiedTime (datetime) --
The time when the model compilation job was last modified.
CompilationJobStatus (string) --
The status of the model compilation job.
NextToken (string) --
If the response is truncated, Amazon SageMaker returns this NextToken . To retrieve the next set of model compilation jobs, use this token in the next request.
Lists the contexts in your account and their properties.
See also: AWS API Documentation
Request Syntax
response = client.list_contexts(
SourceUri='string',
ContextType='string',
CreatedAfter=datetime(2015, 1, 1),
CreatedBefore=datetime(2015, 1, 1),
SortBy='Name'|'CreationTime',
SortOrder='Ascending'|'Descending',
NextToken='string',
MaxResults=123
)
dict
Response Syntax
{
'ContextSummaries': [
{
'ContextArn': 'string',
'ContextName': 'string',
'Source': {
'SourceUri': 'string',
'SourceType': 'string',
'SourceId': 'string'
},
'ContextType': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1)
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
ContextSummaries (list) --
A list of contexts and their properties.
(dict) --
Lists a summary of the properties of a context. A context provides a logical grouping of other entities.
ContextArn (string) --
The Amazon Resource Name (ARN) of the context.
ContextName (string) --
The name of the context.
Source (dict) --
The source of the context.
SourceUri (string) --
The URI of the source.
SourceType (string) --
The type of the source.
SourceId (string) --
The ID of the source.
ContextType (string) --
The type of the context.
CreationTime (datetime) --
When the context was created.
LastModifiedTime (datetime) --
When the context was last modified.
NextToken (string) --
A token for getting the next set of contexts, if there are any.
Exceptions
Lists the data quality job definitions in your account.
See also: AWS API Documentation
Request Syntax
response = client.list_data_quality_job_definitions(
EndpointName='string',
SortBy='Name'|'CreationTime',
SortOrder='Ascending'|'Descending',
NextToken='string',
MaxResults=123,
NameContains='string',
CreationTimeBefore=datetime(2015, 1, 1),
CreationTimeAfter=datetime(2015, 1, 1)
)
dict
Response Syntax
{
'JobDefinitionSummaries': [
{
'MonitoringJobDefinitionName': 'string',
'MonitoringJobDefinitionArn': 'string',
'CreationTime': datetime(2015, 1, 1),
'EndpointName': 'string'
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
JobDefinitionSummaries (list) --
A list of data quality monitoring job definitions.
(dict) --
Summary information about a monitoring job.
MonitoringJobDefinitionName (string) --
The name of the monitoring job.
MonitoringJobDefinitionArn (string) --
The Amazon Resource Name (ARN) of the monitoring job.
CreationTime (datetime) --
The time that the monitoring job was created.
EndpointName (string) --
The name of the endpoint that the job monitors.
NextToken (string) --
If the result of the previous ListDataQualityJobDefinitions request was truncated, the response includes a NextToken . To retrieve the next set of data quality monitoring job definitions, use the token in the next request.
Returns a list of devices in the fleet.
See also: AWS API Documentation
Request Syntax
response = client.list_device_fleets(
NextToken='string',
MaxResults=123,
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
LastModifiedTimeAfter=datetime(2015, 1, 1),
LastModifiedTimeBefore=datetime(2015, 1, 1),
NameContains='string',
SortBy='NAME'|'CREATION_TIME'|'LAST_MODIFIED_TIME',
SortOrder='Ascending'|'Descending'
)
dict
Response Syntax
{
'DeviceFleetSummaries': [
{
'DeviceFleetArn': 'string',
'DeviceFleetName': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1)
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
DeviceFleetSummaries (list) --
Summary of the device fleet.
(dict) --
Summary of the device fleet.
DeviceFleetArn (string) --
Amazon Resource Name (ARN) of the device fleet.
DeviceFleetName (string) --
Name of the device fleet.
CreationTime (datetime) --
Timestamp of when the device fleet was created.
LastModifiedTime (datetime) --
Timestamp of when the device fleet was last updated.
NextToken (string) --
The response from the last list when returning a list large enough to need tokening.
A list of devices.
See also: AWS API Documentation
Request Syntax
response = client.list_devices(
NextToken='string',
MaxResults=123,
LatestHeartbeatAfter=datetime(2015, 1, 1),
ModelName='string',
DeviceFleetName='string'
)
dict
Response Syntax
{
'DeviceSummaries': [
{
'DeviceName': 'string',
'DeviceArn': 'string',
'Description': 'string',
'DeviceFleetName': 'string',
'IotThingName': 'string',
'RegistrationTime': datetime(2015, 1, 1),
'LatestHeartbeat': datetime(2015, 1, 1),
'Models': [
{
'ModelName': 'string',
'ModelVersion': 'string'
},
]
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
DeviceSummaries (list) --
Summary of devices.
(dict) --
Summary of the device.
DeviceName (string) --
The unique identifier of the device.
DeviceArn (string) --
Amazon Resource Name (ARN) of the device.
Description (string) --
A description of the device.
DeviceFleetName (string) --
The name of the fleet the device belongs to.
IotThingName (string) --
The Amazon Web Services Internet of Things (IoT) object thing name associated with the device..
RegistrationTime (datetime) --
The timestamp of the last registration or de-reregistration.
LatestHeartbeat (datetime) --
The last heartbeat received from the device.
Models (list) --
Models on the device.
(dict) --
Summary of model on edge device.
ModelName (string) --
The name of the model.
ModelVersion (string) --
The version model.
NextToken (string) --
The response from the last list when returning a list large enough to need tokening.
Lists the domains.
See also: AWS API Documentation
Request Syntax
response = client.list_domains(
NextToken='string',
MaxResults=123
)
dict
Response Syntax
{
'Domains': [
{
'DomainArn': 'string',
'DomainId': 'string',
'DomainName': 'string',
'Status': 'Deleting'|'Failed'|'InService'|'Pending'|'Updating'|'Update_Failed'|'Delete_Failed',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'Url': 'string'
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
Domains (list) --
The list of domains.
(dict) --
The domain's details.
DomainArn (string) --
The domain's Amazon Resource Name (ARN).
DomainId (string) --
The domain ID.
DomainName (string) --
The domain name.
Status (string) --
The status.
CreationTime (datetime) --
The creation time.
LastModifiedTime (datetime) --
The last modified time.
Url (string) --
The domain's URL.
NextToken (string) --
If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
Returns a list of edge packaging jobs.
See also: AWS API Documentation
Request Syntax
response = client.list_edge_packaging_jobs(
NextToken='string',
MaxResults=123,
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
LastModifiedTimeAfter=datetime(2015, 1, 1),
LastModifiedTimeBefore=datetime(2015, 1, 1),
NameContains='string',
ModelNameContains='string',
StatusEquals='STARTING'|'INPROGRESS'|'COMPLETED'|'FAILED'|'STOPPING'|'STOPPED',
SortBy='NAME'|'MODEL_NAME'|'CREATION_TIME'|'LAST_MODIFIED_TIME'|'STATUS',
SortOrder='Ascending'|'Descending'
)
dict
Response Syntax
{
'EdgePackagingJobSummaries': [
{
'EdgePackagingJobArn': 'string',
'EdgePackagingJobName': 'string',
'EdgePackagingJobStatus': 'STARTING'|'INPROGRESS'|'COMPLETED'|'FAILED'|'STOPPING'|'STOPPED',
'CompilationJobName': 'string',
'ModelName': 'string',
'ModelVersion': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1)
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
EdgePackagingJobSummaries (list) --
Summaries of edge packaging jobs.
(dict) --
Summary of edge packaging job.
EdgePackagingJobArn (string) --
The Amazon Resource Name (ARN) of the edge packaging job.
EdgePackagingJobName (string) --
The name of the edge packaging job.
EdgePackagingJobStatus (string) --
The status of the edge packaging job.
CompilationJobName (string) --
The name of the SageMaker Neo compilation job.
ModelName (string) --
The name of the model.
ModelVersion (string) --
The version of the model.
CreationTime (datetime) --
The timestamp of when the job was created.
LastModifiedTime (datetime) --
The timestamp of when the edge packaging job was last updated.
NextToken (string) --
Token to use when calling the next page of results.
Lists endpoint configurations.
See also: AWS API Documentation
Request Syntax
response = client.list_endpoint_configs(
SortBy='Name'|'CreationTime',
SortOrder='Ascending'|'Descending',
NextToken='string',
MaxResults=123,
NameContains='string',
CreationTimeBefore=datetime(2015, 1, 1),
CreationTimeAfter=datetime(2015, 1, 1)
)
dict
Response Syntax
{
'EndpointConfigs': [
{
'EndpointConfigName': 'string',
'EndpointConfigArn': 'string',
'CreationTime': datetime(2015, 1, 1)
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
EndpointConfigs (list) --
An array of endpoint configurations.
(dict) --
Provides summary information for an endpoint configuration.
EndpointConfigName (string) --
The name of the endpoint configuration.
EndpointConfigArn (string) --
The Amazon Resource Name (ARN) of the endpoint configuration.
CreationTime (datetime) --
A timestamp that shows when the endpoint configuration was created.
NextToken (string) --
If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of endpoint configurations, use it in the subsequent request
Lists endpoints.
See also: AWS API Documentation
Request Syntax
response = client.list_endpoints(
SortBy='Name'|'CreationTime'|'Status',
SortOrder='Ascending'|'Descending',
NextToken='string',
MaxResults=123,
NameContains='string',
CreationTimeBefore=datetime(2015, 1, 1),
CreationTimeAfter=datetime(2015, 1, 1),
LastModifiedTimeBefore=datetime(2015, 1, 1),
LastModifiedTimeAfter=datetime(2015, 1, 1),
StatusEquals='OutOfService'|'Creating'|'Updating'|'SystemUpdating'|'RollingBack'|'InService'|'Deleting'|'Failed'
)
dict
Response Syntax
{
'Endpoints': [
{
'EndpointName': 'string',
'EndpointArn': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'EndpointStatus': 'OutOfService'|'Creating'|'Updating'|'SystemUpdating'|'RollingBack'|'InService'|'Deleting'|'Failed'
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
Endpoints (list) --
An array or endpoint objects.
(dict) --
Provides summary information for an endpoint.
EndpointName (string) --
The name of the endpoint.
EndpointArn (string) --
The Amazon Resource Name (ARN) of the endpoint.
CreationTime (datetime) --
A timestamp that shows when the endpoint was created.
LastModifiedTime (datetime) --
A timestamp that shows when the endpoint was last modified.
EndpointStatus (string) --
The status of the endpoint.
To get a list of endpoints with a specified status, use the ListEndpointsInput$StatusEquals filter.
NextToken (string) --
If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of training jobs, use it in the subsequent request.
Lists all the experiments in your account. The list can be filtered to show only experiments that were created in a specific time range. The list can be sorted by experiment name or creation time.
See also: AWS API Documentation
Request Syntax
response = client.list_experiments(
CreatedAfter=datetime(2015, 1, 1),
CreatedBefore=datetime(2015, 1, 1),
SortBy='Name'|'CreationTime',
SortOrder='Ascending'|'Descending',
NextToken='string',
MaxResults=123
)
dict
Response Syntax
{
'ExperimentSummaries': [
{
'ExperimentArn': 'string',
'ExperimentName': 'string',
'DisplayName': 'string',
'ExperimentSource': {
'SourceArn': 'string',
'SourceType': 'string'
},
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1)
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
ExperimentSummaries (list) --
A list of the summaries of your experiments.
(dict) --
A summary of the properties of an experiment. To get the complete set of properties, call the DescribeExperiment API and provide the ExperimentName .
ExperimentArn (string) --
The Amazon Resource Name (ARN) of the experiment.
ExperimentName (string) --
The name of the experiment.
DisplayName (string) --
The name of the experiment as displayed. If DisplayName isn't specified, ExperimentName is displayed.
ExperimentSource (dict) --
The source of the experiment.
SourceArn (string) --
The Amazon Resource Name (ARN) of the source.
SourceType (string) --
The source type.
CreationTime (datetime) --
When the experiment was created.
LastModifiedTime (datetime) --
When the experiment was last modified.
NextToken (string) --
A token for getting the next set of experiments, if there are any.
List FeatureGroup s based on given filter and order.
See also: AWS API Documentation
Request Syntax
response = client.list_feature_groups(
NameContains='string',
FeatureGroupStatusEquals='Creating'|'Created'|'CreateFailed'|'Deleting'|'DeleteFailed',
OfflineStoreStatusEquals='Active'|'Blocked'|'Disabled',
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
SortOrder='Ascending'|'Descending',
SortBy='Name'|'FeatureGroupStatus'|'OfflineStoreStatus'|'CreationTime',
MaxResults=123,
NextToken='string'
)
dict
Response Syntax
{
'FeatureGroupSummaries': [
{
'FeatureGroupName': 'string',
'FeatureGroupArn': 'string',
'CreationTime': datetime(2015, 1, 1),
'FeatureGroupStatus': 'Creating'|'Created'|'CreateFailed'|'Deleting'|'DeleteFailed',
'OfflineStoreStatus': {
'Status': 'Active'|'Blocked'|'Disabled',
'BlockedReason': 'string'
}
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
FeatureGroupSummaries (list) --
A summary of feature groups.
(dict) --
The name, Arn, CreationTime , FeatureGroup values, LastUpdatedTime and EnableOnlineStorage status of a FeatureGroup .
FeatureGroupName (string) --
The name of FeatureGroup .
FeatureGroupArn (string) --
Unique identifier for the FeatureGroup .
CreationTime (datetime) --
A timestamp indicating the time of creation time of the FeatureGroup .
FeatureGroupStatus (string) --
The status of a FeatureGroup. The status can be any of the following: Creating , Created , CreateFail , Deleting or DetailFail .
OfflineStoreStatus (dict) --
Notifies you if replicating data into the OfflineStore has failed. Returns either: Active or Blocked .
Status (string) --
An OfflineStore status.
BlockedReason (string) --
The justification for why the OfflineStoreStatus is Blocked (if applicable).
NextToken (string) --
A token to resume pagination of ListFeatureGroups results.
Returns information about the flow definitions in your account.
See also: AWS API Documentation
Request Syntax
response = client.list_flow_definitions(
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
SortOrder='Ascending'|'Descending',
NextToken='string',
MaxResults=123
)
dict
Response Syntax
{
'FlowDefinitionSummaries': [
{
'FlowDefinitionName': 'string',
'FlowDefinitionArn': 'string',
'FlowDefinitionStatus': 'Initializing'|'Active'|'Failed'|'Deleting',
'CreationTime': datetime(2015, 1, 1),
'FailureReason': 'string'
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
FlowDefinitionSummaries (list) --
An array of objects describing the flow definitions.
(dict) --
Contains summary information about the flow definition.
FlowDefinitionName (string) --
The name of the flow definition.
FlowDefinitionArn (string) --
The Amazon Resource Name (ARN) of the flow definition.
FlowDefinitionStatus (string) --
The status of the flow definition. Valid values:
CreationTime (datetime) --
The timestamp when SageMaker created the flow definition.
FailureReason (string) --
The reason why the flow definition creation failed. A failure reason is returned only when the flow definition status is Failed .
NextToken (string) --
A token to resume pagination.
Returns information about the human task user interfaces in your account.
See also: AWS API Documentation
Request Syntax
response = client.list_human_task_uis(
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
SortOrder='Ascending'|'Descending',
NextToken='string',
MaxResults=123
)
dict
Response Syntax
{
'HumanTaskUiSummaries': [
{
'HumanTaskUiName': 'string',
'HumanTaskUiArn': 'string',
'CreationTime': datetime(2015, 1, 1)
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
HumanTaskUiSummaries (list) --
An array of objects describing the human task user interfaces.
(dict) --
Container for human task user interface information.
HumanTaskUiName (string) --
The name of the human task user interface.
HumanTaskUiArn (string) --
The Amazon Resource Name (ARN) of the human task user interface.
CreationTime (datetime) --
A timestamp when SageMaker created the human task user interface.
NextToken (string) --
A token to resume pagination.
Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account.
See also: AWS API Documentation
Request Syntax
response = client.list_hyper_parameter_tuning_jobs(
NextToken='string',
MaxResults=123,
SortBy='Name'|'Status'|'CreationTime',
SortOrder='Ascending'|'Descending',
NameContains='string',
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
LastModifiedTimeAfter=datetime(2015, 1, 1),
LastModifiedTimeBefore=datetime(2015, 1, 1),
StatusEquals='Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping'
)
dict
Response Syntax
{
'HyperParameterTuningJobSummaries': [
{
'HyperParameterTuningJobName': 'string',
'HyperParameterTuningJobArn': 'string',
'HyperParameterTuningJobStatus': 'Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping',
'Strategy': 'Bayesian'|'Random',
'CreationTime': datetime(2015, 1, 1),
'HyperParameterTuningEndTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'TrainingJobStatusCounters': {
'Completed': 123,
'InProgress': 123,
'RetryableError': 123,
'NonRetryableError': 123,
'Stopped': 123
},
'ObjectiveStatusCounters': {
'Succeeded': 123,
'Pending': 123,
'Failed': 123
},
'ResourceLimits': {
'MaxNumberOfTrainingJobs': 123,
'MaxParallelTrainingJobs': 123
}
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
HyperParameterTuningJobSummaries (list) --
A list of HyperParameterTuningJobSummary objects that describe the tuning jobs that the ListHyperParameterTuningJobs request returned.
(dict) --
Provides summary information about a hyperparameter tuning job.
HyperParameterTuningJobName (string) --
The name of the tuning job.
HyperParameterTuningJobArn (string) --
The Amazon Resource Name (ARN) of the tuning job.
HyperParameterTuningJobStatus (string) --
The status of the tuning job.
Strategy (string) --
Specifies the search strategy hyperparameter tuning uses to choose which hyperparameters to use for each iteration. Currently, the only valid value is Bayesian.
CreationTime (datetime) --
The date and time that the tuning job was created.
HyperParameterTuningEndTime (datetime) --
The date and time that the tuning job ended.
LastModifiedTime (datetime) --
The date and time that the tuning job was modified.
TrainingJobStatusCounters (dict) --
The TrainingJobStatusCounters object that specifies the numbers of training jobs, categorized by status, that this tuning job launched.
Completed (integer) --
The number of completed training jobs launched by the hyperparameter tuning job.
InProgress (integer) --
The number of in-progress training jobs launched by a hyperparameter tuning job.
RetryableError (integer) --
The number of training jobs that failed, but can be retried. A failed training job can be retried only if it failed because an internal service error occurred.
NonRetryableError (integer) --
The number of training jobs that failed and can't be retried. A failed training job can't be retried if it failed because a client error occurred.
Stopped (integer) --
The number of training jobs launched by a hyperparameter tuning job that were manually stopped.
ObjectiveStatusCounters (dict) --
The ObjectiveStatusCounters object that specifies the numbers of training jobs, categorized by objective metric status, that this tuning job launched.
Succeeded (integer) --
The number of training jobs whose final objective metric was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.
Pending (integer) --
The number of training jobs that are in progress and pending evaluation of their final objective metric.
Failed (integer) --
The number of training jobs whose final objective metric was not evaluated and used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.
ResourceLimits (dict) --
The ResourceLimits object that specifies the maximum number of training jobs and parallel training jobs allowed for this tuning job.
MaxNumberOfTrainingJobs (integer) --
The maximum number of training jobs that a hyperparameter tuning job can launch.
MaxParallelTrainingJobs (integer) --
The maximum number of concurrent training jobs that a hyperparameter tuning job can launch.
NextToken (string) --
If the result of this ListHyperParameterTuningJobs request was truncated, the response includes a NextToken . To retrieve the next set of tuning jobs, use the token in the next request.
Lists the versions of a specified image and their properties. The list can be filtered by creation time or modified time.
See also: AWS API Documentation
Request Syntax
response = client.list_image_versions(
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
ImageName='string',
LastModifiedTimeAfter=datetime(2015, 1, 1),
LastModifiedTimeBefore=datetime(2015, 1, 1),
MaxResults=123,
NextToken='string',
SortBy='CREATION_TIME'|'LAST_MODIFIED_TIME'|'VERSION',
SortOrder='ASCENDING'|'DESCENDING'
)
[REQUIRED]
The name of the image to list the versions of.
dict
Response Syntax
{
'ImageVersions': [
{
'CreationTime': datetime(2015, 1, 1),
'FailureReason': 'string',
'ImageArn': 'string',
'ImageVersionArn': 'string',
'ImageVersionStatus': 'CREATING'|'CREATED'|'CREATE_FAILED'|'DELETING'|'DELETE_FAILED',
'LastModifiedTime': datetime(2015, 1, 1),
'Version': 123
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
ImageVersions (list) --
A list of versions and their properties.
(dict) --
A version of a SageMaker Image . A version represents an existing container image.
CreationTime (datetime) --
When the version was created.
FailureReason (string) --
When a create or delete operation fails, the reason for the failure.
ImageArn (string) --
The Amazon Resource Name (ARN) of the image the version is based on.
ImageVersionArn (string) --
The ARN of the version.
ImageVersionStatus (string) --
The status of the version.
LastModifiedTime (datetime) --
When the version was last modified.
Version (integer) --
The version number.
NextToken (string) --
A token for getting the next set of versions, if there are any.
Exceptions
Lists the images in your account and their properties. The list can be filtered by creation time or modified time, and whether the image name contains a specified string.
See also: AWS API Documentation
Request Syntax
response = client.list_images(
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
LastModifiedTimeAfter=datetime(2015, 1, 1),
LastModifiedTimeBefore=datetime(2015, 1, 1),
MaxResults=123,
NameContains='string',
NextToken='string',
SortBy='CREATION_TIME'|'LAST_MODIFIED_TIME'|'IMAGE_NAME',
SortOrder='ASCENDING'|'DESCENDING'
)
dict
Response Syntax
{
'Images': [
{
'CreationTime': datetime(2015, 1, 1),
'Description': 'string',
'DisplayName': 'string',
'FailureReason': 'string',
'ImageArn': 'string',
'ImageName': 'string',
'ImageStatus': 'CREATING'|'CREATED'|'CREATE_FAILED'|'UPDATING'|'UPDATE_FAILED'|'DELETING'|'DELETE_FAILED',
'LastModifiedTime': datetime(2015, 1, 1)
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
Images (list) --
A list of images and their properties.
(dict) --
A SageMaker image. A SageMaker image represents a set of container images that are derived from a common base container image. Each of these container images is represented by a SageMaker ImageVersion .
CreationTime (datetime) --
When the image was created.
Description (string) --
The description of the image.
DisplayName (string) --
The name of the image as displayed.
FailureReason (string) --
When a create, update, or delete operation fails, the reason for the failure.
ImageArn (string) --
The Amazon Resource Name (ARN) of the image.
ImageName (string) --
The name of the image.
ImageStatus (string) --
The status of the image.
LastModifiedTime (datetime) --
When the image was last modified.
NextToken (string) --
A token for getting the next set of images, if there are any.
Gets a list of labeling jobs.
See also: AWS API Documentation
Request Syntax
response = client.list_labeling_jobs(
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
LastModifiedTimeAfter=datetime(2015, 1, 1),
LastModifiedTimeBefore=datetime(2015, 1, 1),
MaxResults=123,
NextToken='string',
NameContains='string',
SortBy='Name'|'CreationTime'|'Status',
SortOrder='Ascending'|'Descending',
StatusEquals='Initializing'|'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped'
)
dict
Response Syntax
{
'LabelingJobSummaryList': [
{
'LabelingJobName': 'string',
'LabelingJobArn': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'LabelingJobStatus': 'Initializing'|'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'LabelCounters': {
'TotalLabeled': 123,
'HumanLabeled': 123,
'MachineLabeled': 123,
'FailedNonRetryableError': 123,
'Unlabeled': 123
},
'WorkteamArn': 'string',
'PreHumanTaskLambdaArn': 'string',
'AnnotationConsolidationLambdaArn': 'string',
'FailureReason': 'string',
'LabelingJobOutput': {
'OutputDatasetS3Uri': 'string',
'FinalActiveLearningModelArn': 'string'
},
'InputConfig': {
'DataSource': {
'S3DataSource': {
'ManifestS3Uri': 'string'
},
'SnsDataSource': {
'SnsTopicArn': 'string'
}
},
'DataAttributes': {
'ContentClassifiers': [
'FreeOfPersonallyIdentifiableInformation'|'FreeOfAdultContent',
]
}
}
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
LabelingJobSummaryList (list) --
An array of LabelingJobSummary objects, each describing a labeling job.
(dict) --
Provides summary information about a labeling job.
LabelingJobName (string) --
The name of the labeling job.
LabelingJobArn (string) --
The Amazon Resource Name (ARN) assigned to the labeling job when it was created.
CreationTime (datetime) --
The date and time that the job was created (timestamp).
LastModifiedTime (datetime) --
The date and time that the job was last modified (timestamp).
LabelingJobStatus (string) --
The current status of the labeling job.
LabelCounters (dict) --
Counts showing the progress of the labeling job.
TotalLabeled (integer) --
The total number of objects labeled.
HumanLabeled (integer) --
The total number of objects labeled by a human worker.
MachineLabeled (integer) --
The total number of objects labeled by automated data labeling.
FailedNonRetryableError (integer) --
The total number of objects that could not be labeled due to an error.
Unlabeled (integer) --
The total number of objects not yet labeled.
WorkteamArn (string) --
The Amazon Resource Name (ARN) of the work team assigned to the job.
PreHumanTaskLambdaArn (string) --
The Amazon Resource Name (ARN) of a Lambda function. The function is run before each data object is sent to a worker.
AnnotationConsolidationLambdaArn (string) --
The Amazon Resource Name (ARN) of the Lambda function used to consolidate the annotations from individual workers into a label for a data object. For more information, see Annotation Consolidation .
FailureReason (string) --
If the LabelingJobStatus field is Failed , this field contains a description of the error.
LabelingJobOutput (dict) --
The location of the output produced by the labeling job.
OutputDatasetS3Uri (string) --
The Amazon S3 bucket location of the manifest file for labeled data.
FinalActiveLearningModelArn (string) --
The Amazon Resource Name (ARN) for the most recent Amazon SageMaker model trained as part of automated data labeling.
InputConfig (dict) --
Input configuration for the labeling job.
DataSource (dict) --
The location of the input data.
S3DataSource (dict) --
The Amazon S3 location of the input data objects.
ManifestS3Uri (string) --
The Amazon S3 location of the manifest file that describes the input data objects.
The input manifest file referenced in ManifestS3Uri must contain one of the following keys: source-ref or source . The value of the keys are interpreted as follows:
If you are a new user of Ground Truth, it is recommended you review Use an Input Manifest File in the Amazon SageMaker Developer Guide to learn how to create an input manifest file.
SnsDataSource (dict) --
An Amazon SNS data source used for streaming labeling jobs. To learn more, see Send Data to a Streaming Labeling Job .
SnsTopicArn (string) --
The Amazon SNS input topic Amazon Resource Name (ARN). Specify the ARN of the input topic you will use to send new data objects to a streaming labeling job.
DataAttributes (dict) --
Attributes of the data specified by the customer.
ContentClassifiers (list) --
Declares that your content is free of personally identifiable information or adult content. Amazon SageMaker may restrict the Amazon Mechanical Turk workers that can view your task based on this information.
NextToken (string) --
If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of labeling jobs, use it in the subsequent request.
Gets a list of labeling jobs assigned to a specified work team.
See also: AWS API Documentation
Request Syntax
response = client.list_labeling_jobs_for_workteam(
WorkteamArn='string',
MaxResults=123,
NextToken='string',
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
JobReferenceCodeContains='string',
SortBy='CreationTime',
SortOrder='Ascending'|'Descending'
)
[REQUIRED]
The Amazon Resource Name (ARN) of the work team for which you want to see labeling jobs for.
dict
Response Syntax
{
'LabelingJobSummaryList': [
{
'LabelingJobName': 'string',
'JobReferenceCode': 'string',
'WorkRequesterAccountId': 'string',
'CreationTime': datetime(2015, 1, 1),
'LabelCounters': {
'HumanLabeled': 123,
'PendingHuman': 123,
'Total': 123
},
'NumberOfHumanWorkersPerDataObject': 123
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
LabelingJobSummaryList (list) --
An array of LabelingJobSummary objects, each describing a labeling job.
(dict) --
Provides summary information for a work team.
LabelingJobName (string) --
The name of the labeling job that the work team is assigned to.
JobReferenceCode (string) --
A unique identifier for a labeling job. You can use this to refer to a specific labeling job.
WorkRequesterAccountId (string) --
The Amazon Web Services account ID of the account used to start the labeling job.
CreationTime (datetime) --
The date and time that the labeling job was created.
LabelCounters (dict) --
Provides information about the progress of a labeling job.
HumanLabeled (integer) --
The total number of data objects labeled by a human worker.
PendingHuman (integer) --
The total number of data objects that need to be labeled by a human worker.
Total (integer) --
The total number of tasks in the labeling job.
NumberOfHumanWorkersPerDataObject (integer) --
The configured number of workers per data object.
NextToken (string) --
If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of labeling jobs, use it in the subsequent request.
Exceptions
Lists model bias jobs definitions that satisfy various filters.
See also: AWS API Documentation
Request Syntax
response = client.list_model_bias_job_definitions(
EndpointName='string',
SortBy='Name'|'CreationTime',
SortOrder='Ascending'|'Descending',
NextToken='string',
MaxResults=123,
NameContains='string',
CreationTimeBefore=datetime(2015, 1, 1),
CreationTimeAfter=datetime(2015, 1, 1)
)
dict
Response Syntax
{
'JobDefinitionSummaries': [
{
'MonitoringJobDefinitionName': 'string',
'MonitoringJobDefinitionArn': 'string',
'CreationTime': datetime(2015, 1, 1),
'EndpointName': 'string'
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
JobDefinitionSummaries (list) --
A JSON array in which each element is a summary for a model bias jobs.
(dict) --
Summary information about a monitoring job.
MonitoringJobDefinitionName (string) --
The name of the monitoring job.
MonitoringJobDefinitionArn (string) --
The Amazon Resource Name (ARN) of the monitoring job.
CreationTime (datetime) --
The time that the monitoring job was created.
EndpointName (string) --
The name of the endpoint that the job monitors.
NextToken (string) --
If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of jobs, use it in the subsequent request.
Lists model explainability job definitions that satisfy various filters.
See also: AWS API Documentation
Request Syntax
response = client.list_model_explainability_job_definitions(
EndpointName='string',
SortBy='Name'|'CreationTime',
SortOrder='Ascending'|'Descending',
NextToken='string',
MaxResults=123,
NameContains='string',
CreationTimeBefore=datetime(2015, 1, 1),
CreationTimeAfter=datetime(2015, 1, 1)
)
dict
Response Syntax
{
'JobDefinitionSummaries': [
{
'MonitoringJobDefinitionName': 'string',
'MonitoringJobDefinitionArn': 'string',
'CreationTime': datetime(2015, 1, 1),
'EndpointName': 'string'
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
JobDefinitionSummaries (list) --
A JSON array in which each element is a summary for a explainability bias jobs.
(dict) --
Summary information about a monitoring job.
MonitoringJobDefinitionName (string) --
The name of the monitoring job.
MonitoringJobDefinitionArn (string) --
The Amazon Resource Name (ARN) of the monitoring job.
CreationTime (datetime) --
The time that the monitoring job was created.
EndpointName (string) --
The name of the endpoint that the job monitors.
NextToken (string) --
If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of jobs, use it in the subsequent request.
Gets a list of the model groups in your Amazon Web Services account.
See also: AWS API Documentation
Request Syntax
response = client.list_model_package_groups(
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
MaxResults=123,
NameContains='string',
NextToken='string',
SortBy='Name'|'CreationTime',
SortOrder='Ascending'|'Descending'
)
dict
Response Syntax
{
'ModelPackageGroupSummaryList': [
{
'ModelPackageGroupName': 'string',
'ModelPackageGroupArn': 'string',
'ModelPackageGroupDescription': 'string',
'CreationTime': datetime(2015, 1, 1),
'ModelPackageGroupStatus': 'Pending'|'InProgress'|'Completed'|'Failed'|'Deleting'|'DeleteFailed'
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
ModelPackageGroupSummaryList (list) --
A list of summaries of the model groups in your Amazon Web Services account.
(dict) --
Summary information about a model group.
ModelPackageGroupName (string) --
The name of the model group.
ModelPackageGroupArn (string) --
The Amazon Resource Name (ARN) of the model group.
ModelPackageGroupDescription (string) --
A description of the model group.
CreationTime (datetime) --
The time that the model group was created.
ModelPackageGroupStatus (string) --
The status of the model group.
NextToken (string) --
If the response is truncated, SageMaker returns this token. To retrieve the next set of model groups, use it in the subsequent request.
Lists the model packages that have been created.
See also: AWS API Documentation
Request Syntax
response = client.list_model_packages(
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
MaxResults=123,
NameContains='string',
ModelApprovalStatus='Approved'|'Rejected'|'PendingManualApproval',
ModelPackageGroupName='string',
ModelPackageType='Versioned'|'Unversioned'|'Both',
NextToken='string',
SortBy='Name'|'CreationTime',
SortOrder='Ascending'|'Descending'
)
A filter that returns onlyl the model packages of the specified type. This can be one of the following values.
dict
Response Syntax
{
'ModelPackageSummaryList': [
{
'ModelPackageName': 'string',
'ModelPackageGroupName': 'string',
'ModelPackageVersion': 123,
'ModelPackageArn': 'string',
'ModelPackageDescription': 'string',
'CreationTime': datetime(2015, 1, 1),
'ModelPackageStatus': 'Pending'|'InProgress'|'Completed'|'Failed'|'Deleting',
'ModelApprovalStatus': 'Approved'|'Rejected'|'PendingManualApproval'
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
ModelPackageSummaryList (list) --
An array of ModelPackageSummary objects, each of which lists a model package.
(dict) --
Provides summary information about a model package.
ModelPackageName (string) --
The name of the model package.
ModelPackageGroupName (string) --
If the model package is a versioned model, the model group that the versioned model belongs to.
ModelPackageVersion (integer) --
If the model package is a versioned model, the version of the model.
ModelPackageArn (string) --
The Amazon Resource Name (ARN) of the model package.
ModelPackageDescription (string) --
A brief description of the model package.
CreationTime (datetime) --
A timestamp that shows when the model package was created.
ModelPackageStatus (string) --
The overall status of the model package.
ModelApprovalStatus (string) --
The approval status of the model. This can be one of the following values.
NextToken (string) --
If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of model packages, use it in the subsequent request.
Gets a list of model quality monitoring job definitions in your account.
See also: AWS API Documentation
Request Syntax
response = client.list_model_quality_job_definitions(
EndpointName='string',
SortBy='Name'|'CreationTime',
SortOrder='Ascending'|'Descending',
NextToken='string',
MaxResults=123,
NameContains='string',
CreationTimeBefore=datetime(2015, 1, 1),
CreationTimeAfter=datetime(2015, 1, 1)
)
dict
Response Syntax
{
'JobDefinitionSummaries': [
{
'MonitoringJobDefinitionName': 'string',
'MonitoringJobDefinitionArn': 'string',
'CreationTime': datetime(2015, 1, 1),
'EndpointName': 'string'
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
JobDefinitionSummaries (list) --
A list of summaries of model quality monitoring job definitions.
(dict) --
Summary information about a monitoring job.
MonitoringJobDefinitionName (string) --
The name of the monitoring job.
MonitoringJobDefinitionArn (string) --
The Amazon Resource Name (ARN) of the monitoring job.
CreationTime (datetime) --
The time that the monitoring job was created.
EndpointName (string) --
The name of the endpoint that the job monitors.
NextToken (string) --
If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of model quality monitoring job definitions, use it in the next request.
Lists models created with the CreateModel API.
See also: AWS API Documentation
Request Syntax
response = client.list_models(
SortBy='Name'|'CreationTime',
SortOrder='Ascending'|'Descending',
NextToken='string',
MaxResults=123,
NameContains='string',
CreationTimeBefore=datetime(2015, 1, 1),
CreationTimeAfter=datetime(2015, 1, 1)
)
dict
Response Syntax
{
'Models': [
{
'ModelName': 'string',
'ModelArn': 'string',
'CreationTime': datetime(2015, 1, 1)
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
Models (list) --
An array of ModelSummary objects, each of which lists a model.
(dict) --
Provides summary information about a model.
ModelName (string) --
The name of the model that you want a summary for.
ModelArn (string) --
The Amazon Resource Name (ARN) of the model.
CreationTime (datetime) --
A timestamp that indicates when the model was created.
NextToken (string) --
If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of models, use it in the subsequent request.
Returns list of all monitoring job executions.
See also: AWS API Documentation
Request Syntax
response = client.list_monitoring_executions(
MonitoringScheduleName='string',
EndpointName='string',
SortBy='CreationTime'|'ScheduledTime'|'Status',
SortOrder='Ascending'|'Descending',
NextToken='string',
MaxResults=123,
ScheduledTimeBefore=datetime(2015, 1, 1),
ScheduledTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
CreationTimeAfter=datetime(2015, 1, 1),
LastModifiedTimeBefore=datetime(2015, 1, 1),
LastModifiedTimeAfter=datetime(2015, 1, 1),
StatusEquals='Pending'|'Completed'|'CompletedWithViolations'|'InProgress'|'Failed'|'Stopping'|'Stopped',
MonitoringJobDefinitionName='string',
MonitoringTypeEquals='DataQuality'|'ModelQuality'|'ModelBias'|'ModelExplainability'
)
dict
Response Syntax
{
'MonitoringExecutionSummaries': [
{
'MonitoringScheduleName': 'string',
'ScheduledTime': datetime(2015, 1, 1),
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'MonitoringExecutionStatus': 'Pending'|'Completed'|'CompletedWithViolations'|'InProgress'|'Failed'|'Stopping'|'Stopped',
'ProcessingJobArn': 'string',
'EndpointName': 'string',
'FailureReason': 'string',
'MonitoringJobDefinitionName': 'string',
'MonitoringType': 'DataQuality'|'ModelQuality'|'ModelBias'|'ModelExplainability'
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
MonitoringExecutionSummaries (list) --
A JSON array in which each element is a summary for a monitoring execution.
(dict) --
Summary of information about the last monitoring job to run.
MonitoringScheduleName (string) --
The name of the monitoring schedule.
ScheduledTime (datetime) --
The time the monitoring job was scheduled.
CreationTime (datetime) --
The time at which the monitoring job was created.
LastModifiedTime (datetime) --
A timestamp that indicates the last time the monitoring job was modified.
MonitoringExecutionStatus (string) --
The status of the monitoring job.
ProcessingJobArn (string) --
The Amazon Resource Name (ARN) of the monitoring job.
EndpointName (string) --
The name of the endpoint used to run the monitoring job.
FailureReason (string) --
Contains the reason a monitoring job failed, if it failed.
MonitoringJobDefinitionName (string) --
The name of the monitoring job.
MonitoringType (string) --
The type of the monitoring job.
NextToken (string) --
If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of jobs, use it in the subsequent reques
Returns list of all monitoring schedules.
See also: AWS API Documentation
Request Syntax
response = client.list_monitoring_schedules(
EndpointName='string',
SortBy='Name'|'CreationTime'|'Status',
SortOrder='Ascending'|'Descending',
NextToken='string',
MaxResults=123,
NameContains='string',
CreationTimeBefore=datetime(2015, 1, 1),
CreationTimeAfter=datetime(2015, 1, 1),
LastModifiedTimeBefore=datetime(2015, 1, 1),
LastModifiedTimeAfter=datetime(2015, 1, 1),
StatusEquals='Pending'|'Failed'|'Scheduled'|'Stopped',
MonitoringJobDefinitionName='string',
MonitoringTypeEquals='DataQuality'|'ModelQuality'|'ModelBias'|'ModelExplainability'
)
dict
Response Syntax
{
'MonitoringScheduleSummaries': [
{
'MonitoringScheduleName': 'string',
'MonitoringScheduleArn': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'MonitoringScheduleStatus': 'Pending'|'Failed'|'Scheduled'|'Stopped',
'EndpointName': 'string',
'MonitoringJobDefinitionName': 'string',
'MonitoringType': 'DataQuality'|'ModelQuality'|'ModelBias'|'ModelExplainability'
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
MonitoringScheduleSummaries (list) --
A JSON array in which each element is a summary for a monitoring schedule.
(dict) --
Summarizes the monitoring schedule.
MonitoringScheduleName (string) --
The name of the monitoring schedule.
MonitoringScheduleArn (string) --
The Amazon Resource Name (ARN) of the monitoring schedule.
CreationTime (datetime) --
The creation time of the monitoring schedule.
LastModifiedTime (datetime) --
The last time the monitoring schedule was modified.
MonitoringScheduleStatus (string) --
The status of the monitoring schedule.
EndpointName (string) --
The name of the endpoint using the monitoring schedule.
MonitoringJobDefinitionName (string) --
The name of the monitoring job definition that the schedule is for.
MonitoringType (string) --
The type of the monitoring job definition that the schedule is for.
NextToken (string) --
If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of jobs, use it in the subsequent request.
Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.
See also: AWS API Documentation
Request Syntax
response = client.list_notebook_instance_lifecycle_configs(
NextToken='string',
MaxResults=123,
SortBy='Name'|'CreationTime'|'LastModifiedTime',
SortOrder='Ascending'|'Descending',
NameContains='string',
CreationTimeBefore=datetime(2015, 1, 1),
CreationTimeAfter=datetime(2015, 1, 1),
LastModifiedTimeBefore=datetime(2015, 1, 1),
LastModifiedTimeAfter=datetime(2015, 1, 1)
)
dict
Response Syntax
{
'NextToken': 'string',
'NotebookInstanceLifecycleConfigs': [
{
'NotebookInstanceLifecycleConfigName': 'string',
'NotebookInstanceLifecycleConfigArn': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1)
},
]
}
Response Structure
(dict) --
NextToken (string) --
If the response is truncated, Amazon SageMaker returns this token. To get the next set of lifecycle configurations, use it in the next request.
NotebookInstanceLifecycleConfigs (list) --
An array of NotebookInstanceLifecycleConfiguration objects, each listing a lifecycle configuration.
(dict) --
Provides a summary of a notebook instance lifecycle configuration.
NotebookInstanceLifecycleConfigName (string) --
The name of the lifecycle configuration.
NotebookInstanceLifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the lifecycle configuration.
CreationTime (datetime) --
A timestamp that tells when the lifecycle configuration was created.
LastModifiedTime (datetime) --
A timestamp that tells when the lifecycle configuration was last modified.
Returns a list of the Amazon SageMaker notebook instances in the requester's account in an Amazon Web Services Region.
See also: AWS API Documentation
Request Syntax
response = client.list_notebook_instances(
NextToken='string',
MaxResults=123,
SortBy='Name'|'CreationTime'|'Status',
SortOrder='Ascending'|'Descending',
NameContains='string',
CreationTimeBefore=datetime(2015, 1, 1),
CreationTimeAfter=datetime(2015, 1, 1),
LastModifiedTimeBefore=datetime(2015, 1, 1),
LastModifiedTimeAfter=datetime(2015, 1, 1),
StatusEquals='Pending'|'InService'|'Stopping'|'Stopped'|'Failed'|'Deleting'|'Updating',
NotebookInstanceLifecycleConfigNameContains='string',
DefaultCodeRepositoryContains='string',
AdditionalCodeRepositoryEquals='string'
)
If the previous call to the ListNotebookInstances is truncated, the response includes a NextToken . You can use this token in your subsequent ListNotebookInstances request to fetch the next set of notebook instances.
Note
You might specify a filter or a sort order in your request. When response is truncated, you must use the same values for the filer and sort order in the next request.
dict
Response Syntax
{
'NextToken': 'string',
'NotebookInstances': [
{
'NotebookInstanceName': 'string',
'NotebookInstanceArn': 'string',
'NotebookInstanceStatus': 'Pending'|'InService'|'Stopping'|'Stopped'|'Failed'|'Deleting'|'Updating',
'Url': 'string',
'InstanceType': 'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'NotebookInstanceLifecycleConfigName': 'string',
'DefaultCodeRepository': 'string',
'AdditionalCodeRepositories': [
'string',
]
},
]
}
Response Structure
(dict) --
NextToken (string) --
If the response to the previous ListNotebookInstances request was truncated, Amazon SageMaker returns this token. To retrieve the next set of notebook instances, use the token in the next request.
NotebookInstances (list) --
An array of NotebookInstanceSummary objects, one for each notebook instance.
(dict) --
Provides summary information for an Amazon SageMaker notebook instance.
NotebookInstanceName (string) --
The name of the notebook instance that you want a summary for.
NotebookInstanceArn (string) --
The Amazon Resource Name (ARN) of the notebook instance.
NotebookInstanceStatus (string) --
The status of the notebook instance.
Url (string) --
The URL that you use to connect to the Jupyter instance running in your notebook instance.
InstanceType (string) --
The type of ML compute instance that the notebook instance is running on.
CreationTime (datetime) --
A timestamp that shows when the notebook instance was created.
LastModifiedTime (datetime) --
A timestamp that shows when the notebook instance was last modified.
NotebookInstanceLifecycleConfigName (string) --
The name of a notebook instance lifecycle configuration associated with this notebook instance.
For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance .
DefaultCodeRepository (string) --
The Git repository associated with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in Amazon Web Services CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances .
AdditionalCodeRepositories (list) --
An array of up to three Git repositories associated with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in Amazon Web Services CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances .
Gets a list of PipeLineExecutionStep objects.
See also: AWS API Documentation
Request Syntax
response = client.list_pipeline_execution_steps(
PipelineExecutionArn='string',
NextToken='string',
MaxResults=123,
SortOrder='Ascending'|'Descending'
)
dict
Response Syntax
{
'PipelineExecutionSteps': [
{
'StepName': 'string',
'StartTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1),
'StepStatus': 'Starting'|'Executing'|'Stopping'|'Stopped'|'Failed'|'Succeeded',
'CacheHitResult': {
'SourcePipelineExecutionArn': 'string'
},
'FailureReason': 'string',
'Metadata': {
'TrainingJob': {
'Arn': 'string'
},
'ProcessingJob': {
'Arn': 'string'
},
'TransformJob': {
'Arn': 'string'
},
'TuningJob': {
'Arn': 'string'
},
'Model': {
'Arn': 'string'
},
'RegisterModel': {
'Arn': 'string'
},
'Condition': {
'Outcome': 'True'|'False'
},
'Callback': {
'CallbackToken': 'string',
'SqsQueueUrl': 'string',
'OutputParameters': [
{
'Name': 'string',
'Value': 'string'
},
]
}
}
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
PipelineExecutionSteps (list) --
A list of PipeLineExecutionStep objects. Each PipeLineExecutionStep consists of StepName, StartTime, EndTime, StepStatus, and Metadata. Metadata is an object with properties for each job that contains relevant information about the job created by the step.
(dict) --
An execution of a step in a pipeline.
StepName (string) --
The name of the step that is executed.
StartTime (datetime) --
The time that the step started executing.
EndTime (datetime) --
The time that the step stopped executing.
StepStatus (string) --
The status of the step execution.
CacheHitResult (dict) --
If this pipeline execution step was cached, details on the cache hit.
SourcePipelineExecutionArn (string) --
The Amazon Resource Name (ARN) of the pipeline execution.
FailureReason (string) --
The reason why the step failed execution. This is only returned if the step failed its execution.
Metadata (dict) --
Metadata for the step execution.
TrainingJob (dict) --
The Amazon Resource Name (ARN) of the training job that was run by this step execution.
Arn (string) --
The Amazon Resource Name (ARN) of the training job that was run by this step execution.
ProcessingJob (dict) --
The Amazon Resource Name (ARN) of the processing job that was run by this step execution.
Arn (string) --
The Amazon Resource Name (ARN) of the processing job.
TransformJob (dict) --
The Amazon Resource Name (ARN) of the transform job that was run by this step execution.
Arn (string) --
The Amazon Resource Name (ARN) of the transform job that was run by this step execution.
TuningJob (dict) --
The Amazon Resource Name (ARN) of the tuning job that was run by this step execution.
Arn (string) --
The Amazon Resource Name (ARN) of the tuning job that was run by this step execution.
Model (dict) --
Metadata for the Model step.
Arn (string) --
The Amazon Resource Name (ARN) of the created model.
RegisterModel (dict) --
Metadata for the RegisterModel step.
Arn (string) --
The Amazon Resource Name (ARN) of the model package.
Condition (dict) --
If this is a Condition step metadata object, details on the condition.
Outcome (string) --
The outcome of the Condition step evaluation.
Callback (dict) --
Metadata about a callback step.
CallbackToken (string) --
The pipeline generated token from the Amazon SQS queue.
SqsQueueUrl (string) --
The URL of the Amazon Simple Queue Service (Amazon SQS) queue used by the callback step.
OutputParameters (list) --
A list of the output parameters of the callback step.
(dict) --
An output parameter of a pipeline step.
Name (string) --
The name of the output parameter.
Value (string) --
The value of the output parameter.
NextToken (string) --
If the result of the previous ListPipelineExecutionSteps request was truncated, the response includes a NextToken . To retrieve the next set of pipeline execution steps, use the token in the next request.
Exceptions
Gets a list of the pipeline executions.
See also: AWS API Documentation
Request Syntax
response = client.list_pipeline_executions(
PipelineName='string',
CreatedAfter=datetime(2015, 1, 1),
CreatedBefore=datetime(2015, 1, 1),
SortBy='CreationTime'|'PipelineExecutionArn',
SortOrder='Ascending'|'Descending',
NextToken='string',
MaxResults=123
)
[REQUIRED]
The name of the pipeline.
dict
Response Syntax
{
'PipelineExecutionSummaries': [
{
'PipelineExecutionArn': 'string',
'StartTime': datetime(2015, 1, 1),
'PipelineExecutionStatus': 'Executing'|'Stopping'|'Stopped'|'Failed'|'Succeeded',
'PipelineExecutionDescription': 'string',
'PipelineExecutionDisplayName': 'string'
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
PipelineExecutionSummaries (list) --
Contains a sorted list of pipeline execution summary objects matching the specified filters. Each run summary includes the Amazon Resource Name (ARN) of the pipeline execution, the run date, and the status. This list can be empty.
(dict) --
A pipeline execution summary.
PipelineExecutionArn (string) --
The Amazon Resource Name (ARN) of the pipeline execution.
StartTime (datetime) --
The start time of the pipeline execution.
PipelineExecutionStatus (string) --
The status of the pipeline execution.
PipelineExecutionDescription (string) --
The description of the pipeline execution.
PipelineExecutionDisplayName (string) --
The display name of the pipeline execution.
NextToken (string) --
If the result of the previous ListPipelineExecutions request was truncated, the response includes a NextToken . To retrieve the next set of pipeline executions, use the token in the next request.
Exceptions
Gets a list of parameters for a pipeline execution.
See also: AWS API Documentation
Request Syntax
response = client.list_pipeline_parameters_for_execution(
PipelineExecutionArn='string',
NextToken='string',
MaxResults=123
)
[REQUIRED]
The Amazon Resource Name (ARN) of the pipeline execution.
dict
Response Syntax
{
'PipelineParameters': [
{
'Name': 'string',
'Value': 'string'
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
PipelineParameters (list) --
Contains a list of pipeline parameters. This list can be empty.
(dict) --
Assigns a value to a named Pipeline parameter.
Name (string) --
The name of the parameter to assign a value to. This parameter name must match a named parameter in the pipeline definition.
Value (string) --
The literal value for the parameter.
NextToken (string) --
If the result of the previous ListPipelineParametersForExecution request was truncated, the response includes a NextToken . To retrieve the next set of parameters, use the token in the next request.
Exceptions
Gets a list of pipelines.
See also: AWS API Documentation
Request Syntax
response = client.list_pipelines(
PipelineNamePrefix='string',
CreatedAfter=datetime(2015, 1, 1),
CreatedBefore=datetime(2015, 1, 1),
SortBy='Name'|'CreationTime',
SortOrder='Ascending'|'Descending',
NextToken='string',
MaxResults=123
)
dict
Response Syntax
{
'PipelineSummaries': [
{
'PipelineArn': 'string',
'PipelineName': 'string',
'PipelineDisplayName': 'string',
'PipelineDescription': 'string',
'RoleArn': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'LastExecutionTime': datetime(2015, 1, 1)
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
PipelineSummaries (list) --
Contains a sorted list of PipelineSummary objects matching the specified filters. Each PipelineSummary consists of PipelineArn, PipelineName, ExperimentName, PipelineDescription, CreationTime, LastModifiedTime, LastRunTime, and RoleArn. This list can be empty.
(dict) --
A summary of a pipeline.
PipelineArn (string) --
The Amazon Resource Name (ARN) of the pipeline.
PipelineName (string) --
The name of the pipeline.
PipelineDisplayName (string) --
The display name of the pipeline.
PipelineDescription (string) --
The description of the pipeline.
RoleArn (string) --
The Amazon Resource Name (ARN) that the pipeline used to execute.
CreationTime (datetime) --
The creation time of the pipeline.
LastModifiedTime (datetime) --
The time that the pipeline was last modified.
LastExecutionTime (datetime) --
The last time that a pipeline execution began.
NextToken (string) --
If the result of the previous ListPipelines request was truncated, the response includes a NextToken . To retrieve the next set of pipelines, use the token in the next request.
Lists processing jobs that satisfy various filters.
See also: AWS API Documentation
Request Syntax
response = client.list_processing_jobs(
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
LastModifiedTimeAfter=datetime(2015, 1, 1),
LastModifiedTimeBefore=datetime(2015, 1, 1),
NameContains='string',
StatusEquals='InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
SortBy='Name'|'CreationTime'|'Status',
SortOrder='Ascending'|'Descending',
NextToken='string',
MaxResults=123
)
dict
Response Syntax
{
'ProcessingJobSummaries': [
{
'ProcessingJobName': 'string',
'ProcessingJobArn': 'string',
'CreationTime': datetime(2015, 1, 1),
'ProcessingEndTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'ProcessingJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'FailureReason': 'string',
'ExitMessage': 'string'
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
ProcessingJobSummaries (list) --
An array of ProcessingJobSummary objects, each listing a processing job.
(dict) --
Summary of information about a processing job.
ProcessingJobName (string) --
The name of the processing job.
ProcessingJobArn (string) --
The Amazon Resource Name (ARN) of the processing job..
CreationTime (datetime) --
The time at which the processing job was created.
ProcessingEndTime (datetime) --
The time at which the processing job completed.
LastModifiedTime (datetime) --
A timestamp that indicates the last time the processing job was modified.
ProcessingJobStatus (string) --
The status of the processing job.
FailureReason (string) --
A string, up to one KB in size, that contains the reason a processing job failed, if it failed.
ExitMessage (string) --
An optional string, up to one KB in size, that contains metadata from the processing container when the processing job exits.
NextToken (string) --
If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of processing jobs, use it in the subsequent request.
Gets a list of the projects in an Amazon Web Services account.
See also: AWS API Documentation
Request Syntax
response = client.list_projects(
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
MaxResults=123,
NameContains='string',
NextToken='string',
SortBy='Name'|'CreationTime',
SortOrder='Ascending'|'Descending'
)
dict
Response Syntax
{
'ProjectSummaryList': [
{
'ProjectName': 'string',
'ProjectDescription': 'string',
'ProjectArn': 'string',
'ProjectId': 'string',
'CreationTime': datetime(2015, 1, 1),
'ProjectStatus': 'Pending'|'CreateInProgress'|'CreateCompleted'|'CreateFailed'|'DeleteInProgress'|'DeleteFailed'|'DeleteCompleted'
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
ProjectSummaryList (list) --
A list of summaries of projects.
(dict) --
Information about a project.
ProjectName (string) --
The name of the project.
ProjectDescription (string) --
The description of the project.
ProjectArn (string) --
The Amazon Resource Name (ARN) of the project.
ProjectId (string) --
The ID of the project.
CreationTime (datetime) --
The time that the project was created.
ProjectStatus (string) --
The status of the project.
NextToken (string) --
If the result of the previous ListCompilationJobs request was truncated, the response includes a NextToken . To retrieve the next set of model compilation jobs, use the token in the next request.
Gets a list of the work teams that you are subscribed to in the Amazon Web Services Marketplace. The list may be empty if no work team satisfies the filter specified in the NameContains parameter.
See also: AWS API Documentation
Request Syntax
response = client.list_subscribed_workteams(
NameContains='string',
NextToken='string',
MaxResults=123
)
dict
Response Syntax
{
'SubscribedWorkteams': [
{
'WorkteamArn': 'string',
'MarketplaceTitle': 'string',
'SellerName': 'string',
'MarketplaceDescription': 'string',
'ListingId': 'string'
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
SubscribedWorkteams (list) --
An array of Workteam objects, each describing a work team.
(dict) --
Describes a work team of a vendor that does the a labelling job.
WorkteamArn (string) --
The Amazon Resource Name (ARN) of the vendor that you have subscribed.
MarketplaceTitle (string) --
The title of the service provided by the vendor in the Amazon Marketplace.
SellerName (string) --
The name of the vendor in the Amazon Marketplace.
MarketplaceDescription (string) --
The description of the vendor from the Amazon Marketplace.
ListingId (string) --
Marketplace product listing ID.
NextToken (string) --
If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of work teams, use it in the subsequent request.
Returns the tags for the specified Amazon SageMaker resource.
See also: AWS API Documentation
Request Syntax
response = client.list_tags(
ResourceArn='string',
NextToken='string',
MaxResults=123
)
[REQUIRED]
The Amazon Resource Name (ARN) of the resource whose tags you want to retrieve.
dict
Response Syntax
{
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
Tags (list) --
An array of Tag objects, each with a tag key and a value.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
NextToken (string) --
If response is truncated, Amazon SageMaker includes a token in the response. You can use this token in your subsequent request to fetch next set of tokens.
Lists training jobs.
Note
When StatusEquals and MaxResults are set at the same time, the MaxResults number of training jobs are first retrieved ignoring the StatusEquals parameter and then they are filtered by the StatusEquals parameter, which is returned as a response.
For example, if ListTrainingJobs is invoked with the following parameters:
{ ... MaxResults: 100, StatusEquals: InProgress ... }
First, 100 trainings jobs with any status, including those other than InProgress , are selected (sorted according to the creation time, from the most current to the oldest). Next, those with a status of InProgress are returned.
You can quickly test the API using the following Amazon Web Services CLI code.
aws sagemaker list-training-jobs --max-results 100 --status-equals InProgress
See also: AWS API Documentation
Request Syntax
response = client.list_training_jobs(
NextToken='string',
MaxResults=123,
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
LastModifiedTimeAfter=datetime(2015, 1, 1),
LastModifiedTimeBefore=datetime(2015, 1, 1),
NameContains='string',
StatusEquals='InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
SortBy='Name'|'CreationTime'|'Status',
SortOrder='Ascending'|'Descending'
)
dict
Response Syntax
{
'TrainingJobSummaries': [
{
'TrainingJobName': 'string',
'TrainingJobArn': 'string',
'CreationTime': datetime(2015, 1, 1),
'TrainingEndTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'TrainingJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped'
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
TrainingJobSummaries (list) --
An array of TrainingJobSummary objects, each listing a training job.
(dict) --
Provides summary information about a training job.
TrainingJobName (string) --
The name of the training job that you want a summary for.
TrainingJobArn (string) --
The Amazon Resource Name (ARN) of the training job.
CreationTime (datetime) --
A timestamp that shows when the training job was created.
TrainingEndTime (datetime) --
A timestamp that shows when the training job ended. This field is set only if the training job has one of the terminal statuses (Completed , Failed , or Stopped ).
LastModifiedTime (datetime) --
Timestamp when the training job was last modified.
TrainingJobStatus (string) --
The status of the training job.
NextToken (string) --
If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of training jobs, use it in the subsequent request.
Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched.
See also: AWS API Documentation
Request Syntax
response = client.list_training_jobs_for_hyper_parameter_tuning_job(
HyperParameterTuningJobName='string',
NextToken='string',
MaxResults=123,
StatusEquals='InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
SortBy='Name'|'CreationTime'|'Status'|'FinalObjectiveMetricValue',
SortOrder='Ascending'|'Descending'
)
[REQUIRED]
The name of the tuning job whose training jobs you want to list.
The field to sort results by. The default is Name .
If the value of this field is FinalObjectiveMetricValue , any training jobs that did not return an objective metric are not listed.
dict
Response Syntax
{
'TrainingJobSummaries': [
{
'TrainingJobDefinitionName': 'string',
'TrainingJobName': 'string',
'TrainingJobArn': 'string',
'TuningJobName': 'string',
'CreationTime': datetime(2015, 1, 1),
'TrainingStartTime': datetime(2015, 1, 1),
'TrainingEndTime': datetime(2015, 1, 1),
'TrainingJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'TunedHyperParameters': {
'string': 'string'
},
'FailureReason': 'string',
'FinalHyperParameterTuningJobObjectiveMetric': {
'Type': 'Maximize'|'Minimize',
'MetricName': 'string',
'Value': ...
},
'ObjectiveStatus': 'Succeeded'|'Pending'|'Failed'
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
TrainingJobSummaries (list) --
A list of TrainingJobSummary objects that describe the training jobs that the ListTrainingJobsForHyperParameterTuningJob request returned.
(dict) --
Specifies summary information about a training job.
TrainingJobDefinitionName (string) --
The training job definition name.
TrainingJobName (string) --
The name of the training job.
TrainingJobArn (string) --
The Amazon Resource Name (ARN) of the training job.
TuningJobName (string) --
The HyperParameter tuning job that launched the training job.
CreationTime (datetime) --
The date and time that the training job was created.
TrainingStartTime (datetime) --
The date and time that the training job started.
TrainingEndTime (datetime) --
Specifies the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when Amazon SageMaker detects a job failure.
TrainingJobStatus (string) --
The status of the training job.
TunedHyperParameters (dict) --
A list of the hyperparameters for which you specified ranges to search.
FailureReason (string) --
The reason that the training job failed.
FinalHyperParameterTuningJobObjectiveMetric (dict) --
The FinalHyperParameterTuningJobObjectiveMetric object that specifies the value of the objective metric of the tuning job that launched this training job.
Type (string) --
Whether to minimize or maximize the objective metric. Valid values are Minimize and Maximize.
MetricName (string) --
The name of the objective metric.
Value (float) --
The value of the objective metric.
ObjectiveStatus (string) --
The status of the objective metric for the training job:
NextToken (string) --
If the result of this ListTrainingJobsForHyperParameterTuningJob request was truncated, the response includes a NextToken . To retrieve the next set of training jobs, use the token in the next request.
Exceptions
Lists transform jobs.
See also: AWS API Documentation
Request Syntax
response = client.list_transform_jobs(
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
LastModifiedTimeAfter=datetime(2015, 1, 1),
LastModifiedTimeBefore=datetime(2015, 1, 1),
NameContains='string',
StatusEquals='InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
SortBy='Name'|'CreationTime'|'Status',
SortOrder='Ascending'|'Descending',
NextToken='string',
MaxResults=123
)
dict
Response Syntax
{
'TransformJobSummaries': [
{
'TransformJobName': 'string',
'TransformJobArn': 'string',
'CreationTime': datetime(2015, 1, 1),
'TransformEndTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'TransformJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'FailureReason': 'string'
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
TransformJobSummaries (list) --
An array of TransformJobSummary objects.
(dict) --
Provides a summary of a transform job. Multiple TransformJobSummary objects are returned as a list after in response to a ListTransformJobs call.
TransformJobName (string) --
The name of the transform job.
TransformJobArn (string) --
The Amazon Resource Name (ARN) of the transform job.
CreationTime (datetime) --
A timestamp that shows when the transform Job was created.
TransformEndTime (datetime) --
Indicates when the transform job ends on compute instances. For successful jobs and stopped jobs, this is the exact time recorded after the results are uploaded. For failed jobs, this is when Amazon SageMaker detected that the job failed.
LastModifiedTime (datetime) --
Indicates when the transform job was last modified.
TransformJobStatus (string) --
The status of the transform job.
FailureReason (string) --
If the transform job failed, the reason it failed.
NextToken (string) --
If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of transform jobs, use it in the next request.
Lists the trial components in your account. You can sort the list by trial component name or creation time. You can filter the list to show only components that were created in a specific time range. You can also filter on one of the following:
See also: AWS API Documentation
Request Syntax
response = client.list_trial_components(
ExperimentName='string',
TrialName='string',
SourceArn='string',
CreatedAfter=datetime(2015, 1, 1),
CreatedBefore=datetime(2015, 1, 1),
SortBy='Name'|'CreationTime',
SortOrder='Ascending'|'Descending',
MaxResults=123,
NextToken='string'
)
dict
Response Syntax
{
'TrialComponentSummaries': [
{
'TrialComponentName': 'string',
'TrialComponentArn': 'string',
'DisplayName': 'string',
'TrialComponentSource': {
'SourceArn': 'string',
'SourceType': 'string'
},
'Status': {
'PrimaryStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'Message': 'string'
},
'StartTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1),
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'LastModifiedTime': datetime(2015, 1, 1),
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
}
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
TrialComponentSummaries (list) --
A list of the summaries of your trial components.
(dict) --
A summary of the properties of a trial component. To get all the properties, call the DescribeTrialComponent API and provide the TrialComponentName .
TrialComponentName (string) --
The name of the trial component.
TrialComponentArn (string) --
The ARN of the trial component.
DisplayName (string) --
The name of the component as displayed. If DisplayName isn't specified, TrialComponentName is displayed.
TrialComponentSource (dict) --
The Amazon Resource Name (ARN) and job type of the source of a trial component.
SourceArn (string) --
The source ARN.
SourceType (string) --
The source job type.
Status (dict) --
The status of the component. States include:
PrimaryStatus (string) --
The status of the trial component.
Message (string) --
If the component failed, a message describing why.
StartTime (datetime) --
When the component started.
EndTime (datetime) --
When the component ended.
CreationTime (datetime) --
When the component was created.
CreatedBy (dict) --
Who created the component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
LastModifiedTime (datetime) --
When the component was last modified.
LastModifiedBy (dict) --
Who last modified the component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
NextToken (string) --
A token for getting the next set of components, if there are any.
Exceptions
Lists the trials in your account. Specify an experiment name to limit the list to the trials that are part of that experiment. Specify a trial component name to limit the list to the trials that associated with that trial component. The list can be filtered to show only trials that were created in a specific time range. The list can be sorted by trial name or creation time.
See also: AWS API Documentation
Request Syntax
response = client.list_trials(
ExperimentName='string',
TrialComponentName='string',
CreatedAfter=datetime(2015, 1, 1),
CreatedBefore=datetime(2015, 1, 1),
SortBy='Name'|'CreationTime',
SortOrder='Ascending'|'Descending',
MaxResults=123,
NextToken='string'
)
dict
Response Syntax
{
'TrialSummaries': [
{
'TrialArn': 'string',
'TrialName': 'string',
'DisplayName': 'string',
'TrialSource': {
'SourceArn': 'string',
'SourceType': 'string'
},
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1)
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
TrialSummaries (list) --
A list of the summaries of your trials.
(dict) --
A summary of the properties of a trial. To get the complete set of properties, call the DescribeTrial API and provide the TrialName .
TrialArn (string) --
The Amazon Resource Name (ARN) of the trial.
TrialName (string) --
The name of the trial.
DisplayName (string) --
The name of the trial as displayed. If DisplayName isn't specified, TrialName is displayed.
TrialSource (dict) --
The source of the trial.
SourceArn (string) --
The Amazon Resource Name (ARN) of the source.
SourceType (string) --
The source job type.
CreationTime (datetime) --
When the trial was created.
LastModifiedTime (datetime) --
When the trial was last modified.
NextToken (string) --
A token for getting the next set of trials, if there are any.
Exceptions
Lists user profiles.
See also: AWS API Documentation
Request Syntax
response = client.list_user_profiles(
NextToken='string',
MaxResults=123,
SortOrder='Ascending'|'Descending',
SortBy='CreationTime'|'LastModifiedTime',
DomainIdEquals='string',
UserProfileNameContains='string'
)
dict
Response Syntax
{
'UserProfiles': [
{
'DomainId': 'string',
'UserProfileName': 'string',
'Status': 'Deleting'|'Failed'|'InService'|'Pending'|'Updating'|'Update_Failed'|'Delete_Failed',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1)
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
UserProfiles (list) --
The list of user profiles.
(dict) --
The user profile details.
DomainId (string) --
The domain ID.
UserProfileName (string) --
The user profile name.
Status (string) --
The status.
CreationTime (datetime) --
The creation time.
LastModifiedTime (datetime) --
The last modified time.
NextToken (string) --
If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
Use this operation to list all private and vendor workforces in an Amazon Web Services Region. Note that you can only have one private workforce per Amazon Web Services Region.
See also: AWS API Documentation
Request Syntax
response = client.list_workforces(
SortBy='Name'|'CreateDate',
SortOrder='Ascending'|'Descending',
NameContains='string',
NextToken='string',
MaxResults=123
)
dict
Response Syntax
{
'Workforces': [
{
'WorkforceName': 'string',
'WorkforceArn': 'string',
'LastUpdatedDate': datetime(2015, 1, 1),
'SourceIpConfig': {
'Cidrs': [
'string',
]
},
'SubDomain': 'string',
'CognitoConfig': {
'UserPool': 'string',
'ClientId': 'string'
},
'OidcConfig': {
'ClientId': 'string',
'Issuer': 'string',
'AuthorizationEndpoint': 'string',
'TokenEndpoint': 'string',
'UserInfoEndpoint': 'string',
'LogoutEndpoint': 'string',
'JwksUri': 'string'
},
'CreateDate': datetime(2015, 1, 1)
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
Workforces (list) --
A list containing information about your workforce.
(dict) --
A single private workforce, which is automatically created when you create your first private work team. You can create one private work force in each Amazon Web Services Region. By default, any workforce-related API operation used in a specific region will apply to the workforce created in that region. To learn how to create a private workforce, see Create a Private Workforce .
WorkforceName (string) --
The name of the private workforce.
WorkforceArn (string) --
The Amazon Resource Name (ARN) of the private workforce.
LastUpdatedDate (datetime) --
The most recent date that was used to successfully add one or more IP address ranges (CIDRs ) to a private workforce's allow list.
SourceIpConfig (dict) --
A list of one to ten IP address ranges (CIDRs ) to be added to the workforce allow list. By default, a workforce isn't restricted to specific IP addresses.
Cidrs (list) --
A list of one to ten Classless Inter-Domain Routing (CIDR) values.
Maximum: Ten CIDR values
Note
The following Length Constraints apply to individual CIDR values in the CIDR value list.
SubDomain (string) --
The subdomain for your OIDC Identity Provider.
CognitoConfig (dict) --
The configuration of an Amazon Cognito workforce. A single Cognito workforce is created using and corresponds to a single Amazon Cognito user pool .
UserPool (string) --
A user pool is a user directory in Amazon Cognito. With a user pool, your users can sign in to your web or mobile app through Amazon Cognito. Your users can also sign in through social identity providers like Google, Facebook, Amazon, or Apple, and through SAML identity providers.
ClientId (string) --
The client ID for your Amazon Cognito user pool.
OidcConfig (dict) --
The configuration of an OIDC Identity Provider (IdP) private workforce.
ClientId (string) --
The OIDC IdP client ID used to configure your private workforce.
Issuer (string) --
The OIDC IdP issuer used to configure your private workforce.
AuthorizationEndpoint (string) --
The OIDC IdP authorization endpoint used to configure your private workforce.
TokenEndpoint (string) --
The OIDC IdP token endpoint used to configure your private workforce.
UserInfoEndpoint (string) --
The OIDC IdP user information endpoint used to configure your private workforce.
LogoutEndpoint (string) --
The OIDC IdP logout endpoint used to configure your private workforce.
JwksUri (string) --
The OIDC IdP JSON Web Key Set (Jwks) URI used to configure your private workforce.
CreateDate (datetime) --
The date that the workforce is created.
NextToken (string) --
A token to resume pagination.
Gets a list of private work teams that you have defined in a region. The list may be empty if no work team satisfies the filter specified in the NameContains parameter.
See also: AWS API Documentation
Request Syntax
response = client.list_workteams(
SortBy='Name'|'CreateDate',
SortOrder='Ascending'|'Descending',
NameContains='string',
NextToken='string',
MaxResults=123
)
dict
Response Syntax
{
'Workteams': [
{
'WorkteamName': 'string',
'MemberDefinitions': [
{
'CognitoMemberDefinition': {
'UserPool': 'string',
'UserGroup': 'string',
'ClientId': 'string'
},
'OidcMemberDefinition': {
'Groups': [
'string',
]
}
},
],
'WorkteamArn': 'string',
'WorkforceArn': 'string',
'ProductListingIds': [
'string',
],
'Description': 'string',
'SubDomain': 'string',
'CreateDate': datetime(2015, 1, 1),
'LastUpdatedDate': datetime(2015, 1, 1),
'NotificationConfiguration': {
'NotificationTopicArn': 'string'
}
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
Workteams (list) --
An array of Workteam objects, each describing a work team.
(dict) --
Provides details about a labeling work team.
WorkteamName (string) --
The name of the work team.
MemberDefinitions (list) --
A list of MemberDefinition objects that contains objects that identify the workers that make up the work team.
Workforces can be created using Amazon Cognito or your own OIDC Identity Provider (IdP). For private workforces created using Amazon Cognito use CognitoMemberDefinition . For workforces created using your own OIDC identity provider (IdP) use OidcMemberDefinition .
(dict) --
Defines an Amazon Cognito or your own OIDC IdP user group that is part of a work team.
CognitoMemberDefinition (dict) --
The Amazon Cognito user group that is part of the work team.
UserPool (string) --
An identifier for a user pool. The user pool must be in the same region as the service that you are calling.
UserGroup (string) --
An identifier for a user group.
ClientId (string) --
An identifier for an application client. You must create the app client ID using Amazon Cognito.
OidcMemberDefinition (dict) --
A list user groups that exist in your OIDC Identity Provider (IdP). One to ten groups can be used to create a single private work team. When you add a user group to the list of Groups , you can add that user group to one or more private work teams. If you add a user group to a private work team, all workers in that user group are added to the work team.
Groups (list) --
A list of comma seperated strings that identifies user groups in your OIDC IdP. Each user group is made up of a group of private workers.
WorkteamArn (string) --
The Amazon Resource Name (ARN) that identifies the work team.
WorkforceArn (string) --
The Amazon Resource Name (ARN) of the workforce.
ProductListingIds (list) --
The Amazon Marketplace identifier for a vendor's work team.
Description (string) --
A description of the work team.
SubDomain (string) --
The URI of the labeling job's user interface. Workers open this URI to start labeling your data objects.
CreateDate (datetime) --
The date and time that the work team was created (timestamp).
LastUpdatedDate (datetime) --
The date and time that the work team was last updated (timestamp).
NotificationConfiguration (dict) --
Configures SNS notifications of available or expiring work items for work teams.
NotificationTopicArn (string) --
The ARN for the SNS topic to which notifications should be published.
NextToken (string) --
If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of work teams, use it in the subsequent request.
Adds a resouce policy to control access to a model group. For information about resoure policies, see Identity-based policies and resource-based policies in the Amazon Web Services Identity and Access Management User Guide. .
See also: AWS API Documentation
Request Syntax
response = client.put_model_package_group_policy(
ModelPackageGroupName='string',
ResourcePolicy='string'
)
[REQUIRED]
The name of the model group to add a resource policy to.
[REQUIRED]
The resource policy for the model group.
dict
Response Syntax
{
'ModelPackageGroupArn': 'string'
}
Response Structure
(dict) --
ModelPackageGroupArn (string) --
The Amazon Resource Name (ARN) of the model package group.
Register devices.
See also: AWS API Documentation
Request Syntax
response = client.register_devices(
DeviceFleetName='string',
Devices=[
{
'DeviceName': 'string',
'Description': 'string',
'IotThingName': 'string'
},
],
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
[REQUIRED]
The name of the fleet.
[REQUIRED]
A list of devices to register with SageMaker Edge Manager.
Information of a particular device.
The name of the device.
Description of the device.
Amazon Web Services Internet of Things (IoT) object name.
The tags associated with devices.
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
The tag key. Tag keys must be unique per resource.
The tag value.
None
Exceptions
Renders the UI template so that you can preview the worker's experience.
See also: AWS API Documentation
Request Syntax
response = client.render_ui_template(
UiTemplate={
'Content': 'string'
},
Task={
'Input': 'string'
},
RoleArn='string',
HumanTaskUiArn='string'
)
A Template object containing the worker UI template to render.
The content of the Liquid template for the worker user interface.
[REQUIRED]
A RenderableTask object containing a representative task to render.
A JSON object that contains values for the variables defined in the template. It is made available to the template under the substitution variable task.input . For example, if you define a variable task.input.text in your template, you can supply the variable in the JSON object as "text": "sample text" .
[REQUIRED]
The Amazon Resource Name (ARN) that has access to the S3 objects that are used by the template.
The HumanTaskUiArn of the worker UI that you want to render. Do not provide a HumanTaskUiArn if you use the UiTemplate parameter.
See a list of available Human Ui Amazon Resource Names (ARNs) in UiConfig .
dict
Response Syntax
{
'RenderedContent': 'string',
'Errors': [
{
'Code': 'string',
'Message': 'string'
},
]
}
Response Structure
(dict) --
RenderedContent (string) --
A Liquid template that renders the HTML for the worker UI.
Errors (list) --
A list of one or more RenderingError objects if any were encountered while rendering the template. If there were no errors, the list is empty.
(dict) --
A description of an error that occurred while rendering the template.
Code (string) --
A unique identifier for a specific class of errors.
Message (string) --
A human-readable message describing the error.
Exceptions
Finds Amazon SageMaker resources that match a search query. Matching resources are returned as a list of SearchRecord objects in the response. You can sort the search results by any resource property in a ascending or descending order.
You can query against the following value types: numeric, text, Boolean, and timestamp.
See also: AWS API Documentation
Request Syntax
response = client.search(
Resource='TrainingJob'|'Experiment'|'ExperimentTrial'|'ExperimentTrialComponent'|'Endpoint'|'ModelPackage'|'ModelPackageGroup'|'Pipeline'|'PipelineExecution'|'FeatureGroup',
SearchExpression={
'Filters': [
{
'Name': 'string',
'Operator': 'Equals'|'NotEquals'|'GreaterThan'|'GreaterThanOrEqualTo'|'LessThan'|'LessThanOrEqualTo'|'Contains'|'Exists'|'NotExists'|'In',
'Value': 'string'
},
],
'NestedFilters': [
{
'NestedPropertyName': 'string',
'Filters': [
{
'Name': 'string',
'Operator': 'Equals'|'NotEquals'|'GreaterThan'|'GreaterThanOrEqualTo'|'LessThan'|'LessThanOrEqualTo'|'Contains'|'Exists'|'NotExists'|'In',
'Value': 'string'
},
]
},
],
'SubExpressions': [
{'... recursive ...'},
],
'Operator': 'And'|'Or'
},
SortBy='string',
SortOrder='Ascending'|'Descending',
NextToken='string',
MaxResults=123
)
[REQUIRED]
The name of the Amazon SageMaker resource to search for.
A Boolean conditional statement. Resources must satisfy this condition to be included in search results. You must provide at least one subexpression, filter, or nested filter. The maximum number of recursive SubExpressions , NestedFilters , and Filters that can be included in a SearchExpression object is 50.
A list of filter objects.
A conditional statement for a search expression that includes a resource property, a Boolean operator, and a value. Resources that match the statement are returned in the results from the Search API.
If you specify a Value , but not an Operator , Amazon SageMaker uses the equals operator.
In search, there are several property types:
Metrics
To define a metric filter, enter a value using the form "Metrics.<name>" , where <name> is a metric name. For example, the following filter searches for training jobs with an "accuracy" metric greater than "0.9" :
{"Name": "Metrics.accuracy",
"Operator": "GreaterThan",
"Value": "0.9"
}
HyperParameters
To define a hyperparameter filter, enter a value with the form "HyperParameters.<name>" . Decimal hyperparameter values are treated as a decimal in a comparison if the specified Value is also a decimal value. If the specified Value is an integer, the decimal hyperparameter values are treated as integers. For example, the following filter is satisfied by training jobs with a "learning_rate" hyperparameter that is less than "0.5" :
{"Name": "HyperParameters.learning_rate",
"Operator": "LessThan",
"Value": "0.5"
}
Tags
To define a tag filter, enter a value with the form Tags.<key> .
A resource property name. For example, TrainingJobName . For valid property names, see SearchRecord . You must specify a valid property for the resource.
A Boolean binary operator that is used to evaluate the filter. The operator field contains one of the following values:
Equals
The value of Name equals Value .
NotEquals
The value of Name doesn't equal Value .
Exists
The Name property exists.
NotExists
The Name property does not exist.
GreaterThan
The value of Name is greater than Value . Not supported for text properties.
GreaterThanOrEqualTo
The value of Name is greater than or equal to Value . Not supported for text properties.
LessThan
The value of Name is less than Value . Not supported for text properties.
LessThanOrEqualTo
The value of Name is less than or equal to Value . Not supported for text properties.
In
The value of Name is one of the comma delimited strings in Value . Only supported for text properties.
Contains
The value of Name contains the string Value . Only supported for text properties.
A SearchExpression can include the Contains operator multiple times when the value of Name is one of the following:
A SearchExpression can include only one Contains operator for all other values of Name . In these cases, if you include multiple Contains operators in the SearchExpression , the result is the following error message: "'CONTAINS' operator usage limit of 1 exceeded. "
A value used with Name and Operator to determine which resources satisfy the filter's condition. For numerical properties, Value must be an integer or floating-point decimal. For timestamp properties, Value must be an ISO 8601 date-time string of the following format: YYYY-mm-dd'T'HH:MM:SS .
A list of nested filter objects.
A list of nested Filter objects. A resource must satisfy the conditions of all filters to be included in the results returned from the Search API.
For example, to filter on a training job's InputDataConfig property with a specific channel name and S3Uri prefix, define the following filters:
The name of the property to use in the nested filters. The value must match a listed property name, such as InputDataConfig .
A list of filters. Each filter acts on a property. Filters must contain at least one Filters value. For example, a NestedFilters call might include a filter on the PropertyName parameter of the InputDataConfig property: InputDataConfig.DataSource.S3DataSource.S3Uri .
A conditional statement for a search expression that includes a resource property, a Boolean operator, and a value. Resources that match the statement are returned in the results from the Search API.
If you specify a Value , but not an Operator , Amazon SageMaker uses the equals operator.
In search, there are several property types:
Metrics
To define a metric filter, enter a value using the form "Metrics.<name>" , where <name> is a metric name. For example, the following filter searches for training jobs with an "accuracy" metric greater than "0.9" :
{"Name": "Metrics.accuracy",
"Operator": "GreaterThan",
"Value": "0.9"
}
HyperParameters
To define a hyperparameter filter, enter a value with the form "HyperParameters.<name>" . Decimal hyperparameter values are treated as a decimal in a comparison if the specified Value is also a decimal value. If the specified Value is an integer, the decimal hyperparameter values are treated as integers. For example, the following filter is satisfied by training jobs with a "learning_rate" hyperparameter that is less than "0.5" :
{"Name": "HyperParameters.learning_rate",
"Operator": "LessThan",
"Value": "0.5"
}
Tags
To define a tag filter, enter a value with the form Tags.<key> .
A resource property name. For example, TrainingJobName . For valid property names, see SearchRecord . You must specify a valid property for the resource.
A Boolean binary operator that is used to evaluate the filter. The operator field contains one of the following values:
Equals
The value of Name equals Value .
NotEquals
The value of Name doesn't equal Value .
Exists
The Name property exists.
NotExists
The Name property does not exist.
GreaterThan
The value of Name is greater than Value . Not supported for text properties.
GreaterThanOrEqualTo
The value of Name is greater than or equal to Value . Not supported for text properties.
LessThan
The value of Name is less than Value . Not supported for text properties.
LessThanOrEqualTo
The value of Name is less than or equal to Value . Not supported for text properties.
In
The value of Name is one of the comma delimited strings in Value . Only supported for text properties.
Contains
The value of Name contains the string Value . Only supported for text properties.
A SearchExpression can include the Contains operator multiple times when the value of Name is one of the following:
A SearchExpression can include only one Contains operator for all other values of Name . In these cases, if you include multiple Contains operators in the SearchExpression , the result is the following error message: "'CONTAINS' operator usage limit of 1 exceeded. "
A value used with Name and Operator to determine which resources satisfy the filter's condition. For numerical properties, Value must be an integer or floating-point decimal. For timestamp properties, Value must be an ISO 8601 date-time string of the following format: YYYY-mm-dd'T'HH:MM:SS .
A list of search expression objects.
A multi-expression that searches for the specified resource or resources in a search. All resource objects that satisfy the expression's condition are included in the search results. You must specify at least one subexpression, filter, or nested filter. A SearchExpression can contain up to twenty elements.
A SearchExpression contains the following components:
A Boolean operator used to evaluate the search expression. If you want every conditional statement in all lists to be satisfied for the entire search expression to be true, specify And . If only a single conditional statement needs to be true for the entire search expression to be true, specify Or . The default value is And .
dict
Response Syntax
{
'Results': [
{
'TrainingJob': {
'TrainingJobName': 'string',
'TrainingJobArn': 'string',
'TuningJobArn': 'string',
'LabelingJobArn': 'string',
'AutoMLJobArn': 'string',
'ModelArtifacts': {
'S3ModelArtifacts': 'string'
},
'TrainingJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'SecondaryStatus': 'Starting'|'LaunchingMLInstances'|'PreparingTrainingStack'|'Downloading'|'DownloadingTrainingImage'|'Training'|'Uploading'|'Stopping'|'Stopped'|'MaxRuntimeExceeded'|'Completed'|'Failed'|'Interrupted'|'MaxWaitTimeExceeded'|'Updating'|'Restarting',
'FailureReason': 'string',
'HyperParameters': {
'string': 'string'
},
'AlgorithmSpecification': {
'TrainingImage': 'string',
'AlgorithmName': 'string',
'TrainingInputMode': 'Pipe'|'File',
'MetricDefinitions': [
{
'Name': 'string',
'Regex': 'string'
},
],
'EnableSageMakerMetricsTimeSeries': True|False
},
'RoleArn': 'string',
'InputDataConfig': [
{
'ChannelName': 'string',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'AttributeNames': [
'string',
]
},
'FileSystemDataSource': {
'FileSystemId': 'string',
'FileSystemAccessMode': 'rw'|'ro',
'FileSystemType': 'EFS'|'FSxLustre',
'DirectoryPath': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File',
'ShuffleConfig': {
'Seed': 123
}
},
],
'OutputDataConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string'
},
'ResourceConfig': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
},
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123
},
'CreationTime': datetime(2015, 1, 1),
'TrainingStartTime': datetime(2015, 1, 1),
'TrainingEndTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'SecondaryStatusTransitions': [
{
'Status': 'Starting'|'LaunchingMLInstances'|'PreparingTrainingStack'|'Downloading'|'DownloadingTrainingImage'|'Training'|'Uploading'|'Stopping'|'Stopped'|'MaxRuntimeExceeded'|'Completed'|'Failed'|'Interrupted'|'MaxWaitTimeExceeded'|'Updating'|'Restarting',
'StartTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1),
'StatusMessage': 'string'
},
],
'FinalMetricDataList': [
{
'MetricName': 'string',
'Value': ...,
'Timestamp': datetime(2015, 1, 1)
},
],
'EnableNetworkIsolation': True|False,
'EnableInterContainerTrafficEncryption': True|False,
'EnableManagedSpotTraining': True|False,
'CheckpointConfig': {
'S3Uri': 'string',
'LocalPath': 'string'
},
'TrainingTimeInSeconds': 123,
'BillableTimeInSeconds': 123,
'DebugHookConfig': {
'LocalPath': 'string',
'S3OutputPath': 'string',
'HookParameters': {
'string': 'string'
},
'CollectionConfigurations': [
{
'CollectionName': 'string',
'CollectionParameters': {
'string': 'string'
}
},
]
},
'ExperimentConfig': {
'ExperimentName': 'string',
'TrialName': 'string',
'TrialComponentDisplayName': 'string'
},
'DebugRuleConfigurations': [
{
'RuleConfigurationName': 'string',
'LocalPath': 'string',
'S3OutputPath': 'string',
'RuleEvaluatorImage': 'string',
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'RuleParameters': {
'string': 'string'
}
},
],
'TensorBoardOutputConfig': {
'LocalPath': 'string',
'S3OutputPath': 'string'
},
'DebugRuleEvaluationStatuses': [
{
'RuleConfigurationName': 'string',
'RuleEvaluationJobArn': 'string',
'RuleEvaluationStatus': 'InProgress'|'NoIssuesFound'|'IssuesFound'|'Error'|'Stopping'|'Stopped',
'StatusDetails': 'string',
'LastModifiedTime': datetime(2015, 1, 1)
},
],
'Environment': {
'string': 'string'
},
'RetryStrategy': {
'MaximumRetryAttempts': 123
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'Experiment': {
'ExperimentName': 'string',
'ExperimentArn': 'string',
'DisplayName': 'string',
'Source': {
'SourceArn': 'string',
'SourceType': 'string'
},
'Description': 'string',
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'LastModifiedTime': datetime(2015, 1, 1),
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'Trial': {
'TrialName': 'string',
'TrialArn': 'string',
'DisplayName': 'string',
'ExperimentName': 'string',
'Source': {
'SourceArn': 'string',
'SourceType': 'string'
},
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'LastModifiedTime': datetime(2015, 1, 1),
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'MetadataProperties': {
'CommitId': 'string',
'Repository': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string'
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
],
'TrialComponentSummaries': [
{
'TrialComponentName': 'string',
'TrialComponentArn': 'string',
'TrialComponentSource': {
'SourceArn': 'string',
'SourceType': 'string'
},
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
}
},
]
},
'TrialComponent': {
'TrialComponentName': 'string',
'DisplayName': 'string',
'TrialComponentArn': 'string',
'Source': {
'SourceArn': 'string',
'SourceType': 'string'
},
'Status': {
'PrimaryStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'Message': 'string'
},
'StartTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1),
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'LastModifiedTime': datetime(2015, 1, 1),
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'Parameters': {
'string': {
'StringValue': 'string',
'NumberValue': 123.0
}
},
'InputArtifacts': {
'string': {
'MediaType': 'string',
'Value': 'string'
}
},
'OutputArtifacts': {
'string': {
'MediaType': 'string',
'Value': 'string'
}
},
'Metrics': [
{
'MetricName': 'string',
'SourceArn': 'string',
'TimeStamp': datetime(2015, 1, 1),
'Max': 123.0,
'Min': 123.0,
'Last': 123.0,
'Count': 123,
'Avg': 123.0,
'StdDev': 123.0
},
],
'MetadataProperties': {
'CommitId': 'string',
'Repository': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string'
},
'SourceDetail': {
'SourceArn': 'string',
'TrainingJob': {
'TrainingJobName': 'string',
'TrainingJobArn': 'string',
'TuningJobArn': 'string',
'LabelingJobArn': 'string',
'AutoMLJobArn': 'string',
'ModelArtifacts': {
'S3ModelArtifacts': 'string'
},
'TrainingJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'SecondaryStatus': 'Starting'|'LaunchingMLInstances'|'PreparingTrainingStack'|'Downloading'|'DownloadingTrainingImage'|'Training'|'Uploading'|'Stopping'|'Stopped'|'MaxRuntimeExceeded'|'Completed'|'Failed'|'Interrupted'|'MaxWaitTimeExceeded'|'Updating'|'Restarting',
'FailureReason': 'string',
'HyperParameters': {
'string': 'string'
},
'AlgorithmSpecification': {
'TrainingImage': 'string',
'AlgorithmName': 'string',
'TrainingInputMode': 'Pipe'|'File',
'MetricDefinitions': [
{
'Name': 'string',
'Regex': 'string'
},
],
'EnableSageMakerMetricsTimeSeries': True|False
},
'RoleArn': 'string',
'InputDataConfig': [
{
'ChannelName': 'string',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'AttributeNames': [
'string',
]
},
'FileSystemDataSource': {
'FileSystemId': 'string',
'FileSystemAccessMode': 'rw'|'ro',
'FileSystemType': 'EFS'|'FSxLustre',
'DirectoryPath': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File',
'ShuffleConfig': {
'Seed': 123
}
},
],
'OutputDataConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string'
},
'ResourceConfig': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
},
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123
},
'CreationTime': datetime(2015, 1, 1),
'TrainingStartTime': datetime(2015, 1, 1),
'TrainingEndTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'SecondaryStatusTransitions': [
{
'Status': 'Starting'|'LaunchingMLInstances'|'PreparingTrainingStack'|'Downloading'|'DownloadingTrainingImage'|'Training'|'Uploading'|'Stopping'|'Stopped'|'MaxRuntimeExceeded'|'Completed'|'Failed'|'Interrupted'|'MaxWaitTimeExceeded'|'Updating'|'Restarting',
'StartTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1),
'StatusMessage': 'string'
},
],
'FinalMetricDataList': [
{
'MetricName': 'string',
'Value': ...,
'Timestamp': datetime(2015, 1, 1)
},
],
'EnableNetworkIsolation': True|False,
'EnableInterContainerTrafficEncryption': True|False,
'EnableManagedSpotTraining': True|False,
'CheckpointConfig': {
'S3Uri': 'string',
'LocalPath': 'string'
},
'TrainingTimeInSeconds': 123,
'BillableTimeInSeconds': 123,
'DebugHookConfig': {
'LocalPath': 'string',
'S3OutputPath': 'string',
'HookParameters': {
'string': 'string'
},
'CollectionConfigurations': [
{
'CollectionName': 'string',
'CollectionParameters': {
'string': 'string'
}
},
]
},
'ExperimentConfig': {
'ExperimentName': 'string',
'TrialName': 'string',
'TrialComponentDisplayName': 'string'
},
'DebugRuleConfigurations': [
{
'RuleConfigurationName': 'string',
'LocalPath': 'string',
'S3OutputPath': 'string',
'RuleEvaluatorImage': 'string',
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'RuleParameters': {
'string': 'string'
}
},
],
'TensorBoardOutputConfig': {
'LocalPath': 'string',
'S3OutputPath': 'string'
},
'DebugRuleEvaluationStatuses': [
{
'RuleConfigurationName': 'string',
'RuleEvaluationJobArn': 'string',
'RuleEvaluationStatus': 'InProgress'|'NoIssuesFound'|'IssuesFound'|'Error'|'Stopping'|'Stopped',
'StatusDetails': 'string',
'LastModifiedTime': datetime(2015, 1, 1)
},
],
'Environment': {
'string': 'string'
},
'RetryStrategy': {
'MaximumRetryAttempts': 123
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'ProcessingJob': {
'ProcessingInputs': [
{
'InputName': 'string',
'AppManaged': True|False,
'S3Input': {
'S3Uri': 'string',
'LocalPath': 'string',
'S3DataType': 'ManifestFile'|'S3Prefix',
'S3InputMode': 'Pipe'|'File',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'S3CompressionType': 'None'|'Gzip'
},
'DatasetDefinition': {
'AthenaDatasetDefinition': {
'Catalog': 'string',
'Database': 'string',
'QueryString': 'string',
'WorkGroup': 'string',
'OutputS3Uri': 'string',
'KmsKeyId': 'string',
'OutputFormat': 'PARQUET'|'ORC'|'AVRO'|'JSON'|'TEXTFILE',
'OutputCompression': 'GZIP'|'SNAPPY'|'ZLIB'
},
'RedshiftDatasetDefinition': {
'ClusterId': 'string',
'Database': 'string',
'DbUser': 'string',
'QueryString': 'string',
'ClusterRoleArn': 'string',
'OutputS3Uri': 'string',
'KmsKeyId': 'string',
'OutputFormat': 'PARQUET'|'CSV',
'OutputCompression': 'None'|'GZIP'|'BZIP2'|'ZSTD'|'SNAPPY'
},
'LocalPath': 'string',
'DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'InputMode': 'Pipe'|'File'
}
},
],
'ProcessingOutputConfig': {
'Outputs': [
{
'OutputName': 'string',
'S3Output': {
'S3Uri': 'string',
'LocalPath': 'string',
'S3UploadMode': 'Continuous'|'EndOfJob'
},
'FeatureStoreOutput': {
'FeatureGroupName': 'string'
},
'AppManaged': True|False
},
],
'KmsKeyId': 'string'
},
'ProcessingJobName': 'string',
'ProcessingResources': {
'ClusterConfig': {
'InstanceCount': 123,
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
}
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123
},
'AppSpecification': {
'ImageUri': 'string',
'ContainerEntrypoint': [
'string',
],
'ContainerArguments': [
'string',
]
},
'Environment': {
'string': 'string'
},
'NetworkConfig': {
'EnableInterContainerTrafficEncryption': True|False,
'EnableNetworkIsolation': True|False,
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
},
'RoleArn': 'string',
'ExperimentConfig': {
'ExperimentName': 'string',
'TrialName': 'string',
'TrialComponentDisplayName': 'string'
},
'ProcessingJobArn': 'string',
'ProcessingJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'ExitMessage': 'string',
'FailureReason': 'string',
'ProcessingEndTime': datetime(2015, 1, 1),
'ProcessingStartTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'CreationTime': datetime(2015, 1, 1),
'MonitoringScheduleArn': 'string',
'AutoMLJobArn': 'string',
'TrainingJobArn': 'string',
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'TransformJob': {
'TransformJobName': 'string',
'TransformJobArn': 'string',
'TransformJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'FailureReason': 'string',
'ModelName': 'string',
'MaxConcurrentTransforms': 123,
'ModelClientConfig': {
'InvocationsTimeoutInSeconds': 123,
'InvocationsMaxRetries': 123
},
'MaxPayloadInMB': 123,
'BatchStrategy': 'MultiRecord'|'SingleRecord',
'Environment': {
'string': 'string'
},
'TransformInput': {
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'SplitType': 'None'|'Line'|'RecordIO'|'TFRecord'
},
'TransformOutput': {
'S3OutputPath': 'string',
'Accept': 'string',
'AssembleWith': 'None'|'Line',
'KmsKeyId': 'string'
},
'TransformResources': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'InstanceCount': 123,
'VolumeKmsKeyId': 'string'
},
'CreationTime': datetime(2015, 1, 1),
'TransformStartTime': datetime(2015, 1, 1),
'TransformEndTime': datetime(2015, 1, 1),
'LabelingJobArn': 'string',
'AutoMLJobArn': 'string',
'DataProcessing': {
'InputFilter': 'string',
'OutputFilter': 'string',
'JoinSource': 'Input'|'None'
},
'ExperimentConfig': {
'ExperimentName': 'string',
'TrialName': 'string',
'TrialComponentDisplayName': 'string'
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
}
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
],
'Parents': [
{
'TrialName': 'string',
'ExperimentName': 'string'
},
]
},
'Endpoint': {
'EndpointName': 'string',
'EndpointArn': 'string',
'EndpointConfigName': 'string',
'ProductionVariants': [
{
'VariantName': 'string',
'DeployedImages': [
{
'SpecifiedImage': 'string',
'ResolvedImage': 'string',
'ResolutionTime': datetime(2015, 1, 1)
},
],
'CurrentWeight': ...,
'DesiredWeight': ...,
'CurrentInstanceCount': 123,
'DesiredInstanceCount': 123
},
],
'DataCaptureConfig': {
'EnableCapture': True|False,
'CaptureStatus': 'Started'|'Stopped',
'CurrentSamplingPercentage': 123,
'DestinationS3Uri': 'string',
'KmsKeyId': 'string'
},
'EndpointStatus': 'OutOfService'|'Creating'|'Updating'|'SystemUpdating'|'RollingBack'|'InService'|'Deleting'|'Failed',
'FailureReason': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'MonitoringSchedules': [
{
'MonitoringScheduleArn': 'string',
'MonitoringScheduleName': 'string',
'MonitoringScheduleStatus': 'Pending'|'Failed'|'Scheduled'|'Stopped',
'MonitoringType': 'DataQuality'|'ModelQuality'|'ModelBias'|'ModelExplainability',
'FailureReason': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'MonitoringScheduleConfig': {
'ScheduleConfig': {
'ScheduleExpression': 'string'
},
'MonitoringJobDefinition': {
'BaselineConfig': {
'BaseliningJobName': 'string',
'ConstraintsResource': {
'S3Uri': 'string'
},
'StatisticsResource': {
'S3Uri': 'string'
}
},
'MonitoringInputs': [
{
'EndpointInput': {
'EndpointName': 'string',
'LocalPath': 'string',
'S3InputMode': 'Pipe'|'File',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'FeaturesAttribute': 'string',
'InferenceAttribute': 'string',
'ProbabilityAttribute': 'string',
'ProbabilityThresholdAttribute': 123.0,
'StartTimeOffset': 'string',
'EndTimeOffset': 'string'
}
},
],
'MonitoringOutputConfig': {
'MonitoringOutputs': [
{
'S3Output': {
'S3Uri': 'string',
'LocalPath': 'string',
'S3UploadMode': 'Continuous'|'EndOfJob'
}
},
],
'KmsKeyId': 'string'
},
'MonitoringResources': {
'ClusterConfig': {
'InstanceCount': 123,
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
}
},
'MonitoringAppSpecification': {
'ImageUri': 'string',
'ContainerEntrypoint': [
'string',
],
'ContainerArguments': [
'string',
],
'RecordPreprocessorSourceUri': 'string',
'PostAnalyticsProcessorSourceUri': 'string'
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123
},
'Environment': {
'string': 'string'
},
'NetworkConfig': {
'EnableInterContainerTrafficEncryption': True|False,
'EnableNetworkIsolation': True|False,
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
},
'RoleArn': 'string'
},
'MonitoringJobDefinitionName': 'string',
'MonitoringType': 'DataQuality'|'ModelQuality'|'ModelBias'|'ModelExplainability'
},
'EndpointName': 'string',
'LastMonitoringExecutionSummary': {
'MonitoringScheduleName': 'string',
'ScheduledTime': datetime(2015, 1, 1),
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'MonitoringExecutionStatus': 'Pending'|'Completed'|'CompletedWithViolations'|'InProgress'|'Failed'|'Stopping'|'Stopped',
'ProcessingJobArn': 'string',
'EndpointName': 'string',
'FailureReason': 'string',
'MonitoringJobDefinitionName': 'string',
'MonitoringType': 'DataQuality'|'ModelQuality'|'ModelBias'|'ModelExplainability'
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
],
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'ModelPackage': {
'ModelPackageName': 'string',
'ModelPackageGroupName': 'string',
'ModelPackageVersion': 123,
'ModelPackageArn': 'string',
'ModelPackageDescription': 'string',
'CreationTime': datetime(2015, 1, 1),
'InferenceSpecification': {
'Containers': [
{
'ContainerHostname': 'string',
'Image': 'string',
'ImageDigest': 'string',
'ModelDataUrl': 'string',
'ProductId': 'string',
'Environment': {
'string': 'string'
}
},
],
'SupportedTransformInstanceTypes': [
'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
],
'SupportedRealtimeInferenceInstanceTypes': [
'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge',
],
'SupportedContentTypes': [
'string',
],
'SupportedResponseMIMETypes': [
'string',
]
},
'SourceAlgorithmSpecification': {
'SourceAlgorithms': [
{
'ModelDataUrl': 'string',
'AlgorithmName': 'string'
},
]
},
'ValidationSpecification': {
'ValidationRole': 'string',
'ValidationProfiles': [
{
'ProfileName': 'string',
'TransformJobDefinition': {
'MaxConcurrentTransforms': 123,
'MaxPayloadInMB': 123,
'BatchStrategy': 'MultiRecord'|'SingleRecord',
'Environment': {
'string': 'string'
},
'TransformInput': {
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'SplitType': 'None'|'Line'|'RecordIO'|'TFRecord'
},
'TransformOutput': {
'S3OutputPath': 'string',
'Accept': 'string',
'AssembleWith': 'None'|'Line',
'KmsKeyId': 'string'
},
'TransformResources': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'InstanceCount': 123,
'VolumeKmsKeyId': 'string'
}
}
},
]
},
'ModelPackageStatus': 'Pending'|'InProgress'|'Completed'|'Failed'|'Deleting',
'ModelPackageStatusDetails': {
'ValidationStatuses': [
{
'Name': 'string',
'Status': 'NotStarted'|'InProgress'|'Completed'|'Failed',
'FailureReason': 'string'
},
],
'ImageScanStatuses': [
{
'Name': 'string',
'Status': 'NotStarted'|'InProgress'|'Completed'|'Failed',
'FailureReason': 'string'
},
]
},
'CertifyForMarketplace': True|False,
'ModelApprovalStatus': 'Approved'|'Rejected'|'PendingManualApproval',
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'MetadataProperties': {
'CommitId': 'string',
'Repository': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string'
},
'ModelMetrics': {
'ModelQuality': {
'Statistics': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'Constraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'ModelDataQuality': {
'Statistics': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'Constraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'Bias': {
'Report': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'Explainability': {
'Report': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
}
},
'LastModifiedTime': datetime(2015, 1, 1),
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'ApprovalDescription': 'string',
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'ModelPackageGroup': {
'ModelPackageGroupName': 'string',
'ModelPackageGroupArn': 'string',
'ModelPackageGroupDescription': 'string',
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'ModelPackageGroupStatus': 'Pending'|'InProgress'|'Completed'|'Failed'|'Deleting'|'DeleteFailed',
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'Pipeline': {
'PipelineArn': 'string',
'PipelineName': 'string',
'PipelineDisplayName': 'string',
'PipelineDescription': 'string',
'RoleArn': 'string',
'PipelineStatus': 'Active',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'LastRunTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'PipelineExecution': {
'PipelineArn': 'string',
'PipelineExecutionArn': 'string',
'PipelineExecutionDisplayName': 'string',
'PipelineExecutionStatus': 'Executing'|'Stopping'|'Stopped'|'Failed'|'Succeeded',
'PipelineExecutionDescription': 'string',
'PipelineExperimentConfig': {
'ExperimentName': 'string',
'TrialName': 'string'
},
'FailureReason': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'PipelineParameters': [
{
'Name': 'string',
'Value': 'string'
},
]
},
'FeatureGroup': {
'FeatureGroupArn': 'string',
'FeatureGroupName': 'string',
'RecordIdentifierFeatureName': 'string',
'EventTimeFeatureName': 'string',
'FeatureDefinitions': [
{
'FeatureName': 'string',
'FeatureType': 'Integral'|'Fractional'|'String'
},
],
'CreationTime': datetime(2015, 1, 1),
'OnlineStoreConfig': {
'SecurityConfig': {
'KmsKeyId': 'string'
},
'EnableOnlineStore': True|False
},
'OfflineStoreConfig': {
'S3StorageConfig': {
'S3Uri': 'string',
'KmsKeyId': 'string',
'ResolvedOutputS3Uri': 'string'
},
'DisableGlueTableCreation': True|False,
'DataCatalogConfig': {
'TableName': 'string',
'Catalog': 'string',
'Database': 'string'
}
},
'RoleArn': 'string',
'FeatureGroupStatus': 'Creating'|'Created'|'CreateFailed'|'Deleting'|'DeleteFailed',
'OfflineStoreStatus': {
'Status': 'Active'|'Blocked'|'Disabled',
'BlockedReason': 'string'
},
'FailureReason': 'string',
'Description': 'string',
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
}
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
Results (list) --
A list of SearchRecord objects.
(dict) --
A single resource returned as part of the Search API response.
TrainingJob (dict) --
The properties of a training job.
TrainingJobName (string) --
The name of the training job.
TrainingJobArn (string) --
The Amazon Resource Name (ARN) of the training job.
TuningJobArn (string) --
The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.
LabelingJobArn (string) --
The Amazon Resource Name (ARN) of the labeling job.
AutoMLJobArn (string) --
The Amazon Resource Name (ARN) of the job.
ModelArtifacts (dict) --
Information about the Amazon S3 location that is configured for storing model artifacts.
S3ModelArtifacts (string) --
The path of the S3 object that contains the model artifacts. For example, s3://bucket-name/keynameprefix/model.tar.gz .
TrainingJobStatus (string) --
The status of the training job.
Training job statuses are:
For more detailed information, see SecondaryStatus .
SecondaryStatus (string) --
Provides detailed information about the state of the training job. For detailed information about the secondary status of the training job, see StatusMessage under SecondaryStatusTransition .
Amazon SageMaker provides primary statuses and secondary statuses that apply to each of them:
InProgress
Starting - Starting the training job.
Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.
Training - Training is in progress.
Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.
Completed
Completed - The training job has completed.
Failed
Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse .
Stopped
MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.
Stopped - The training job has stopped.
Stopping
Stopping - Stopping the training job.
Warning
Valid values for SecondaryStatus are subject to change.
We no longer support the following secondary statuses:
FailureReason (string) --
If the training job failed, the reason it failed.
HyperParameters (dict) --
Algorithm-specific parameters.
AlgorithmSpecification (dict) --
Information about the algorithm used for training, and algorithm metadata.
TrainingImage (string) --
The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters . Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
AlgorithmName (string) --
The name of the algorithm resource to use for the training job. This must be an algorithm resource that you created or subscribe to on Amazon Web Services Marketplace. If you specify a value for this parameter, you can't specify a value for TrainingImage .
TrainingInputMode (string) --
The input mode that the algorithm supports. For the input modes that Amazon SageMaker algorithms support, see Algorithms . If an algorithm supports the File input mode, Amazon SageMaker downloads the training data from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an algorithm supports the Pipe input mode, Amazon SageMaker streams data directly from S3 to the container.
In File mode, make sure you provision ML storage volume with sufficient capacity to accommodate the data download from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container use ML storage volume to also store intermediate information, if any.
For distributed algorithms using File mode, training data is distributed uniformly, and your training duration is predictable if the input data objects size is approximately same. Amazon SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed where one host in a training cluster is overloaded, thus becoming bottleneck in training.
MetricDefinitions (list) --
A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. Amazon SageMaker publishes each metric to Amazon CloudWatch.
(dict) --
Specifies a metric that the training algorithm writes to stderr or stdout . Amazon SageMakerhyperparameter tuning captures all defined metrics. You specify one metric that a hyperparameter tuning job uses as its objective metric to choose the best training job.
Name (string) --
The name of the metric.
Regex (string) --
A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining Objective Metrics .
EnableSageMakerMetricsTimeSeries (boolean) --
To generate and save time-series metrics during training, set to true . The default is false and time-series metrics aren't generated except in the following cases:
RoleArn (string) --
The Amazon Web Services Identity and Access Management (IAM) role configured for the training job.
InputDataConfig (list) --
An array of Channel objects that describes each data input channel.
(dict) --
A channel is a named input source that training algorithms can consume.
ChannelName (string) --
The name of the channel.
DataSource (dict) --
The location of the channel data.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) --
If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects that match the specified key name prefix for model training.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for model training.
If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .
S3Uri (string) --
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
S3DataDistributionType (string) --
If you want Amazon SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .
If you want Amazon SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.
AttributeNames (list) --
A list of one or more attribute names to use that are found in a specified augmented manifest file.
FileSystemDataSource (dict) --
The file system that is associated with a channel.
FileSystemId (string) --
The file system id.
FileSystemAccessMode (string) --
The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.
FileSystemType (string) --
The file system type.
DirectoryPath (string) --
The full path to the directory to associate with the channel.
ContentType (string) --
The MIME type of the data.
CompressionType (string) --
If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
RecordWrapperType (string) --
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, Amazon SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO .
In File mode, leave this field unset or set it to None.
InputMode (string) --
(Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , Amazon SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
To use a model for incremental training, choose File input model.
ShuffleConfig (dict) --
A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
Seed (integer) --
Determines the shuffling order in ShuffleConfig value.
OutputDataConfig (dict) --
The S3 path where model artifacts that you configured when creating the job are stored. Amazon SageMaker creates subfolders for model artifacts.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
If you use a KMS key ID or an alias of your master key, the Amazon SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. Amazon SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig . If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
S3OutputPath (string) --
Identifies the S3 path where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
ResourceConfig (dict) --
Resources, including ML compute instances and ML storage volumes, that are configured for model training.
InstanceType (string) --
The ML compute instance type.
InstanceCount (integer) --
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB (integer) --
The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.
You must specify sufficient ML storage for your scenario.
Note
Amazon SageMaker supports only the General Purpose SSD (gp2) ML storage volume type.
Note
Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for training, Amazon SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can't request a VolumeSizeInGB greater than the total size of the local instance storage.
For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes .
VolumeKmsKeyId (string) --
The Amazon Web Services KMS key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be in any of the following formats:
VpcConfig (dict) --
A VpcConfig object that specifies the VPC that this training job has access to. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud .
SecurityGroupIds (list) --
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
Subnets (list) --
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
StoppingCondition (dict) --
Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.
MaxRuntimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can run. If the job does not complete during this time, Amazon SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
The default value is 1 day. The maximum value is 28 days.
MaxWaitTimeInSeconds (integer) --
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, Amazon SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
CreationTime (datetime) --
A timestamp that indicates when the training job was created.
TrainingStartTime (datetime) --
Indicates the time when the training job starts on training instances. You are billed for the time interval between this time and the value of TrainingEndTime . The start time in CloudWatch Logs might be later than this time. The difference is due to the time it takes to download the training data and to the size of the training container.
TrainingEndTime (datetime) --
Indicates the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when Amazon SageMaker detects a job failure.
LastModifiedTime (datetime) --
A timestamp that indicates when the status of the training job was last modified.
SecondaryStatusTransitions (list) --
A history of all of the secondary statuses that the training job has transitioned through.
(dict) --
An array element of DescribeTrainingJobResponse$SecondaryStatusTransitions . It provides additional details about a status that the training job has transitioned through. A training job can be in one of several states, for example, starting, downloading, training, or uploading. Within each state, there are a number of intermediate states. For example, within the starting state, Amazon SageMaker could be starting the training job or launching the ML instances. These transitional states are referred to as the job's secondary status.
Status (string) --
Contains a secondary status information from a training job.
Status might be one of the following secondary statuses:
InProgress
Starting - Starting the training job.
Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.
Training - Training is in progress.
Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.
Completed
Completed - The training job has completed.
Failed
Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse .
Stopped
MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.
Stopped - The training job has stopped.
Stopping
Stopping - Stopping the training job.
We no longer support the following secondary statuses:
StartTime (datetime) --
A timestamp that shows when the training job transitioned to the current secondary status state.
EndTime (datetime) --
A timestamp that shows when the training job transitioned out of this secondary status state into another secondary status state or when the training job has ended.
StatusMessage (string) --
A detailed description of the progress within a secondary status.
Amazon SageMaker provides secondary statuses and status messages that apply to each of them:
Starting
Starting the training job.
Launching requested ML instances.
Insufficient capacity error from EC2 while launching instances, retrying!
Launched instance was unhealthy, replacing it!
Preparing the instances for training.
Training
Downloading the training image.
Training image download completed. Training in progress.
Warning
Status messages are subject to change. Therefore, we recommend not including them in code that programmatically initiates actions. For examples, don't use status messages in if statements.
To have an overview of your training job's progress, view TrainingJobStatus and SecondaryStatus in DescribeTrainingJob , and StatusMessage together. For example, at the start of a training job, you might see the following:
FinalMetricDataList (list) --
A list of final metric values that are set when the training job completes. Used only if the training job was configured to use metrics.
(dict) --
The name, value, and date and time of a metric that was emitted to Amazon CloudWatch.
MetricName (string) --
The name of the metric.
Value (float) --
The value of the metric.
Timestamp (datetime) --
The date and time that the algorithm emitted the metric.
EnableNetworkIsolation (boolean) --
If the TrainingJob was created with network isolation, the value is set to true . If network isolation is enabled, nodes can't communicate beyond the VPC they run in.
EnableInterContainerTrafficEncryption (boolean) --
To encrypt all communications between ML compute instances in distributed training, choose True . Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.
EnableManagedSpotTraining (boolean) --
When true, enables managed spot training using Amazon EC2 Spot instances to run training jobs instead of on-demand instances. For more information, see Managed Spot Training .
CheckpointConfig (dict) --
Contains information about the output location for managed spot training checkpoint data.
S3Uri (string) --
Identifies the S3 path where you want Amazon SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix .
LocalPath (string) --
(Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/ .
TrainingTimeInSeconds (integer) --
The training time in seconds.
BillableTimeInSeconds (integer) --
The billable time in seconds.
DebugHookConfig (dict) --
Configuration information for the Debugger hook parameters, metric and tensor collections, and storage paths. To learn more about how to configure the DebugHookConfig parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
LocalPath (string) --
Path to local storage location for metrics and tensors. Defaults to /opt/ml/output/tensors/ .
S3OutputPath (string) --
Path to Amazon S3 storage location for metrics and tensors.
HookParameters (dict) --
Configuration information for the Debugger hook parameters.
CollectionConfigurations (list) --
Configuration information for Debugger tensor collections. To learn more about how to configure the CollectionConfiguration parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
(dict) --
Configuration information for the Debugger output tensor collections.
CollectionName (string) --
The name of the tensor collection. The name must be unique relative to other rule configuration names.
CollectionParameters (dict) --
Parameter values for the tensor collection. The allowed parameters are "name" , "include_regex" , "reduction_config" , "save_config" , "tensor_names" , and "save_histogram" .
ExperimentConfig (dict) --
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
ExperimentName (string) --
The name of an existing experiment to associate the trial component with.
TrialName (string) --
The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
TrialComponentDisplayName (string) --
The display name for the trial component. If this key isn't specified, the display name is the trial component name.
DebugRuleConfigurations (list) --
Information about the debug rule configuration.
(dict) --
Configuration information for SageMaker Debugger rules for debugging. To learn more about how to configure the DebugRuleConfiguration parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
RuleConfigurationName (string) --
The name of the rule configuration. It must be unique relative to other rule configuration names.
LocalPath (string) --
Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/ .
S3OutputPath (string) --
Path to Amazon S3 storage location for rules.
RuleEvaluatorImage (string) --
The Amazon Elastic Container (ECR) Image for the managed rule evaluation.
InstanceType (string) --
The instance type to deploy a Debugger custom rule for debugging a training job.
VolumeSizeInGB (integer) --
The size, in GB, of the ML storage volume attached to the processing instance.
RuleParameters (dict) --
Runtime configuration for rule container.
TensorBoardOutputConfig (dict) --
Configuration of storage locations for the Debugger TensorBoard output data.
LocalPath (string) --
Path to local storage location for tensorBoard output. Defaults to /opt/ml/output/tensorboard .
S3OutputPath (string) --
Path to Amazon S3 storage location for TensorBoard output.
DebugRuleEvaluationStatuses (list) --
Information about the evaluation status of the rules for the training job.
(dict) --
Information about the status of the rule evaluation.
RuleConfigurationName (string) --
The name of the rule configuration.
RuleEvaluationJobArn (string) --
The Amazon Resource Name (ARN) of the rule evaluation job.
RuleEvaluationStatus (string) --
Status of the rule evaluation.
StatusDetails (string) --
Details from the rule evaluation.
LastModifiedTime (datetime) --
Timestamp when the rule evaluation status was last modified.
Environment (dict) --
The environment variables to set in the Docker container.
RetryStrategy (dict) --
The number of times to retry the job when the job fails due to an InternalServerError .
MaximumRetryAttempts (integer) --
The number of times to retry the job. When the job is retried, it's SecondaryStatus is changed to STARTING .
Tags (list) --
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
Experiment (dict) --
The properties of an experiment.
ExperimentName (string) --
The name of the experiment.
ExperimentArn (string) --
The Amazon Resource Name (ARN) of the experiment.
DisplayName (string) --
The name of the experiment as displayed. If DisplayName isn't specified, ExperimentName is displayed.
Source (dict) --
The source of the experiment.
SourceArn (string) --
The Amazon Resource Name (ARN) of the source.
SourceType (string) --
The source type.
Description (string) --
The description of the experiment.
CreationTime (datetime) --
When the experiment was created.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
LastModifiedTime (datetime) --
When the experiment was last modified.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
Tags (list) --
The list of tags that are associated with the experiment. You can use Search API to search on the tags.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
Trial (dict) --
The properties of a trial.
TrialName (string) --
The name of the trial.
TrialArn (string) --
The Amazon Resource Name (ARN) of the trial.
DisplayName (string) --
The name of the trial as displayed. If DisplayName isn't specified, TrialName is displayed.
ExperimentName (string) --
The name of the experiment the trial is part of.
Source (dict) --
The source of the trial.
SourceArn (string) --
The Amazon Resource Name (ARN) of the source.
SourceType (string) --
The source job type.
CreationTime (datetime) --
When the trial was created.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
LastModifiedTime (datetime) --
Who last modified the trial.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
MetadataProperties (dict) --
Metadata properties of the tracking entity, trial, or trial component.
CommitId (string) --
The commit ID.
Repository (string) --
The repository.
GeneratedBy (string) --
The entity this entity was generated by.
ProjectId (string) --
The project ID.
Tags (list) --
The list of tags that are associated with the trial. You can use Search API to search on the tags.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
TrialComponentSummaries (list) --
A list of the components associated with the trial. For each component, a summary of the component's properties is included.
(dict) --
A short summary of a trial component.
TrialComponentName (string) --
The name of the trial component.
TrialComponentArn (string) --
The Amazon Resource Name (ARN) of the trial component.
TrialComponentSource (dict) --
The Amazon Resource Name (ARN) and job type of the source of a trial component.
SourceArn (string) --
The source ARN.
SourceType (string) --
The source job type.
CreationTime (datetime) --
When the component was created.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
TrialComponent (dict) --
The properties of a trial component.
TrialComponentName (string) --
The name of the trial component.
DisplayName (string) --
The name of the component as displayed. If DisplayName isn't specified, TrialComponentName is displayed.
TrialComponentArn (string) --
The Amazon Resource Name (ARN) of the trial component.
Source (dict) --
The Amazon Resource Name (ARN) and job type of the source of the component.
SourceArn (string) --
The source ARN.
SourceType (string) --
The source job type.
Status (dict) --
The status of the trial component.
PrimaryStatus (string) --
The status of the trial component.
Message (string) --
If the component failed, a message describing why.
StartTime (datetime) --
When the component started.
EndTime (datetime) --
When the component ended.
CreationTime (datetime) --
When the component was created.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
LastModifiedTime (datetime) --
When the component was last modified.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
Parameters (dict) --
The hyperparameters of the component.
(string) --
(dict) --
The value of a hyperparameter. Only one of NumberValue or StringValue can be specified.
This object is specified in the CreateTrialComponent request.
StringValue (string) --
The string value of a categorical hyperparameter. If you specify a value for this parameter, you can't specify the NumberValue parameter.
NumberValue (float) --
The numeric value of a numeric hyperparameter. If you specify a value for this parameter, you can't specify the StringValue parameter.
InputArtifacts (dict) --
The input artifacts of the component.
(string) --
(dict) --
Represents an input or output artifact of a trial component. You specify TrialComponentArtifact as part of the InputArtifacts and OutputArtifacts parameters in the CreateTrialComponent request.
Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and instance types. Examples of output artifacts are metrics, snapshots, logs, and images.
MediaType (string) --
The media type of the artifact, which indicates the type of data in the artifact file. The media type consists of a type and a subtype concatenated with a slash (/) character, for example, text/csv, image/jpeg, and s3/uri. The type specifies the category of the media. The subtype specifies the kind of data.
Value (string) --
The location of the artifact.
OutputArtifacts (dict) --
The output artifacts of the component.
(string) --
(dict) --
Represents an input or output artifact of a trial component. You specify TrialComponentArtifact as part of the InputArtifacts and OutputArtifacts parameters in the CreateTrialComponent request.
Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and instance types. Examples of output artifacts are metrics, snapshots, logs, and images.
MediaType (string) --
The media type of the artifact, which indicates the type of data in the artifact file. The media type consists of a type and a subtype concatenated with a slash (/) character, for example, text/csv, image/jpeg, and s3/uri. The type specifies the category of the media. The subtype specifies the kind of data.
Value (string) --
The location of the artifact.
Metrics (list) --
The metrics for the component.
(dict) --
A summary of the metrics of a trial component.
MetricName (string) --
The name of the metric.
SourceArn (string) --
The Amazon Resource Name (ARN) of the source.
TimeStamp (datetime) --
When the metric was last updated.
Max (float) --
The maximum value of the metric.
Min (float) --
The minimum value of the metric.
Last (float) --
The most recent value of the metric.
Count (integer) --
The number of samples used to generate the metric.
Avg (float) --
The average value of the metric.
StdDev (float) --
The standard deviation of the metric.
MetadataProperties (dict) --
Metadata properties of the tracking entity, trial, or trial component.
CommitId (string) --
The commit ID.
Repository (string) --
The repository.
GeneratedBy (string) --
The entity this entity was generated by.
ProjectId (string) --
The project ID.
SourceDetail (dict) --
Details of the source of the component.
SourceArn (string) --
The Amazon Resource Name (ARN) of the source.
TrainingJob (dict) --
Information about a training job that's the source of a trial component.
TrainingJobName (string) --
The name of the training job.
TrainingJobArn (string) --
The Amazon Resource Name (ARN) of the training job.
TuningJobArn (string) --
The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.
LabelingJobArn (string) --
The Amazon Resource Name (ARN) of the labeling job.
AutoMLJobArn (string) --
The Amazon Resource Name (ARN) of the job.
ModelArtifacts (dict) --
Information about the Amazon S3 location that is configured for storing model artifacts.
S3ModelArtifacts (string) --
The path of the S3 object that contains the model artifacts. For example, s3://bucket-name/keynameprefix/model.tar.gz .
TrainingJobStatus (string) --
The status of the training job.
Training job statuses are:
For more detailed information, see SecondaryStatus .
SecondaryStatus (string) --
Provides detailed information about the state of the training job. For detailed information about the secondary status of the training job, see StatusMessage under SecondaryStatusTransition .
Amazon SageMaker provides primary statuses and secondary statuses that apply to each of them:
InProgress
Starting - Starting the training job.
Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.
Training - Training is in progress.
Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.
Completed
Completed - The training job has completed.
Failed
Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse .
Stopped
MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.
Stopped - The training job has stopped.
Stopping
Stopping - Stopping the training job.
Warning
Valid values for SecondaryStatus are subject to change.
We no longer support the following secondary statuses:
FailureReason (string) --
If the training job failed, the reason it failed.
HyperParameters (dict) --
Algorithm-specific parameters.
AlgorithmSpecification (dict) --
Information about the algorithm used for training, and algorithm metadata.
TrainingImage (string) --
The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters . Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
AlgorithmName (string) --
The name of the algorithm resource to use for the training job. This must be an algorithm resource that you created or subscribe to on Amazon Web Services Marketplace. If you specify a value for this parameter, you can't specify a value for TrainingImage .
TrainingInputMode (string) --
The input mode that the algorithm supports. For the input modes that Amazon SageMaker algorithms support, see Algorithms . If an algorithm supports the File input mode, Amazon SageMaker downloads the training data from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an algorithm supports the Pipe input mode, Amazon SageMaker streams data directly from S3 to the container.
In File mode, make sure you provision ML storage volume with sufficient capacity to accommodate the data download from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container use ML storage volume to also store intermediate information, if any.
For distributed algorithms using File mode, training data is distributed uniformly, and your training duration is predictable if the input data objects size is approximately same. Amazon SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed where one host in a training cluster is overloaded, thus becoming bottleneck in training.
MetricDefinitions (list) --
A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. Amazon SageMaker publishes each metric to Amazon CloudWatch.
(dict) --
Specifies a metric that the training algorithm writes to stderr or stdout . Amazon SageMakerhyperparameter tuning captures all defined metrics. You specify one metric that a hyperparameter tuning job uses as its objective metric to choose the best training job.
Name (string) --
The name of the metric.
Regex (string) --
A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining Objective Metrics .
EnableSageMakerMetricsTimeSeries (boolean) --
To generate and save time-series metrics during training, set to true . The default is false and time-series metrics aren't generated except in the following cases:
RoleArn (string) --
The Amazon Web Services Identity and Access Management (IAM) role configured for the training job.
InputDataConfig (list) --
An array of Channel objects that describes each data input channel.
(dict) --
A channel is a named input source that training algorithms can consume.
ChannelName (string) --
The name of the channel.
DataSource (dict) --
The location of the channel data.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) --
If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects that match the specified key name prefix for model training.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for model training.
If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .
S3Uri (string) --
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
S3DataDistributionType (string) --
If you want Amazon SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .
If you want Amazon SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.
AttributeNames (list) --
A list of one or more attribute names to use that are found in a specified augmented manifest file.
FileSystemDataSource (dict) --
The file system that is associated with a channel.
FileSystemId (string) --
The file system id.
FileSystemAccessMode (string) --
The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.
FileSystemType (string) --
The file system type.
DirectoryPath (string) --
The full path to the directory to associate with the channel.
ContentType (string) --
The MIME type of the data.
CompressionType (string) --
If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
RecordWrapperType (string) --
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, Amazon SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO .
In File mode, leave this field unset or set it to None.
InputMode (string) --
(Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , Amazon SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
To use a model for incremental training, choose File input model.
ShuffleConfig (dict) --
A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
Seed (integer) --
Determines the shuffling order in ShuffleConfig value.
OutputDataConfig (dict) --
The S3 path where model artifacts that you configured when creating the job are stored. Amazon SageMaker creates subfolders for model artifacts.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
If you use a KMS key ID or an alias of your master key, the Amazon SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. Amazon SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig . If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
S3OutputPath (string) --
Identifies the S3 path where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
ResourceConfig (dict) --
Resources, including ML compute instances and ML storage volumes, that are configured for model training.
InstanceType (string) --
The ML compute instance type.
InstanceCount (integer) --
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB (integer) --
The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.
You must specify sufficient ML storage for your scenario.
Note
Amazon SageMaker supports only the General Purpose SSD (gp2) ML storage volume type.
Note
Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for training, Amazon SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can't request a VolumeSizeInGB greater than the total size of the local instance storage.
For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes .
VolumeKmsKeyId (string) --
The Amazon Web Services KMS key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be in any of the following formats:
VpcConfig (dict) --
A VpcConfig object that specifies the VPC that this training job has access to. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud .
SecurityGroupIds (list) --
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
Subnets (list) --
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
StoppingCondition (dict) --
Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.
MaxRuntimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can run. If the job does not complete during this time, Amazon SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
The default value is 1 day. The maximum value is 28 days.
MaxWaitTimeInSeconds (integer) --
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, Amazon SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
CreationTime (datetime) --
A timestamp that indicates when the training job was created.
TrainingStartTime (datetime) --
Indicates the time when the training job starts on training instances. You are billed for the time interval between this time and the value of TrainingEndTime . The start time in CloudWatch Logs might be later than this time. The difference is due to the time it takes to download the training data and to the size of the training container.
TrainingEndTime (datetime) --
Indicates the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when Amazon SageMaker detects a job failure.
LastModifiedTime (datetime) --
A timestamp that indicates when the status of the training job was last modified.
SecondaryStatusTransitions (list) --
A history of all of the secondary statuses that the training job has transitioned through.
(dict) --
An array element of DescribeTrainingJobResponse$SecondaryStatusTransitions . It provides additional details about a status that the training job has transitioned through. A training job can be in one of several states, for example, starting, downloading, training, or uploading. Within each state, there are a number of intermediate states. For example, within the starting state, Amazon SageMaker could be starting the training job or launching the ML instances. These transitional states are referred to as the job's secondary status.
Status (string) --
Contains a secondary status information from a training job.
Status might be one of the following secondary statuses:
InProgress
Starting - Starting the training job.
Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.
Training - Training is in progress.
Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.
Completed
Completed - The training job has completed.
Failed
Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse .
Stopped
MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.
Stopped - The training job has stopped.
Stopping
Stopping - Stopping the training job.
We no longer support the following secondary statuses:
StartTime (datetime) --
A timestamp that shows when the training job transitioned to the current secondary status state.
EndTime (datetime) --
A timestamp that shows when the training job transitioned out of this secondary status state into another secondary status state or when the training job has ended.
StatusMessage (string) --
A detailed description of the progress within a secondary status.
Amazon SageMaker provides secondary statuses and status messages that apply to each of them:
Starting
Starting the training job.
Launching requested ML instances.
Insufficient capacity error from EC2 while launching instances, retrying!
Launched instance was unhealthy, replacing it!
Preparing the instances for training.
Training
Downloading the training image.
Training image download completed. Training in progress.
Warning
Status messages are subject to change. Therefore, we recommend not including them in code that programmatically initiates actions. For examples, don't use status messages in if statements.
To have an overview of your training job's progress, view TrainingJobStatus and SecondaryStatus in DescribeTrainingJob , and StatusMessage together. For example, at the start of a training job, you might see the following:
FinalMetricDataList (list) --
A list of final metric values that are set when the training job completes. Used only if the training job was configured to use metrics.
(dict) --
The name, value, and date and time of a metric that was emitted to Amazon CloudWatch.
MetricName (string) --
The name of the metric.
Value (float) --
The value of the metric.
Timestamp (datetime) --
The date and time that the algorithm emitted the metric.
EnableNetworkIsolation (boolean) --
If the TrainingJob was created with network isolation, the value is set to true . If network isolation is enabled, nodes can't communicate beyond the VPC they run in.
EnableInterContainerTrafficEncryption (boolean) --
To encrypt all communications between ML compute instances in distributed training, choose True . Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.
EnableManagedSpotTraining (boolean) --
When true, enables managed spot training using Amazon EC2 Spot instances to run training jobs instead of on-demand instances. For more information, see Managed Spot Training .
CheckpointConfig (dict) --
Contains information about the output location for managed spot training checkpoint data.
S3Uri (string) --
Identifies the S3 path where you want Amazon SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix .
LocalPath (string) --
(Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/ .
TrainingTimeInSeconds (integer) --
The training time in seconds.
BillableTimeInSeconds (integer) --
The billable time in seconds.
DebugHookConfig (dict) --
Configuration information for the Debugger hook parameters, metric and tensor collections, and storage paths. To learn more about how to configure the DebugHookConfig parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
LocalPath (string) --
Path to local storage location for metrics and tensors. Defaults to /opt/ml/output/tensors/ .
S3OutputPath (string) --
Path to Amazon S3 storage location for metrics and tensors.
HookParameters (dict) --
Configuration information for the Debugger hook parameters.
CollectionConfigurations (list) --
Configuration information for Debugger tensor collections. To learn more about how to configure the CollectionConfiguration parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
(dict) --
Configuration information for the Debugger output tensor collections.
CollectionName (string) --
The name of the tensor collection. The name must be unique relative to other rule configuration names.
CollectionParameters (dict) --
Parameter values for the tensor collection. The allowed parameters are "name" , "include_regex" , "reduction_config" , "save_config" , "tensor_names" , and "save_histogram" .
ExperimentConfig (dict) --
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
ExperimentName (string) --
The name of an existing experiment to associate the trial component with.
TrialName (string) --
The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
TrialComponentDisplayName (string) --
The display name for the trial component. If this key isn't specified, the display name is the trial component name.
DebugRuleConfigurations (list) --
Information about the debug rule configuration.
(dict) --
Configuration information for SageMaker Debugger rules for debugging. To learn more about how to configure the DebugRuleConfiguration parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
RuleConfigurationName (string) --
The name of the rule configuration. It must be unique relative to other rule configuration names.
LocalPath (string) --
Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/ .
S3OutputPath (string) --
Path to Amazon S3 storage location for rules.
RuleEvaluatorImage (string) --
The Amazon Elastic Container (ECR) Image for the managed rule evaluation.
InstanceType (string) --
The instance type to deploy a Debugger custom rule for debugging a training job.
VolumeSizeInGB (integer) --
The size, in GB, of the ML storage volume attached to the processing instance.
RuleParameters (dict) --
Runtime configuration for rule container.
TensorBoardOutputConfig (dict) --
Configuration of storage locations for the Debugger TensorBoard output data.
LocalPath (string) --
Path to local storage location for tensorBoard output. Defaults to /opt/ml/output/tensorboard .
S3OutputPath (string) --
Path to Amazon S3 storage location for TensorBoard output.
DebugRuleEvaluationStatuses (list) --
Information about the evaluation status of the rules for the training job.
(dict) --
Information about the status of the rule evaluation.
RuleConfigurationName (string) --
The name of the rule configuration.
RuleEvaluationJobArn (string) --
The Amazon Resource Name (ARN) of the rule evaluation job.
RuleEvaluationStatus (string) --
Status of the rule evaluation.
StatusDetails (string) --
Details from the rule evaluation.
LastModifiedTime (datetime) --
Timestamp when the rule evaluation status was last modified.
Environment (dict) --
The environment variables to set in the Docker container.
RetryStrategy (dict) --
The number of times to retry the job when the job fails due to an InternalServerError .
MaximumRetryAttempts (integer) --
The number of times to retry the job. When the job is retried, it's SecondaryStatus is changed to STARTING .
Tags (list) --
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
ProcessingJob (dict) --
Information about a processing job that's the source of a trial component.
ProcessingInputs (list) --
List of input configurations for the processing job.
(dict) --
The inputs for a processing job. The processing input must specify exactly one of either S3Input or DatasetDefinition types.
InputName (string) --
The name for the processing job input.
AppManaged (boolean) --
When True , input operations such as data download are managed natively by the processing job application. When False (default), input operations are managed by Amazon SageMaker.
S3Input (dict) --
Configuration for downloading input data from Amazon S3 into the processing container.
S3Uri (string) --
The URI of the Amazon S3 prefix Amazon SageMaker downloads data required to run a processing job.
LocalPath (string) --
The local path in your container where you want Amazon SageMaker to write input data to. LocalPath is an absolute path to the input data and must begin with /opt/ml/processing/ . LocalPath is a required parameter when AppManaged is False (default).
S3DataType (string) --
Whether you use an S3Prefix or a ManifestFile for the data type. If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for the processing job. If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for the processing job.
S3InputMode (string) --
Whether to use File or Pipe input mode. In File mode, Amazon SageMaker copies the data from the input source onto the local ML storage volume before starting your processing container. This is the most commonly used input mode. In Pipe mode, Amazon SageMaker streams input data from the source directly to your processing container into named pipes without using the ML storage volume.
S3DataDistributionType (string) --
Whether to distribute the data from Amazon S3 to all processing instances with FullyReplicated , or whether the data from Amazon S3 is shared by Amazon S3 key, downloading one shard of data to each processing instance.
S3CompressionType (string) --
Whether to GZIP-decompress the data in Amazon S3 as it is streamed into the processing container. Gzip can only be used when Pipe mode is specified as the S3InputMode . In Pipe mode, Amazon SageMaker streams input data from the source directly to your container without using the EBS volume.
DatasetDefinition (dict) --
Configuration for a Dataset Definition input.
AthenaDatasetDefinition (dict) --
Configuration for Athena Dataset Definition input.
Catalog (string) --
The name of the data catalog used in Athena query execution.
Database (string) --
The name of the database used in the Athena query execution.
QueryString (string) --
The SQL query statements, to be executed.
WorkGroup (string) --
The name of the workgroup in which the Athena query is being started.
OutputS3Uri (string) --
The location in Amazon S3 where Athena query results are stored.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data generated from an Athena query execution.
OutputFormat (string) --
The data storage format for Athena query results.
OutputCompression (string) --
The compression used for Athena query results.
RedshiftDatasetDefinition (dict) --
Configuration for Redshift Dataset Definition input.
ClusterId (string) --
The Redshift cluster Identifier.
Database (string) --
The name of the Redshift database used in Redshift query execution.
DbUser (string) --
The database user name used in Redshift query execution.
QueryString (string) --
The SQL query statements to be executed.
ClusterRoleArn (string) --
The IAM role attached to your Redshift cluster that Amazon SageMaker uses to generate datasets.
OutputS3Uri (string) --
The location in Amazon S3 where the Redshift query results are stored.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data from a Redshift execution.
OutputFormat (string) --
The data storage format for Redshift query results.
OutputCompression (string) --
The compression used for Redshift query results.
LocalPath (string) --
The local path where you want Amazon SageMaker to download the Dataset Definition inputs to run a processing job. LocalPath is an absolute path to the input data. This is a required parameter when AppManaged is False (default).
DataDistributionType (string) --
Whether the generated dataset is FullyReplicated or ShardedByS3Key (default).
InputMode (string) --
Whether to use File or Pipe input mode. In File (default) mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode. In Pipe mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume.
ProcessingOutputConfig (dict) --
Configuration for uploading output from the processing container.
Outputs (list) --
An array of outputs configuring the data to upload from the processing container.
(dict) --
Describes the results of a processing job. The processing output must specify exactly one of either S3Output or FeatureStoreOutput types.
OutputName (string) --
The name for the processing job output.
S3Output (dict) --
Configuration for processing job outputs in Amazon S3.
S3Uri (string) --
A URI that identifies the Amazon S3 bucket where you want Amazon SageMaker to save the results of a processing job.
LocalPath (string) --
The local path of a directory where you want Amazon SageMaker to upload its contents to Amazon S3. LocalPath is an absolute path to a directory containing output files. This directory will be created by the platform and exist when your container's entrypoint is invoked.
S3UploadMode (string) --
Whether to upload the results of the processing job continuously or after the job completes.
FeatureStoreOutput (dict) --
Configuration for processing job outputs in Amazon SageMaker Feature Store. This processing output type is only supported when AppManaged is specified.
FeatureGroupName (string) --
The name of the Amazon SageMaker FeatureGroup to use as the destination for processing job output. Note that your processing script is responsible for putting records into your Feature Store.
AppManaged (boolean) --
When True , output operations such as data upload are managed natively by the processing job application. When False (default), output operations are managed by Amazon SageMaker.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the processing job output. KmsKeyId can be an ID of a KMS key, ARN of a KMS key, alias of a KMS key, or alias of a KMS key. The KmsKeyId is applied to all outputs.
ProcessingJobName (string) --
The name of the processing job.
ProcessingResources (dict) --
Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job. In distributed training, you specify more than one instance.
ClusterConfig (dict) --
The configuration for the resources in a cluster used to run the processing job.
InstanceCount (integer) --
The number of ML compute instances to use in the processing job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
InstanceType (string) --
The ML compute instance type for the processing job.
VolumeSizeInGB (integer) --
The size of the ML storage volume in gigabytes that you want to provision. You must specify sufficient ML storage for your scenario.
Note
Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for processing, Amazon SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can't request a VolumeSizeInGB greater than the total size of the local instance storage.
For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes .
VolumeKmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the processing job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
StoppingCondition (dict) --
Configures conditions under which the processing job should be stopped, such as how long the processing job has been running. After the condition is met, the processing job is stopped.
MaxRuntimeInSeconds (integer) --
Specifies the maximum runtime in seconds.
AppSpecification (dict) --
Configuration to run a processing job in a specified container image.
ImageUri (string) --
The container image to be run by the processing job.
ContainerEntrypoint (list) --
The entrypoint for a container used to run a processing job.
ContainerArguments (list) --
The arguments for a container used to run a processing job.
Environment (dict) --
Sets the environment variables in the Docker container.
NetworkConfig (dict) --
Networking options for a job, such as network traffic encryption between containers, whether to allow inbound and outbound network calls to and from containers, and the VPC subnets and security groups to use for VPC-enabled jobs.
EnableInterContainerTrafficEncryption (boolean) --
Whether to encrypt all communications between distributed processing jobs. Choose True to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer.
EnableNetworkIsolation (boolean) --
Whether to allow inbound and outbound network calls to and from the containers used for the processing job.
VpcConfig (dict) --
Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud .
SecurityGroupIds (list) --
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
Subnets (list) --
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
RoleArn (string) --
The ARN of the role used to create the processing job.
ExperimentConfig (dict) --
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
ExperimentName (string) --
The name of an existing experiment to associate the trial component with.
TrialName (string) --
The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
TrialComponentDisplayName (string) --
The display name for the trial component. If this key isn't specified, the display name is the trial component name.
ProcessingJobArn (string) --
The ARN of the processing job.
ProcessingJobStatus (string) --
The status of the processing job.
ExitMessage (string) --
A string, up to one KB in size, that contains metadata from the processing container when the processing job exits.
FailureReason (string) --
A string, up to one KB in size, that contains the reason a processing job failed, if it failed.
ProcessingEndTime (datetime) --
The time that the processing job ended.
ProcessingStartTime (datetime) --
The time that the processing job started.
LastModifiedTime (datetime) --
The time the processing job was last modified.
CreationTime (datetime) --
The time the processing job was created.
MonitoringScheduleArn (string) --
The ARN of a monitoring schedule for an endpoint associated with this processing job.
AutoMLJobArn (string) --
The Amazon Resource Name (ARN) of the AutoML job associated with this processing job.
TrainingJobArn (string) --
The ARN of the training job associated with this processing job.
Tags (list) --
An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
TransformJob (dict) --
Information about a transform job that's the source of a trial component.
TransformJobName (string) --
The name of the transform job.
TransformJobArn (string) --
The Amazon Resource Name (ARN) of the transform job.
TransformJobStatus (string) --
The status of the transform job.
Transform job statuses are:
FailureReason (string) --
If the transform job failed, the reason it failed.
ModelName (string) --
The name of the model associated with the transform job.
MaxConcurrentTransforms (integer) --
The maximum number of parallel requests that can be sent to each instance in a transform job. If MaxConcurrentTransforms is set to 0 or left unset, SageMaker checks the optional execution-parameters to determine the settings for your chosen algorithm. If the execution-parameters endpoint is not enabled, the default value is 1. For built-in algorithms, you don't need to set a value for MaxConcurrentTransforms .
ModelClientConfig (dict) --
Configures the timeout and maximum number of retries for processing a transform job invocation.
InvocationsTimeoutInSeconds (integer) --
The timeout value in seconds for an invocation request.
InvocationsMaxRetries (integer) --
The maximum number of retries when invocation requests are failing.
MaxPayloadInMB (integer) --
The maximum allowed size of the payload, in MB. A payload is the data portion of a record (without metadata). The value in MaxPayloadInMB must be greater than, or equal to, the size of a single record. To estimate the size of a record in MB, divide the size of your dataset by the number of records. To ensure that the records fit within the maximum payload size, we recommend using a slightly larger value. The default value is 6 MB. For cases where the payload might be arbitrarily large and is transmitted using HTTP chunked encoding, set the value to 0. This feature works only in supported algorithms. Currently, SageMaker built-in algorithms do not support HTTP chunked encoding.
BatchStrategy (string) --
Specifies the number of records to include in a mini-batch for an HTTP inference request. A record is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record.
Environment (dict) --
The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
TransformInput (dict) --
Describes the input source of a transform job and the way the transform job consumes it.
DataSource (dict) --
Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) --
If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.
The following values are compatible: ManifestFile , S3Prefix
The following value is not compatible: AugmentedManifestFile
S3Uri (string) --
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
ContentType (string) --
The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
CompressionType (string) --
If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None .
SplitType (string) --
The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None , which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:
When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord , Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord , Amazon SageMaker sends individual records in each request.
Note
Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord . Padding is not removed if the value of BatchStrategy is set to MultiRecord .
For more information about RecordIO , see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord , see Consuming TFRecord data in the TensorFlow documentation.
TransformOutput (dict) --
Describes the results of a transform job.
S3OutputPath (string) --
The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, s3://bucket-name/key-name-prefix .
For every S3 object used as input for the transform job, batch transform stores the transformed data with an .``out`` suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at s3://bucket-name/input-name-prefix/dataset01/data.csv , batch transform stores the transformed data at s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out . Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .``out`` file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.
Accept (string) --
The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
AssembleWith (string) --
Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify None . To add a newline character at the end of every transformed record, specify Line .
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
TransformResources (dict) --
Describes the resources, including ML instance types and ML instance count, to use for transform job.
InstanceType (string) --
The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or ml.m5.large instance types.
InstanceCount (integer) --
The number of ML compute instances to use in the transform job. For distributed transform jobs, specify a value greater than 1. The default value is 1 .
VolumeKmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be any of the following formats:
CreationTime (datetime) --
A timestamp that shows when the transform Job was created.
TransformStartTime (datetime) --
Indicates when the transform job starts on ML instances. You are billed for the time interval between this time and the value of TransformEndTime .
TransformEndTime (datetime) --
Indicates when the transform job has been completed, or has stopped or failed. You are billed for the time interval between this time and the value of TransformStartTime .
LabelingJobArn (string) --
The Amazon Resource Name (ARN) of the labeling job that created the transform job.
AutoMLJobArn (string) --
The Amazon Resource Name (ARN) of the AutoML job that created the transform job.
DataProcessing (dict) --
The data structure used to specify the data to be used for inference in a batch transform job and to associate the data that is relevant to the prediction results in the output. The input filter provided allows you to exclude input data that is not needed for inference in a batch transform job. The output filter provided allows you to include input data relevant to interpreting the predictions in the output from the job. For more information, see Associate Prediction Results with their Corresponding Input Records .
InputFilter (string) --
A JSONPath expression used to select a portion of the input data to pass to the algorithm. Use the InputFilter parameter to exclude fields, such as an ID column, from the input. If you want Amazon SageMaker to pass the entire input dataset to the algorithm, accept the default value $ .
Examples: "$" , "$[1:]" , "$.features"
OutputFilter (string) --
A JSONPath expression used to select a portion of the joined dataset to save in the output file for a batch transform job. If you want Amazon SageMaker to store the entire input dataset in the output file, leave the default value, $ . If you specify indexes that aren't within the dimension size of the joined dataset, you get an error.
Examples: "$" , "$[0,5:]" , "$['id','SageMakerOutput']"
JoinSource (string) --
Specifies the source of the data to join with the transformed data. The valid values are None and Input . The default value is None , which specifies not to join the input with the transformed data. If you want the batch transform job to join the original input data with the transformed data, set JoinSource to Input . You can specify OutputFilter as an additional filter to select a portion of the joined dataset and store it in the output file.
For JSON or JSONLines objects, such as a JSON array, Amazon SageMaker adds the transformed data to the input JSON object in an attribute called SageMakerOutput . The joined result for JSON must be a key-value pair object. If the input is not a key-value pair object, Amazon SageMaker creates a new JSON file. In the new JSON file, and the input data is stored under the SageMakerInput key and the results are stored in SageMakerOutput .
For CSV data, Amazon SageMaker takes each row as a JSON array and joins the transformed data with the input by appending each transformed row to the end of the input. The joined data has the original input data followed by the transformed data and the output is a CSV file.
For information on how joining in applied, see Workflow for Associating Inferences with Input Records .
ExperimentConfig (dict) --
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
ExperimentName (string) --
The name of an existing experiment to associate the trial component with.
TrialName (string) --
The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
TrialComponentDisplayName (string) --
The display name for the trial component. If this key isn't specified, the display name is the trial component name.
Tags (list) --
A list of tags associated with the transform job.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
Tags (list) --
The list of tags that are associated with the component. You can use Search API to search on the tags.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
Parents (list) --
An array of the parents of the component. A parent is a trial the component is associated with and the experiment the trial is part of. A component might not have any parents.
(dict) --
The trial that a trial component is associated with and the experiment the trial is part of. A component might not be associated with a trial. A component can be associated with multiple trials.
TrialName (string) --
The name of the trial.
ExperimentName (string) --
The name of the experiment.
Endpoint (dict) --
A hosted endpoint for real-time inference.
EndpointName (string) --
The name of the endpoint.
EndpointArn (string) --
The Amazon Resource Name (ARN) of the endpoint.
EndpointConfigName (string) --
The endpoint configuration associated with the endpoint.
ProductionVariants (list) --
A list of the production variants hosted on the endpoint. Each production variant is a model.
(dict) --
Describes weight and capacities for a production variant associated with an endpoint. If you sent a request to the UpdateEndpointWeightsAndCapacities API and the endpoint status is Updating , you get different desired and current values.
VariantName (string) --
The name of the variant.
DeployedImages (list) --
An array of DeployedImage objects that specify the Amazon EC2 Container Registry paths of the inference images deployed on instances of this ProductionVariant .
(dict) --
Gets the Amazon EC2 Container Registry path of the docker image of the model that is hosted in this ProductionVariant .
If you used the registry/repository[:tag] form to specify the image path of the primary container when you created the model hosted in this ProductionVariant , the path resolves to a path of the form registry/repository[@digest] . A digest is a hash value that identifies a specific version of an image. For information about Amazon ECR paths, see Pulling an Image in the Amazon ECR User Guide .
SpecifiedImage (string) --
The image path you specified when you created the model.
ResolvedImage (string) --
The specific digest path of the image hosted in this ProductionVariant .
ResolutionTime (datetime) --
The date and time when the image path for the model resolved to the ResolvedImage
CurrentWeight (float) --
The weight associated with the variant.
DesiredWeight (float) --
The requested weight, as specified in the UpdateEndpointWeightsAndCapacities request.
CurrentInstanceCount (integer) --
The number of instances associated with the variant.
DesiredInstanceCount (integer) --
The number of instances requested in the UpdateEndpointWeightsAndCapacities request.
DataCaptureConfig (dict) --
EndpointStatus (string) --
The status of the endpoint.
FailureReason (string) --
If the endpoint failed, the reason it failed.
CreationTime (datetime) --
The time that the endpoint was created.
LastModifiedTime (datetime) --
The last time the endpoint was modified.
MonitoringSchedules (list) --
A list of monitoring schedules for the endpoint. For information about model monitoring, see Amazon SageMaker Model Monitor .
(dict) --
A schedule for a model monitoring job. For information about model monitor, see Amazon SageMaker Model Monitor .
MonitoringScheduleArn (string) --
The Amazon Resource Name (ARN) of the monitoring schedule.
MonitoringScheduleName (string) --
The name of the monitoring schedule.
MonitoringScheduleStatus (string) --
The status of the monitoring schedule. This can be one of the following values.
MonitoringType (string) --
The type of the monitoring job definition to schedule.
FailureReason (string) --
If the monitoring schedule failed, the reason it failed.
CreationTime (datetime) --
The time that the monitoring schedule was created.
LastModifiedTime (datetime) --
The last time the monitoring schedule was changed.
MonitoringScheduleConfig (dict) --
Configures the monitoring schedule and defines the monitoring job.
ScheduleConfig (dict) --
Configures the monitoring schedule.
ScheduleExpression (string) --
A cron expression that describes details about the monitoring schedule.
Currently the only supported cron expressions are:
For example, the following are valid cron expressions:
To support running every 6, 12 hours, the following are also supported:
cron(0 [00-23]/[01-24] ? * * *)
For example, the following are valid cron expressions:
Note
MonitoringJobDefinition (dict) --
Defines the monitoring job.
BaselineConfig (dict) --
Baseline configuration used to validate that the data conforms to the specified constraints and statistics
BaseliningJobName (string) --
The name of the job that performs baselining for the monitoring job.
ConstraintsResource (dict) --
The baseline constraint file in Amazon S3 that the current monitoring job should validated against.
S3Uri (string) --
The Amazon S3 URI for the constraints resource.
StatisticsResource (dict) --
The baseline statistics file in Amazon S3 that the current monitoring job should be validated against.
S3Uri (string) --
The Amazon S3 URI for the statistics resource.
MonitoringInputs (list) --
The array of inputs for the monitoring job. Currently we support monitoring an Amazon SageMaker Endpoint.
(dict) --
The inputs for a monitoring job.
EndpointInput (dict) --
The endpoint for a monitoring job.
EndpointName (string) --
An endpoint in customer's account which has enabled DataCaptureConfig enabled.
LocalPath (string) --
Path to the filesystem where the endpoint data is available to the container.
S3InputMode (string) --
Whether the Pipe or File is used as the input mode for transfering data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .
S3DataDistributionType (string) --
Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated
FeaturesAttribute (string) --
The attributes of the input data that are the input features.
InferenceAttribute (string) --
The attribute of the input data that represents the ground truth label.
ProbabilityAttribute (string) --
In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute (float) --
The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset (string) --
If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
EndTimeOffset (string) --
If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
MonitoringOutputConfig (dict) --
The array of outputs from the monitoring job to be uploaded to Amazon Simple Storage Service (Amazon S3).
MonitoringOutputs (list) --
Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.
(dict) --
The output object for a monitoring job.
S3Output (dict) --
The Amazon S3 storage location where the results of a monitoring job are saved.
S3Uri (string) --
A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.
LocalPath (string) --
The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.
S3UploadMode (string) --
Whether to upload the results of the monitoring job continuously or after the job completes.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.
MonitoringResources (dict) --
Identifies the resources, ML compute instances, and ML storage volumes to deploy for a monitoring job. In distributed processing, you specify more than one instance.
ClusterConfig (dict) --
The configuration for the cluster resources used to run the processing job.
InstanceCount (integer) --
The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
InstanceType (string) --
The ML compute instance type for the processing job.
VolumeSizeInGB (integer) --
The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.
VolumeKmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job.
MonitoringAppSpecification (dict) --
Configures the monitoring job to run a specified Docker container image.
ImageUri (string) --
The container image to be run by the monitoring job.
ContainerEntrypoint (list) --
Specifies the entrypoint for a container used to run the monitoring job.
ContainerArguments (list) --
An array of arguments for the container used to run the monitoring job.
RecordPreprocessorSourceUri (string) --
An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flatted json so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.
PostAnalyticsProcessorSourceUri (string) --
An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.
StoppingCondition (dict) --
Specifies a time limit for how long the monitoring job is allowed to run.
MaxRuntimeInSeconds (integer) --
The maximum runtime allowed in seconds.
Note
The MaxRuntimeInSeconds cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.
Environment (dict) --
Sets the environment variables in the Docker container.
NetworkConfig (dict) --
Specifies networking options for an monitoring job.
EnableInterContainerTrafficEncryption (boolean) --
Whether to encrypt all communications between distributed processing jobs. Choose True to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer.
EnableNetworkIsolation (boolean) --
Whether to allow inbound and outbound network calls to and from the containers used for the processing job.
VpcConfig (dict) --
Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud .
SecurityGroupIds (list) --
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
Subnets (list) --
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
RoleArn (string) --
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
MonitoringJobDefinitionName (string) --
The name of the monitoring job definition to schedule.
MonitoringType (string) --
The type of the monitoring job definition to schedule.
EndpointName (string) --
The endpoint that hosts the model being monitored.
LastMonitoringExecutionSummary (dict) --
Summary of information about the last monitoring job to run.
MonitoringScheduleName (string) --
The name of the monitoring schedule.
ScheduledTime (datetime) --
The time the monitoring job was scheduled.
CreationTime (datetime) --
The time at which the monitoring job was created.
LastModifiedTime (datetime) --
A timestamp that indicates the last time the monitoring job was modified.
MonitoringExecutionStatus (string) --
The status of the monitoring job.
ProcessingJobArn (string) --
The Amazon Resource Name (ARN) of the monitoring job.
EndpointName (string) --
The name of the endpoint used to run the monitoring job.
FailureReason (string) --
Contains the reason a monitoring job failed, if it failed.
MonitoringJobDefinitionName (string) --
The name of the monitoring job.
MonitoringType (string) --
The type of the monitoring job.
Tags (list) --
A list of the tags associated with the monitoring schedlue. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
Tags (list) --
A list of the tags associated with the endpoint. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
ModelPackage (dict) --
A versioned model that can be deployed for SageMaker inference.
ModelPackageName (string) --
The name of the model.
ModelPackageGroupName (string) --
The model group to which the model belongs.
ModelPackageVersion (integer) --
The version number of a versioned model.
ModelPackageArn (string) --
The Amazon Resource Name (ARN) of the model package.
ModelPackageDescription (string) --
The description of the model package.
CreationTime (datetime) --
The time that the model package was created.
InferenceSpecification (dict) --
Defines how to perform inference generation after a training job is run.
Containers (list) --
The Amazon ECR registry path of the Docker image that contains the inference code.
(dict) --
Describes the Docker container for the model package.
ContainerHostname (string) --
The DNS host name for the Docker container.
Image (string) --
The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by Amazon SageMaker, the inference code must meet Amazon SageMaker requirements. Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
ImageDigest (string) --
An MD5 hash of the training algorithm that identifies the Docker image used for training.
ModelDataUrl (string) --
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
Note
The model artifacts must be in an S3 bucket that is in the same region as the model package.
ProductId (string) --
The Amazon Web Services Marketplace product ID of the model package.
Environment (dict) --
The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
SupportedTransformInstanceTypes (list) --
A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
This parameter is required for unversioned models, and optional for versioned models.
SupportedRealtimeInferenceInstanceTypes (list) --
A list of the instance types that are used to generate inferences in real-time.
This parameter is required for unversioned models, and optional for versioned models.
SupportedContentTypes (list) --
The supported MIME types for the input data.
SupportedResponseMIMETypes (list) --
The supported MIME types for the output data.
SourceAlgorithmSpecification (dict) --
A list of algorithms that were used to create a model package.
SourceAlgorithms (list) --
A list of the algorithms that were used to create a model package.
(dict) --
Specifies an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your Amazon SageMaker account or an algorithm in Amazon Web Services Marketplace that you are subscribed to.
ModelDataUrl (string) --
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
Note
The model artifacts must be in an S3 bucket that is in the same region as the algorithm.
AlgorithmName (string) --
The name of an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your Amazon SageMaker account or an algorithm in Amazon Web Services Marketplace that you are subscribed to.
ValidationSpecification (dict) --
Specifies batch transform jobs that Amazon SageMaker runs to validate your model package.
ValidationRole (string) --
The IAM roles to be used for the validation of the model package.
ValidationProfiles (list) --
An array of ModelPackageValidationProfile objects, each of which specifies a batch transform job that Amazon SageMaker runs to validate your model package.
(dict) --
Contains data, such as the inputs and targeted instance types that are used in the process of validating the model package.
The data provided in the validation profile is made available to your buyers on Amazon Web Services Marketplace.
ProfileName (string) --
The name of the profile for the model package.
TransformJobDefinition (dict) --
The TransformJobDefinition object that describes the transform job used for the validation of the model package.
MaxConcurrentTransforms (integer) --
The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1.
MaxPayloadInMB (integer) --
The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata).
BatchStrategy (string) --
A string that determines the number of records included in a single mini-batch.
SingleRecord means only one record is used per mini-batch. MultiRecord means a mini-batch is set to contain as many records that can fit within the MaxPayloadInMB limit.
Environment (dict) --
The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
TransformInput (dict) --
A description of the input source and the way the transform job consumes it.
DataSource (dict) --
Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) --
If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.
The following values are compatible: ManifestFile , S3Prefix
The following value is not compatible: AugmentedManifestFile
S3Uri (string) --
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
ContentType (string) --
The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
CompressionType (string) --
If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None .
SplitType (string) --
The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None , which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:
When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord , Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord , Amazon SageMaker sends individual records in each request.
Note
Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord . Padding is not removed if the value of BatchStrategy is set to MultiRecord .
For more information about RecordIO , see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord , see Consuming TFRecord data in the TensorFlow documentation.
TransformOutput (dict) --
Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
S3OutputPath (string) --
The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, s3://bucket-name/key-name-prefix .
For every S3 object used as input for the transform job, batch transform stores the transformed data with an .``out`` suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at s3://bucket-name/input-name-prefix/dataset01/data.csv , batch transform stores the transformed data at s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out . Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .``out`` file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.
Accept (string) --
The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
AssembleWith (string) --
Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify None . To add a newline character at the end of every transformed record, specify Line .
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
TransformResources (dict) --
Identifies the ML compute instances for the transform job.
InstanceType (string) --
The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or ml.m5.large instance types.
InstanceCount (integer) --
The number of ML compute instances to use in the transform job. For distributed transform jobs, specify a value greater than 1. The default value is 1 .
VolumeKmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be any of the following formats:
ModelPackageStatus (string) --
The status of the model package. This can be one of the following values.
ModelPackageStatusDetails (dict) --
Specifies the validation and image scan statuses of the model package.
ValidationStatuses (list) --
The validation status of the model package.
(dict) --
Represents the overall status of a model package.
Name (string) --
The name of the model package for which the overall status is being reported.
Status (string) --
The current status.
FailureReason (string) --
if the overall status is Failed , the reason for the failure.
ImageScanStatuses (list) --
The status of the scan of the Docker image container for the model package.
(dict) --
Represents the overall status of a model package.
Name (string) --
The name of the model package for which the overall status is being reported.
Status (string) --
The current status.
FailureReason (string) --
if the overall status is Failed , the reason for the failure.
CertifyForMarketplace (boolean) --
Whether the model package is to be certified to be listed on Amazon Web Services Marketplace. For information about listing model packages on Amazon Web Services Marketplace, see List Your Algorithm or Model Package on Amazon Web Services Marketplace .
ModelApprovalStatus (string) --
The approval status of the model. This can be one of the following values.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
MetadataProperties (dict) --
Metadata properties of the tracking entity, trial, or trial component.
CommitId (string) --
The commit ID.
Repository (string) --
The repository.
GeneratedBy (string) --
The entity this entity was generated by.
ProjectId (string) --
The project ID.
ModelMetrics (dict) --
Metrics for the model.
ModelQuality (dict) --
Metrics that measure the quality of a model.
Statistics (dict) --
Model quality statistics.
Constraints (dict) --
Model quality constraints.
ModelDataQuality (dict) --
Metrics that measure the quality of the input data for a model.
Statistics (dict) --
Data quality statistics for a model.
Constraints (dict) --
Data quality constraints for a model.
Bias (dict) --
Metrics that measure bais in a model.
Report (dict) --
The bias report for a model
Explainability (dict) --
Metrics that help explain a model.
Report (dict) --
The explainability report for a model.
LastModifiedTime (datetime) --
The last time the model package was modified.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
ApprovalDescription (string) --
A description provided when the model approval is set.
Tags (list) --
A list of the tags associated with the model package. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
ModelPackageGroup (dict) --
A group of versioned models in the model registry.
ModelPackageGroupName (string) --
The name of the model group.
ModelPackageGroupArn (string) --
The Amazon Resource Name (ARN) of the model group.
ModelPackageGroupDescription (string) --
The description for the model group.
CreationTime (datetime) --
The time that the model group was created.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
ModelPackageGroupStatus (string) --
The status of the model group. This can be one of the following values.
Tags (list) --
A list of the tags associated with the model group. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
Pipeline (dict) --
A SageMaker Model Building Pipeline instance.
PipelineArn (string) --
The Amazon Resource Name (ARN) of the pipeline.
PipelineName (string) --
The name of the pipeline.
PipelineDisplayName (string) --
The display name of the pipeline.
PipelineDescription (string) --
The description of the pipeline.
RoleArn (string) --
The Amazon Resource Name (ARN) of the role that created the pipeline.
PipelineStatus (string) --
The status of the pipeline.
CreationTime (datetime) --
The creation time of the pipeline.
LastModifiedTime (datetime) --
The time that the pipeline was last modified.
LastRunTime (datetime) --
The time when the pipeline was last run.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
Tags (list) --
A list of tags that apply to the pipeline.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
PipelineExecution (dict) --
An execution of a pipeline.
PipelineArn (string) --
The Amazon Resource Name (ARN) of the pipeline that was executed.
PipelineExecutionArn (string) --
The Amazon Resource Name (ARN) of the pipeline execution.
PipelineExecutionDisplayName (string) --
The display name of the pipeline execution.
PipelineExecutionStatus (string) --
The status of the pipeline status.
PipelineExecutionDescription (string) --
The description of the pipeline execution.
PipelineExperimentConfig (dict) --
Specifies the names of the experiment and trial created by a pipeline.
ExperimentName (string) --
The name of the experiment.
TrialName (string) --
The name of the trial.
FailureReason (string) --
If the execution failed, a message describing why.
CreationTime (datetime) --
The creation time of the pipeline execution.
LastModifiedTime (datetime) --
The time that the pipeline execution was last modified.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
PipelineParameters (list) --
Contains a list of pipeline parameters. This list can be empty.
(dict) --
Assigns a value to a named Pipeline parameter.
Name (string) --
The name of the parameter to assign a value to. This parameter name must match a named parameter in the pipeline definition.
Value (string) --
The literal value for the parameter.
FeatureGroup (dict) --
Amazon SageMaker Feature Store stores features in a collection called Feature Group. A Feature Group can be visualized as a table which has rows, with a unique identifier for each row where each column in the table is a feature. In principle, a Feature Group is composed of features and values per features.
FeatureGroupArn (string) --
The Amazon Resource Name (ARN) of a FeatureGroup .
FeatureGroupName (string) --
The name of the FeatureGroup .
RecordIdentifierFeatureName (string) --
The name of the Feature whose value uniquely identifies a Record defined in the FeatureGroup FeatureDefinitions .
EventTimeFeatureName (string) --
The name of the feature that stores the EventTime of a Record in a FeatureGroup .
A EventTime is point in time when a new event occurs that corresponds to the creation or update of a Record in FeatureGroup . All Records in the FeatureGroup must have a corresponding EventTime .
FeatureDefinitions (list) --
A list of Feature s. Each Feature must include a FeatureName and a FeatureType .
Valid FeatureType s are Integral , Fractional and String .
FeatureName s cannot be any of the following: is_deleted , write_time , api_invocation_time .
You can create up to 2,500 FeatureDefinition s per FeatureGroup .
(dict) --
A list of features. You must include FeatureName and FeatureType . Valid feature FeatureType s are Integral , Fractional and String .
FeatureName (string) --
The name of a feature. The type must be a string. FeatureName cannot be any of the following: is_deleted , write_time , api_invocation_time .
FeatureType (string) --
The value type of a feature. Valid values are Integral, Fractional, or String.
CreationTime (datetime) --
The time a FeatureGroup was created.
OnlineStoreConfig (dict) --
Use this to specify the Amazon Web Services Key Management Service (KMS) Key ID, or KMSKeyId , for at rest data encryption. You can turn OnlineStore on or off by specifying the EnableOnlineStore flag at General Assembly; the default value is False .
SecurityConfig (dict) --
Use to specify KMS Key ID (KMSKeyId ) for at-rest encryption of your OnlineStore .
KmsKeyId (string) --
The ID of the Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker Feature Store uses to encrypt the Amazon S3 objects at rest using Amazon S3 server-side encryption.
The caller (either IAM user or IAM role) of CreateFeatureGroup must have below permissions to the OnlineStore KmsKeyId :
The caller (either IAM user or IAM role) to all DataPlane operations (PutRecord , GetRecord , DeleteRecord ) must have the following permissions to the KmsKeyId :
EnableOnlineStore (boolean) --
Turn OnlineStore off by specifying False for the EnableOnlineStore flag. Turn OnlineStore on by specifying True for the EnableOnlineStore flag.
The default value is False .
OfflineStoreConfig (dict) --
The configuration of an OfflineStore .
Provide an OfflineStoreConfig in a request to CreateFeatureGroup to create an OfflineStore .
To encrypt an OfflineStore using at rest data encryption, specify Amazon Web Services Key Management Service (KMS) key ID, or KMSKeyId , in S3StorageConfig .
S3StorageConfig (dict) --
The Amazon Simple Storage (Amazon S3) location of OfflineStore .
S3Uri (string) --
The S3 URI, or location in Amazon S3, of OfflineStore .
S3 URIs have a format similar to the following: s3://example-bucket/prefix/ .
KmsKeyId (string) --
The Amazon Web Services Key Management Service (KMS) key ID of the key used to encrypt any objects written into the OfflineStore S3 location.
The IAM roleARN that is passed as a parameter to CreateFeatureGroup must have below permissions to the KmsKeyId :
ResolvedOutputS3Uri (string) --
The S3 path where offline records are written.
DisableGlueTableCreation (boolean) --
Set to True to disable the automatic creation of an Amazon Web Services Glue table when configuring an OfflineStore .
DataCatalogConfig (dict) --
The meta data of the Glue table that is autogenerated when an OfflineStore is created.
TableName (string) --
The name of the Glue table.
Catalog (string) --
The name of the Glue table catalog.
Database (string) --
The name of the Glue table database.
RoleArn (string) --
The Amazon Resource Name (ARN) of the IAM execution role used to create the feature group.
FeatureGroupStatus (string) --
A FeatureGroup status.
OfflineStoreStatus (dict) --
The status of OfflineStore .
Status (string) --
An OfflineStore status.
BlockedReason (string) --
The justification for why the OfflineStoreStatus is Blocked (if applicable).
FailureReason (string) --
The reason that the FeatureGroup failed to be replicated in the OfflineStore . This is failure may be due to a failure to create a FeatureGroup in or delete a FeatureGroup from the OfflineStore .
Description (string) --
A free form description of a FeatureGroup .
Tags (list) --
Tags used to define a FeatureGroup .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
NextToken (string) --
If the result of the previous Search request was truncated, the response includes a NextToken. To retrieve the next set of results, use the token in the next request.
Notifies the pipeline that the execution of a callback step failed, along with a message describing why. When a callback step is run, the pipeline generates a callback token and includes the token in a message sent to Amazon Simple Queue Service (Amazon SQS).
See also: AWS API Documentation
Request Syntax
response = client.send_pipeline_execution_step_failure(
CallbackToken='string',
FailureReason='string',
ClientRequestToken='string'
)
[REQUIRED]
The pipeline generated token from the Amazon SQS queue.
A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.
This field is autopopulated if not provided.
dict
Response Syntax
{
'PipelineExecutionArn': 'string'
}
Response Structure
(dict) --
PipelineExecutionArn (string) --
The Amazon Resource Name (ARN) of the pipeline execution.
Exceptions
Notifies the pipeline that the execution of a callback step succeeded and provides a list of the step's output parameters. When a callback step is run, the pipeline generates a callback token and includes the token in a message sent to Amazon Simple Queue Service (Amazon SQS).
See also: AWS API Documentation
Request Syntax
response = client.send_pipeline_execution_step_success(
CallbackToken='string',
OutputParameters=[
{
'Name': 'string',
'Value': 'string'
},
],
ClientRequestToken='string'
)
[REQUIRED]
The pipeline generated token from the Amazon SQS queue.
A list of the output parameters of the callback step.
An output parameter of a pipeline step.
The name of the output parameter.
The value of the output parameter.
A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.
This field is autopopulated if not provided.
dict
Response Syntax
{
'PipelineExecutionArn': 'string'
}
Response Structure
(dict) --
PipelineExecutionArn (string) --
The Amazon Resource Name (ARN) of the pipeline execution.
Exceptions
Starts a previously stopped monitoring schedule.
Note
By default, when you successfully create a new schedule, the status of a monitoring schedule is scheduled .
See also: AWS API Documentation
Request Syntax
response = client.start_monitoring_schedule(
MonitoringScheduleName='string'
)
[REQUIRED]
The name of the schedule to start.
Exceptions
Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume. After configuring the notebook instance, Amazon SageMaker sets the notebook instance status to InService . A notebook instance's status must be InService before you can connect to your Jupyter notebook.
See also: AWS API Documentation
Request Syntax
response = client.start_notebook_instance(
NotebookInstanceName='string'
)
[REQUIRED]
The name of the notebook instance to start.
Exceptions
Starts a pipeline execution.
See also: AWS API Documentation
Request Syntax
response = client.start_pipeline_execution(
PipelineName='string',
PipelineExecutionDisplayName='string',
PipelineParameters=[
{
'Name': 'string',
'Value': 'string'
},
],
PipelineExecutionDescription='string',
ClientRequestToken='string'
)
[REQUIRED]
The name of the pipeline.
Contains a list of pipeline parameters. This list can be empty.
Assigns a value to a named Pipeline parameter.
The name of the parameter to assign a value to. This parameter name must match a named parameter in the pipeline definition.
The literal value for the parameter.
[REQUIRED]
A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.
This field is autopopulated if not provided.
dict
Response Syntax
{
'PipelineExecutionArn': 'string'
}
Response Structure
(dict) --
PipelineExecutionArn (string) --
The Amazon Resource Name (ARN) of the pipeline execution.
Exceptions
A method for forcing the termination of a running job.
See also: AWS API Documentation
Request Syntax
response = client.stop_auto_ml_job(
AutoMLJobName='string'
)
[REQUIRED]
The name of the object you are requesting.
Exceptions
Stops a model compilation job.
To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal. This gracefully shuts the job down. If the job hasn't stopped, it sends the SIGKILL signal.
When it receives a StopCompilationJob request, Amazon SageMaker changes the CompilationJobSummary$CompilationJobStatus of the job to Stopping . After Amazon SageMaker stops the job, it sets the CompilationJobSummary$CompilationJobStatus to Stopped .
See also: AWS API Documentation
Request Syntax
response = client.stop_compilation_job(
CompilationJobName='string'
)
[REQUIRED]
The name of the model compilation job to stop.
Exceptions
Request to stop an edge packaging job.
See also: AWS API Documentation
Request Syntax
response = client.stop_edge_packaging_job(
EdgePackagingJobName='string'
)
[REQUIRED]
The name of the edge packaging job.
Stops a running hyperparameter tuning job and all running training jobs that the tuning job launched.
All model artifacts output from the training jobs are stored in Amazon Simple Storage Service (Amazon S3). All data that the training jobs write to Amazon CloudWatch Logs are still available in CloudWatch. After the tuning job moves to the Stopped state, it releases all reserved resources for the tuning job.
See also: AWS API Documentation
Request Syntax
response = client.stop_hyper_parameter_tuning_job(
HyperParameterTuningJobName='string'
)
[REQUIRED]
The name of the tuning job to stop.
Exceptions
Stops a running labeling job. A job that is stopped cannot be restarted. Any results obtained before the job is stopped are placed in the Amazon S3 output bucket.
See also: AWS API Documentation
Request Syntax
response = client.stop_labeling_job(
LabelingJobName='string'
)
[REQUIRED]
The name of the labeling job to stop.
Exceptions
Stops a previously started monitoring schedule.
See also: AWS API Documentation
Request Syntax
response = client.stop_monitoring_schedule(
MonitoringScheduleName='string'
)
[REQUIRED]
The name of the schedule to stop.
Exceptions
Terminates the ML compute instance. Before terminating the instance, Amazon SageMaker disconnects the ML storage volume from it. Amazon SageMaker preserves the ML storage volume. Amazon SageMaker stops charging you for the ML compute instance when you call StopNotebookInstance .
To access data on the ML storage volume for a notebook instance that has been terminated, call the StartNotebookInstance API. StartNotebookInstance launches another ML compute instance, configures it, and attaches the preserved ML storage volume so you can continue your work.
See also: AWS API Documentation
Request Syntax
response = client.stop_notebook_instance(
NotebookInstanceName='string'
)
[REQUIRED]
The name of the notebook instance to terminate.
Stops a pipeline execution.
A pipeline execution won't stop while a callback step is running. When you call StopPipelineExecution on a pipeline execution with a running callback step, SageMaker Pipelines sends an additional Amazon SQS message to the specified SQS queue. The body of the SQS message contains a "Status" field which is set to "Stopping".
You should add logic to your Amazon SQS message consumer to take any needed action (for example, resource cleanup) upon receipt of the message followed by a call to SendPipelineExecutionStepSuccess or SendPipelineExecutionStepFailure .
Only when SageMaker Pipelines receives one of these calls will it stop the pipeline execution.
See also: AWS API Documentation
Request Syntax
response = client.stop_pipeline_execution(
PipelineExecutionArn='string',
ClientRequestToken='string'
)
[REQUIRED]
The Amazon Resource Name (ARN) of the pipeline execution.
[REQUIRED]
A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.
This field is autopopulated if not provided.
dict
Response Syntax
{
'PipelineExecutionArn': 'string'
}
Response Structure
(dict) --
PipelineExecutionArn (string) --
The Amazon Resource Name (ARN) of the pipeline execution.
Exceptions
Stops a processing job.
See also: AWS API Documentation
Request Syntax
response = client.stop_processing_job(
ProcessingJobName='string'
)
[REQUIRED]
The name of the processing job to stop.
Exceptions
Stops a training job. To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts, so the results of the training is not lost.
When it receives a StopTrainingJob request, Amazon SageMaker changes the status of the job to Stopping . After Amazon SageMaker stops the job, it sets the status to Stopped .
See also: AWS API Documentation
Request Syntax
response = client.stop_training_job(
TrainingJobName='string'
)
[REQUIRED]
The name of the training job to stop.
Exceptions
Stops a transform job.
When Amazon SageMaker receives a StopTransformJob request, the status of the job changes to Stopping . After Amazon SageMaker stops the job, the status is set to Stopped . When you stop a transform job before it is completed, Amazon SageMaker doesn't store the job's output in Amazon S3.
See also: AWS API Documentation
Request Syntax
response = client.stop_transform_job(
TransformJobName='string'
)
[REQUIRED]
The name of the transform job to stop.
Exceptions
Updates an action.
See also: AWS API Documentation
Request Syntax
response = client.update_action(
ActionName='string',
Description='string',
Status='Unknown'|'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
Properties={
'string': 'string'
},
PropertiesToRemove=[
'string',
]
)
[REQUIRED]
The name of the action to update.
The new list of properties. Overwrites the current property list.
A list of properties to remove.
dict
Response Syntax
{
'ActionArn': 'string'
}
Response Structure
(dict) --
ActionArn (string) --
The Amazon Resource Name (ARN) of the action.
Exceptions
Updates the properties of an AppImageConfig.
See also: AWS API Documentation
Request Syntax
response = client.update_app_image_config(
AppImageConfigName='string',
KernelGatewayImageConfig={
'KernelSpecs': [
{
'Name': 'string',
'DisplayName': 'string'
},
],
'FileSystemConfig': {
'MountPath': 'string',
'DefaultUid': 123,
'DefaultGid': 123
}
}
)
[REQUIRED]
The name of the AppImageConfig to update.
The new KernelGateway app to run on the image.
The specification of the Jupyter kernels in the image.
The specification of a Jupyter kernel.
The name of the Jupyter kernel in the image. This value is case sensitive.
The display name of the kernel.
The Amazon Elastic File System (EFS) storage configuration for a SageMaker image.
The path within the image to mount the user's EFS home directory. The directory should be empty. If not specified, defaults to /home/sagemaker-user .
The default POSIX user ID (UID). If not specified, defaults to 1000 .
The default POSIX group ID (GID). If not specified, defaults to 100 .
dict
Response Syntax
{
'AppImageConfigArn': 'string'
}
Response Structure
(dict) --
AppImageConfigArn (string) --
The Amazon Resource Name (ARN) for the AppImageConfig.
Exceptions
Updates an artifact.
See also: AWS API Documentation
Request Syntax
response = client.update_artifact(
ArtifactArn='string',
ArtifactName='string',
Properties={
'string': 'string'
},
PropertiesToRemove=[
'string',
]
)
[REQUIRED]
The Amazon Resource Name (ARN) of the artifact to update.
The new list of properties. Overwrites the current property list.
A list of properties to remove.
dict
Response Syntax
{
'ArtifactArn': 'string'
}
Response Structure
(dict) --
ArtifactArn (string) --
The Amazon Resource Name (ARN) of the artifact.
Exceptions
Updates the specified Git repository with the specified values.
See also: AWS API Documentation
Request Syntax
response = client.update_code_repository(
CodeRepositoryName='string',
GitConfig={
'SecretArn': 'string'
}
)
[REQUIRED]
The name of the Git repository to update.
The configuration of the git repository, including the URL and the Amazon Resource Name (ARN) of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the repository. The secret must have a staging label of AWSCURRENT and must be in the following format:
{"username": *UserName* , "password": *Password* }
The Amazon Resource Name (ARN) of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the git repository. The secret must have a staging label of AWSCURRENT and must be in the following format:
{"username": *UserName* , "password": *Password* }
dict
Response Syntax
{
'CodeRepositoryArn': 'string'
}
Response Structure
(dict) --
CodeRepositoryArn (string) --
The ARN of the Git repository.
Updates a context.
See also: AWS API Documentation
Request Syntax
response = client.update_context(
ContextName='string',
Description='string',
Properties={
'string': 'string'
},
PropertiesToRemove=[
'string',
]
)
[REQUIRED]
The name of the context to update.
The new list of properties. Overwrites the current property list.
A list of properties to remove.
dict
Response Syntax
{
'ContextArn': 'string'
}
Response Structure
(dict) --
ContextArn (string) --
The Amazon Resource Name (ARN) of the context.
Exceptions
Updates a fleet of devices.
See also: AWS API Documentation
Request Syntax
response = client.update_device_fleet(
DeviceFleetName='string',
RoleArn='string',
Description='string',
OutputConfig={
'S3OutputLocation': 'string',
'KmsKeyId': 'string',
'PresetDeploymentType': 'GreengrassV2Component',
'PresetDeploymentConfig': 'string'
},
EnableIotRoleAlias=True|False
)
[REQUIRED]
The name of the fleet.
[REQUIRED]
Output configuration for storing sample data collected by the fleet.
The Amazon Simple Storage (S3) bucker URI.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume after compilation job. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account.
The deployment type SageMaker Edge Manager will create. Currently only supports Amazon Web Services IoT Greengrass Version 2 components.
The configuration used to create deployment artifacts. Specify configuration options with a JSON string. The available configuration options for each type are:
Note
Amazon Web Services IoT Greengrass uses semantic versions for components. Semantic versions follow a*major.minor.patch* number system. For example, version 1.0.0 represents the first major release for a component. For more information, see the semantic version specification .
Whether to create an Amazon Web Services IoT Role Alias during device fleet creation. The name of the role alias generated will match this pattern: "SageMakerEdge-{DeviceFleetName}".
For example, if your device fleet is called "demo-fleet", the name of the role alias will be "SageMakerEdge-demo-fleet".
None
Exceptions
Updates one or more devices in a fleet.
See also: AWS API Documentation
Request Syntax
response = client.update_devices(
DeviceFleetName='string',
Devices=[
{
'DeviceName': 'string',
'Description': 'string',
'IotThingName': 'string'
},
]
)
[REQUIRED]
The name of the fleet the devices belong to.
[REQUIRED]
List of devices to register with Edge Manager agent.
Information of a particular device.
The name of the device.
Description of the device.
Amazon Web Services Internet of Things (IoT) object name.
None
Updates the default settings for new user profiles in the domain.
See also: AWS API Documentation
Request Syntax
response = client.update_domain(
DomainId='string',
DefaultUserSettings={
'ExecutionRole': 'string',
'SecurityGroups': [
'string',
],
'SharingSettings': {
'NotebookOutputOption': 'Allowed'|'Disabled',
'S3OutputPath': 'string',
'S3KmsKeyId': 'string'
},
'JupyterServerAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'
}
},
'KernelGatewayAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
]
},
'TensorBoardAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'
}
}
}
)
[REQUIRED]
The ID of the domain to be updated.
A collection of settings.
The execution role for the user.
The security groups for the Amazon Virtual Private Cloud (VPC) that Studio uses for communication.
Optional when the CreateDomain.AppNetworkAccessType parameter is set to PublicInternetOnly .
Required when the CreateDomain.AppNetworkAccessType parameter is set to VpcOnly .
Amazon SageMaker adds a security group to allow NFS traffic from SageMaker Studio. Therefore, the number of security groups that you can specify is one less than the maximum number shown.
Specifies options for sharing SageMaker Studio notebooks.
Whether to include the notebook cell output when sharing the notebook. The default is Disabled .
When NotebookOutputOption is Allowed , the Amazon S3 bucket used to store the shared notebook snapshots.
When NotebookOutputOption is Allowed , the Amazon Web Services Key Management Service (KMS) encryption key ID used to encrypt the notebook cell output in the Amazon S3 bucket.
The Jupyter server's app settings.
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app.
The ARN of the SageMaker image that the image version belongs to.
The ARN of the image version created on the instance.
The instance type that the image version runs on.
The kernel gateway app settings.
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.
The ARN of the SageMaker image that the image version belongs to.
The ARN of the image version created on the instance.
The instance type that the image version runs on.
A list of custom SageMaker images that are configured to run as a KernelGateway app.
A custom SageMaker image. For more information, see Bring your own SageMaker image .
The name of the CustomImage. Must be unique to your account.
The version number of the CustomImage.
The name of the AppImageConfig.
The TensorBoard app settings.
The default instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.
The ARN of the SageMaker image that the image version belongs to.
The ARN of the image version created on the instance.
The instance type that the image version runs on.
dict
Response Syntax
{
'DomainArn': 'string'
}
Response Structure
(dict) --
DomainArn (string) --
The Amazon Resource Name (ARN) of the domain.
Exceptions
Deploys the new EndpointConfig specified in the request, switches to using newly created endpoint, and then deletes resources provisioned for the endpoint using the previous EndpointConfig (there is no availability loss).
When Amazon SageMaker receives the request, it sets the endpoint status to Updating . After updating the endpoint, it sets the status to InService . To check the status of an endpoint, use the DescribeEndpoint API.
Note
You must not delete an EndpointConfig in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. To update an endpoint, you must create a new EndpointConfig .
If you delete the EndpointConfig of an endpoint that is active or being created or updated you may lose visibility into the instance type the endpoint is using. The endpoint must be deleted in order to stop incurring charges.
See also: AWS API Documentation
Request Syntax
response = client.update_endpoint(
EndpointName='string',
EndpointConfigName='string',
RetainAllVariantProperties=True|False,
ExcludeRetainedVariantProperties=[
{
'VariantPropertyType': 'DesiredInstanceCount'|'DesiredWeight'|'DataCaptureConfig'
},
],
DeploymentConfig={
'BlueGreenUpdatePolicy': {
'TrafficRoutingConfiguration': {
'Type': 'ALL_AT_ONCE'|'CANARY',
'WaitIntervalInSeconds': 123,
'CanarySize': {
'Type': 'INSTANCE_COUNT'|'CAPACITY_PERCENT',
'Value': 123
}
},
'TerminationWaitInSeconds': 123,
'MaximumExecutionTimeoutInSeconds': 123
},
'AutoRollbackConfiguration': {
'Alarms': [
{
'AlarmName': 'string'
},
]
}
}
)
[REQUIRED]
The name of the endpoint whose configuration you want to update.
[REQUIRED]
The name of the new endpoint configuration.
When you are updating endpoint resources with UpdateEndpointInput$RetainAllVariantProperties , whose value is set to true , ExcludeRetainedVariantProperties specifies the list of type VariantProperty to override with the values provided by EndpointConfig . If you don't specify a value for ExcludeAllVariantProperties , no variant properties are overridden.
Specifies a production variant property type for an Endpoint.
If you are updating an endpoint with the UpdateEndpointInput$RetainAllVariantProperties option set to true , the VariantProperty objects listed in UpdateEndpointInput$ExcludeRetainedVariantProperties override the existing variant properties of the endpoint.
The type of variant property. The supported values are:
The deployment configuration for the endpoint to be updated.
This API is not supported.
This API is not supported.
dict
Response Syntax
{
'EndpointArn': 'string'
}
Response Structure
(dict) --
EndpointArn (string) --
The Amazon Resource Name (ARN) of the endpoint.
Exceptions
Updates variant weight of one or more variants associated with an existing endpoint, or capacity of one variant associated with an existing endpoint. When it receives the request, Amazon SageMaker sets the endpoint status to Updating . After updating the endpoint, it sets the status to InService . To check the status of an endpoint, use the DescribeEndpoint API.
See also: AWS API Documentation
Request Syntax
response = client.update_endpoint_weights_and_capacities(
EndpointName='string',
DesiredWeightsAndCapacities=[
{
'VariantName': 'string',
'DesiredWeight': ...,
'DesiredInstanceCount': 123
},
]
)
[REQUIRED]
The name of an existing Amazon SageMaker endpoint.
[REQUIRED]
An object that provides new capacity and weight values for a variant.
Specifies weight and capacity values for a production variant.
The name of the variant to update.
The variant's weight.
The variant's capacity.
dict
Response Syntax
{
'EndpointArn': 'string'
}
Response Structure
(dict) --
EndpointArn (string) --
The Amazon Resource Name (ARN) of the updated endpoint.
Exceptions
Adds, updates, or removes the description of an experiment. Updates the display name of an experiment.
See also: AWS API Documentation
Request Syntax
response = client.update_experiment(
ExperimentName='string',
DisplayName='string',
Description='string'
)
[REQUIRED]
The name of the experiment to update.
dict
Response Syntax
{
'ExperimentArn': 'string'
}
Response Structure
(dict) --
ExperimentArn (string) --
The Amazon Resource Name (ARN) of the experiment.
Exceptions
Updates the properties of a SageMaker image. To change the image's tags, use the AddTags and DeleteTags APIs.
See also: AWS API Documentation
Request Syntax
response = client.update_image(
DeleteProperties=[
'string',
],
Description='string',
DisplayName='string',
ImageName='string',
RoleArn='string'
)
A list of properties to delete. Only the Description and DisplayName properties can be deleted.
[REQUIRED]
The name of the image to update.
dict
Response Syntax
{
'ImageArn': 'string'
}
Response Structure
(dict) --
ImageArn (string) --
The Amazon Resource Name (ARN) of the image.
Exceptions
Updates a versioned model.
See also: AWS API Documentation
Request Syntax
response = client.update_model_package(
ModelPackageArn='string',
ModelApprovalStatus='Approved'|'Rejected'|'PendingManualApproval',
ApprovalDescription='string'
)
[REQUIRED]
The Amazon Resource Name (ARN) of the model.
[REQUIRED]
The approval status of the model.
dict
Response Syntax
{
'ModelPackageArn': 'string'
}
Response Structure
(dict) --
ModelPackageArn (string) --
The Amazon Resource Name (ARN) of the model.
Updates a previously created schedule.
See also: AWS API Documentation
Request Syntax
response = client.update_monitoring_schedule(
MonitoringScheduleName='string',
MonitoringScheduleConfig={
'ScheduleConfig': {
'ScheduleExpression': 'string'
},
'MonitoringJobDefinition': {
'BaselineConfig': {
'BaseliningJobName': 'string',
'ConstraintsResource': {
'S3Uri': 'string'
},
'StatisticsResource': {
'S3Uri': 'string'
}
},
'MonitoringInputs': [
{
'EndpointInput': {
'EndpointName': 'string',
'LocalPath': 'string',
'S3InputMode': 'Pipe'|'File',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'FeaturesAttribute': 'string',
'InferenceAttribute': 'string',
'ProbabilityAttribute': 'string',
'ProbabilityThresholdAttribute': 123.0,
'StartTimeOffset': 'string',
'EndTimeOffset': 'string'
}
},
],
'MonitoringOutputConfig': {
'MonitoringOutputs': [
{
'S3Output': {
'S3Uri': 'string',
'LocalPath': 'string',
'S3UploadMode': 'Continuous'|'EndOfJob'
}
},
],
'KmsKeyId': 'string'
},
'MonitoringResources': {
'ClusterConfig': {
'InstanceCount': 123,
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
}
},
'MonitoringAppSpecification': {
'ImageUri': 'string',
'ContainerEntrypoint': [
'string',
],
'ContainerArguments': [
'string',
],
'RecordPreprocessorSourceUri': 'string',
'PostAnalyticsProcessorSourceUri': 'string'
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123
},
'Environment': {
'string': 'string'
},
'NetworkConfig': {
'EnableInterContainerTrafficEncryption': True|False,
'EnableNetworkIsolation': True|False,
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
},
'RoleArn': 'string'
},
'MonitoringJobDefinitionName': 'string',
'MonitoringType': 'DataQuality'|'ModelQuality'|'ModelBias'|'ModelExplainability'
}
)
[REQUIRED]
The name of the monitoring schedule. The name must be unique within an Amazon Web Services Region within an Amazon Web Services account.
[REQUIRED]
The configuration object that specifies the monitoring schedule and defines the monitoring job.
Configures the monitoring schedule.
A cron expression that describes details about the monitoring schedule.
Currently the only supported cron expressions are:
For example, the following are valid cron expressions:
To support running every 6, 12 hours, the following are also supported:
cron(0 [00-23]/[01-24] ? * * *)
For example, the following are valid cron expressions:
Note
Defines the monitoring job.
Baseline configuration used to validate that the data conforms to the specified constraints and statistics
The name of the job that performs baselining for the monitoring job.
The baseline constraint file in Amazon S3 that the current monitoring job should validated against.
The Amazon S3 URI for the constraints resource.
The baseline statistics file in Amazon S3 that the current monitoring job should be validated against.
The Amazon S3 URI for the statistics resource.
The array of inputs for the monitoring job. Currently we support monitoring an Amazon SageMaker Endpoint.
The inputs for a monitoring job.
The endpoint for a monitoring job.
An endpoint in customer's account which has enabled DataCaptureConfig enabled.
Path to the filesystem where the endpoint data is available to the container.
Whether the Pipe or File is used as the input mode for transfering data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .
Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated
The attributes of the input data that are the input features.
The attribute of the input data that represents the ground truth label.
In a classification problem, the attribute that represents the class probability.
The threshold for the class probability to be evaluated as a positive result.
If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
The array of outputs from the monitoring job to be uploaded to Amazon Simple Storage Service (Amazon S3).
Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.
The output object for a monitoring job.
The Amazon S3 storage location where the results of a monitoring job are saved.
A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.
The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.
Whether to upload the results of the monitoring job continuously or after the job completes.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.
Identifies the resources, ML compute instances, and ML storage volumes to deploy for a monitoring job. In distributed processing, you specify more than one instance.
The configuration for the cluster resources used to run the processing job.
The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
The ML compute instance type for the processing job.
The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job.
Configures the monitoring job to run a specified Docker container image.
The container image to be run by the monitoring job.
Specifies the entrypoint for a container used to run the monitoring job.
An array of arguments for the container used to run the monitoring job.
An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flatted json so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.
An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.
Specifies a time limit for how long the monitoring job is allowed to run.
The maximum runtime allowed in seconds.
Note
The MaxRuntimeInSeconds cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.
Sets the environment variables in the Docker container.
Specifies networking options for an monitoring job.
Whether to encrypt all communications between distributed processing jobs. Choose True to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer.
Whether to allow inbound and outbound network calls to and from the containers used for the processing job.
Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud .
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
The name of the monitoring job definition to schedule.
The type of the monitoring job definition to schedule.
dict
Response Syntax
{
'MonitoringScheduleArn': 'string'
}
Response Structure
(dict) --
MonitoringScheduleArn (string) --
The Amazon Resource Name (ARN) of the monitoring schedule.
Exceptions
Updates a notebook instance. NotebookInstance updates include upgrading or downgrading the ML compute instance used for your notebook instance to accommodate changes in your workload requirements.
See also: AWS API Documentation
Request Syntax
response = client.update_notebook_instance(
NotebookInstanceName='string',
InstanceType='ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge',
RoleArn='string',
LifecycleConfigName='string',
DisassociateLifecycleConfig=True|False,
VolumeSizeInGB=123,
DefaultCodeRepository='string',
AdditionalCodeRepositories=[
'string',
],
AcceleratorTypes=[
'ml.eia1.medium'|'ml.eia1.large'|'ml.eia1.xlarge'|'ml.eia2.medium'|'ml.eia2.large'|'ml.eia2.xlarge',
],
DisassociateAcceleratorTypes=True|False,
DisassociateDefaultCodeRepository=True|False,
DisassociateAdditionalCodeRepositories=True|False,
RootAccess='Enabled'|'Disabled'
)
[REQUIRED]
The name of the notebook instance to update.
The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker can assume to access the notebook instance. For more information, see Amazon SageMaker Roles .
Note
To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission.
An array of up to three Git repositories to associate with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in Amazon Web Services CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances .
A list of the Elastic Inference (EI) instance types to associate with this notebook instance. Currently only one EI instance type can be associated with a notebook instance. For more information, see Using Elastic Inference in Amazon SageMaker .
Whether root access is enabled or disabled for users of the notebook instance. The default value is Enabled .
Note
If you set this to Disabled , users don't have root access on the notebook instance, but lifecycle configuration scripts still run with root permissions.
dict
Response Syntax
{}
Response Structure
Exceptions
Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API.
See also: AWS API Documentation
Request Syntax
response = client.update_notebook_instance_lifecycle_config(
NotebookInstanceLifecycleConfigName='string',
OnCreate=[
{
'Content': 'string'
},
],
OnStart=[
{
'Content': 'string'
},
]
)
[REQUIRED]
The name of the lifecycle configuration.
The shell script that runs only once, when you create a notebook instance. The shell script must be a base64-encoded string.
Contains the notebook instance lifecycle configuration script.
Each lifecycle configuration script has a limit of 16384 characters.
The value of the $PATH environment variable that is available to both scripts is /sbin:bin:/usr/sbin:/usr/bin .
View CloudWatch Logs for notebook instance lifecycle configurations in log group /aws/sagemaker/NotebookInstances in log stream [notebook-instance-name]/[LifecycleConfigHook] .
Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or started.
For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance .
A base64-encoded string that contains a shell script for a notebook instance lifecycle configuration.
The shell script that runs every time you start a notebook instance, including when you create the notebook instance. The shell script must be a base64-encoded string.
Contains the notebook instance lifecycle configuration script.
Each lifecycle configuration script has a limit of 16384 characters.
The value of the $PATH environment variable that is available to both scripts is /sbin:bin:/usr/sbin:/usr/bin .
View CloudWatch Logs for notebook instance lifecycle configurations in log group /aws/sagemaker/NotebookInstances in log stream [notebook-instance-name]/[LifecycleConfigHook] .
Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or started.
For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance .
A base64-encoded string that contains a shell script for a notebook instance lifecycle configuration.
dict
Response Syntax
{}
Response Structure
Exceptions
Updates a pipeline.
See also: AWS API Documentation
Request Syntax
response = client.update_pipeline(
PipelineName='string',
PipelineDisplayName='string',
PipelineDefinition='string',
PipelineDescription='string',
RoleArn='string'
)
[REQUIRED]
The name of the pipeline to update.
dict
Response Syntax
{
'PipelineArn': 'string'
}
Response Structure
(dict) --
PipelineArn (string) --
The Amazon Resource Name (ARN) of the updated pipeline.
Exceptions
Updates a pipeline execution.
See also: AWS API Documentation
Request Syntax
response = client.update_pipeline_execution(
PipelineExecutionArn='string',
PipelineExecutionDescription='string',
PipelineExecutionDisplayName='string'
)
[REQUIRED]
The Amazon Resource Name (ARN) of the pipeline execution.
dict
Response Syntax
{
'PipelineExecutionArn': 'string'
}
Response Structure
(dict) --
PipelineExecutionArn (string) --
The Amazon Resource Name (ARN) of the updated pipeline execution.
Exceptions
Update a model training job to request a new Debugger profiling configuration.
See also: AWS API Documentation
Request Syntax
response = client.update_training_job(
TrainingJobName='string',
ProfilerConfig={
'S3OutputPath': 'string',
'ProfilingIntervalInMilliseconds': 123,
'ProfilingParameters': {
'string': 'string'
},
'DisableProfiler': True|False
},
ProfilerRuleConfigurations=[
{
'RuleConfigurationName': 'string',
'LocalPath': 'string',
'S3OutputPath': 'string',
'RuleEvaluatorImage': 'string',
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'RuleParameters': {
'string': 'string'
}
},
]
)
[REQUIRED]
The name of a training job to update the Debugger profiling configuration.
Configuration information for Debugger system monitoring, framework profiling, and storage paths.
Path to Amazon S3 storage location for system and framework metrics.
A time interval for capturing system metrics in milliseconds. Available values are 100, 200, 500, 1000 (1 second), 5000 (5 seconds), and 60000 (1 minute) milliseconds. The default value is 500 milliseconds.
Configuration information for capturing framework metrics. Available key strings for different profiling options are DetailedProfilingConfig , PythonProfilingConfig , and DataLoaderProfilingConfig . The following codes are configuration structures for the ProfilingParameters parameter. To learn more about how to configure the ProfilingParameters parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
To disable Debugger monitoring and profiling, set to True .
Configuration information for Debugger rules for profiling system and framework metrics.
Configuration information for profiling rules.
The name of the rule configuration. It must be unique relative to other rule configuration names.
Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/ .
Path to Amazon S3 storage location for rules.
The Amazon Elastic Container (ECR) Image for the managed rule evaluation.
The instance type to deploy a Debugger custom rule for profiling a training job.
The size, in GB, of the ML storage volume attached to the processing instance.
Runtime configuration for rule container.
dict
Response Syntax
{
'TrainingJobArn': 'string'
}
Response Structure
(dict) --
TrainingJobArn (string) --
The Amazon Resource Name (ARN) of the training job.
Exceptions
Updates the display name of a trial.
See also: AWS API Documentation
Request Syntax
response = client.update_trial(
TrialName='string',
DisplayName='string'
)
[REQUIRED]
The name of the trial to update.
dict
Response Syntax
{
'TrialArn': 'string'
}
Response Structure
(dict) --
TrialArn (string) --
The Amazon Resource Name (ARN) of the trial.
Exceptions
Updates one or more properties of a trial component.
See also: AWS API Documentation
Request Syntax
response = client.update_trial_component(
TrialComponentName='string',
DisplayName='string',
Status={
'PrimaryStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'Message': 'string'
},
StartTime=datetime(2015, 1, 1),
EndTime=datetime(2015, 1, 1),
Parameters={
'string': {
'StringValue': 'string',
'NumberValue': 123.0
}
},
ParametersToRemove=[
'string',
],
InputArtifacts={
'string': {
'MediaType': 'string',
'Value': 'string'
}
},
InputArtifactsToRemove=[
'string',
],
OutputArtifacts={
'string': {
'MediaType': 'string',
'Value': 'string'
}
},
OutputArtifactsToRemove=[
'string',
]
)
[REQUIRED]
The name of the component to update.
The new status of the component.
The status of the trial component.
If the component failed, a message describing why.
Replaces all of the component's hyperparameters with the specified hyperparameters.
The value of a hyperparameter. Only one of NumberValue or StringValue can be specified.
This object is specified in the CreateTrialComponent request.
The string value of a categorical hyperparameter. If you specify a value for this parameter, you can't specify the NumberValue parameter.
The numeric value of a numeric hyperparameter. If you specify a value for this parameter, you can't specify the StringValue parameter.
The hyperparameters to remove from the component.
Replaces all of the component's input artifacts with the specified artifacts.
Represents an input or output artifact of a trial component. You specify TrialComponentArtifact as part of the InputArtifacts and OutputArtifacts parameters in the CreateTrialComponent request.
Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and instance types. Examples of output artifacts are metrics, snapshots, logs, and images.
The media type of the artifact, which indicates the type of data in the artifact file. The media type consists of a type and a subtype concatenated with a slash (/) character, for example, text/csv, image/jpeg, and s3/uri. The type specifies the category of the media. The subtype specifies the kind of data.
The location of the artifact.
The input artifacts to remove from the component.
Replaces all of the component's output artifacts with the specified artifacts.
Represents an input or output artifact of a trial component. You specify TrialComponentArtifact as part of the InputArtifacts and OutputArtifacts parameters in the CreateTrialComponent request.
Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and instance types. Examples of output artifacts are metrics, snapshots, logs, and images.
The media type of the artifact, which indicates the type of data in the artifact file. The media type consists of a type and a subtype concatenated with a slash (/) character, for example, text/csv, image/jpeg, and s3/uri. The type specifies the category of the media. The subtype specifies the kind of data.
The location of the artifact.
The output artifacts to remove from the component.
dict
Response Syntax
{
'TrialComponentArn': 'string'
}
Response Structure
(dict) --
TrialComponentArn (string) --
The Amazon Resource Name (ARN) of the trial component.
Exceptions
Updates a user profile.
See also: AWS API Documentation
Request Syntax
response = client.update_user_profile(
DomainId='string',
UserProfileName='string',
UserSettings={
'ExecutionRole': 'string',
'SecurityGroups': [
'string',
],
'SharingSettings': {
'NotebookOutputOption': 'Allowed'|'Disabled',
'S3OutputPath': 'string',
'S3KmsKeyId': 'string'
},
'JupyterServerAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'
}
},
'KernelGatewayAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
]
},
'TensorBoardAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'
}
}
}
)
[REQUIRED]
The domain ID.
[REQUIRED]
The user profile name.
A collection of settings.
The execution role for the user.
The security groups for the Amazon Virtual Private Cloud (VPC) that Studio uses for communication.
Optional when the CreateDomain.AppNetworkAccessType parameter is set to PublicInternetOnly .
Required when the CreateDomain.AppNetworkAccessType parameter is set to VpcOnly .
Amazon SageMaker adds a security group to allow NFS traffic from SageMaker Studio. Therefore, the number of security groups that you can specify is one less than the maximum number shown.
Specifies options for sharing SageMaker Studio notebooks.
Whether to include the notebook cell output when sharing the notebook. The default is Disabled .
When NotebookOutputOption is Allowed , the Amazon S3 bucket used to store the shared notebook snapshots.
When NotebookOutputOption is Allowed , the Amazon Web Services Key Management Service (KMS) encryption key ID used to encrypt the notebook cell output in the Amazon S3 bucket.
The Jupyter server's app settings.
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app.
The ARN of the SageMaker image that the image version belongs to.
The ARN of the image version created on the instance.
The instance type that the image version runs on.
The kernel gateway app settings.
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.
The ARN of the SageMaker image that the image version belongs to.
The ARN of the image version created on the instance.
The instance type that the image version runs on.
A list of custom SageMaker images that are configured to run as a KernelGateway app.
A custom SageMaker image. For more information, see Bring your own SageMaker image .
The name of the CustomImage. Must be unique to your account.
The version number of the CustomImage.
The name of the AppImageConfig.
The TensorBoard app settings.
The default instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.
The ARN of the SageMaker image that the image version belongs to.
The ARN of the image version created on the instance.
The instance type that the image version runs on.
dict
Response Syntax
{
'UserProfileArn': 'string'
}
Response Structure
(dict) --
UserProfileArn (string) --
The user profile Amazon Resource Name (ARN).
Exceptions
Use this operation to update your workforce. You can use this operation to require that workers use specific IP addresses to work on tasks and to update your OpenID Connect (OIDC) Identity Provider (IdP) workforce configuration.
Use SourceIpConfig to restrict worker access to tasks to a specific range of IP addresses. You specify allowed IP addresses by creating a list of up to ten CIDRs . By default, a workforce isn't restricted to specific IP addresses. If you specify a range of IP addresses, workers who attempt to access tasks using any IP address outside the specified range are denied and get a Not Found error message on the worker portal.
Use OidcConfig to update the configuration of a workforce created using your own OIDC IdP.
Warning
You can only update your OIDC IdP configuration when there are no work teams associated with your workforce. You can delete work teams using the operation.
After restricting access to a range of IP addresses or updating your OIDC IdP configuration with this operation, you can view details about your update workforce using the operation.
Warning
This operation only applies to private workforces.
See also: AWS API Documentation
Request Syntax
response = client.update_workforce(
WorkforceName='string',
SourceIpConfig={
'Cidrs': [
'string',
]
},
OidcConfig={
'ClientId': 'string',
'ClientSecret': 'string',
'Issuer': 'string',
'AuthorizationEndpoint': 'string',
'TokenEndpoint': 'string',
'UserInfoEndpoint': 'string',
'LogoutEndpoint': 'string',
'JwksUri': 'string'
}
)
[REQUIRED]
The name of the private workforce that you want to update. You can find your workforce name by using the operation.
A list of one to ten worker IP address ranges (CIDRs ) that can be used to access tasks assigned to this workforce.
Maximum: Ten CIDR values
A list of one to ten Classless Inter-Domain Routing (CIDR) values.
Maximum: Ten CIDR values
Note
The following Length Constraints apply to individual CIDR values in the CIDR value list.
Use this parameter to update your OIDC Identity Provider (IdP) configuration for a workforce made using your own IdP.
The OIDC IdP client ID used to configure your private workforce.
The OIDC IdP client secret used to configure your private workforce.
The OIDC IdP issuer used to configure your private workforce.
The OIDC IdP authorization endpoint used to configure your private workforce.
The OIDC IdP token endpoint used to configure your private workforce.
The OIDC IdP user information endpoint used to configure your private workforce.
The OIDC IdP logout endpoint used to configure your private workforce.
The OIDC IdP JSON Web Key Set (Jwks) URI used to configure your private workforce.
dict
Response Syntax
{
'Workforce': {
'WorkforceName': 'string',
'WorkforceArn': 'string',
'LastUpdatedDate': datetime(2015, 1, 1),
'SourceIpConfig': {
'Cidrs': [
'string',
]
},
'SubDomain': 'string',
'CognitoConfig': {
'UserPool': 'string',
'ClientId': 'string'
},
'OidcConfig': {
'ClientId': 'string',
'Issuer': 'string',
'AuthorizationEndpoint': 'string',
'TokenEndpoint': 'string',
'UserInfoEndpoint': 'string',
'LogoutEndpoint': 'string',
'JwksUri': 'string'
},
'CreateDate': datetime(2015, 1, 1)
}
}
Response Structure
(dict) --
Workforce (dict) --
A single private workforce. You can create one private work force in each Amazon Web Services Region. By default, any workforce-related API operation used in a specific region will apply to the workforce created in that region. To learn how to create a private workforce, see Create a Private Workforce .
WorkforceName (string) --
The name of the private workforce.
WorkforceArn (string) --
The Amazon Resource Name (ARN) of the private workforce.
LastUpdatedDate (datetime) --
The most recent date that was used to successfully add one or more IP address ranges (CIDRs ) to a private workforce's allow list.
SourceIpConfig (dict) --
A list of one to ten IP address ranges (CIDRs ) to be added to the workforce allow list. By default, a workforce isn't restricted to specific IP addresses.
Cidrs (list) --
A list of one to ten Classless Inter-Domain Routing (CIDR) values.
Maximum: Ten CIDR values
Note
The following Length Constraints apply to individual CIDR values in the CIDR value list.
SubDomain (string) --
The subdomain for your OIDC Identity Provider.
CognitoConfig (dict) --
The configuration of an Amazon Cognito workforce. A single Cognito workforce is created using and corresponds to a single Amazon Cognito user pool .
UserPool (string) --
A user pool is a user directory in Amazon Cognito. With a user pool, your users can sign in to your web or mobile app through Amazon Cognito. Your users can also sign in through social identity providers like Google, Facebook, Amazon, or Apple, and through SAML identity providers.
ClientId (string) --
The client ID for your Amazon Cognito user pool.
OidcConfig (dict) --
The configuration of an OIDC Identity Provider (IdP) private workforce.
ClientId (string) --
The OIDC IdP client ID used to configure your private workforce.
Issuer (string) --
The OIDC IdP issuer used to configure your private workforce.
AuthorizationEndpoint (string) --
The OIDC IdP authorization endpoint used to configure your private workforce.
TokenEndpoint (string) --
The OIDC IdP token endpoint used to configure your private workforce.
UserInfoEndpoint (string) --
The OIDC IdP user information endpoint used to configure your private workforce.
LogoutEndpoint (string) --
The OIDC IdP logout endpoint used to configure your private workforce.
JwksUri (string) --
The OIDC IdP JSON Web Key Set (Jwks) URI used to configure your private workforce.
CreateDate (datetime) --
The date that the workforce is created.
Updates an existing work team with new member definitions or description.
See also: AWS API Documentation
Request Syntax
response = client.update_workteam(
WorkteamName='string',
MemberDefinitions=[
{
'CognitoMemberDefinition': {
'UserPool': 'string',
'UserGroup': 'string',
'ClientId': 'string'
},
'OidcMemberDefinition': {
'Groups': [
'string',
]
}
},
],
Description='string',
NotificationConfiguration={
'NotificationTopicArn': 'string'
}
)
[REQUIRED]
The name of the work team to update.
A list of MemberDefinition objects that contains objects that identify the workers that make up the work team.
Workforces can be created using Amazon Cognito or your own OIDC Identity Provider (IdP). For private workforces created using Amazon Cognito use CognitoMemberDefinition . For workforces created using your own OIDC identity provider (IdP) use OidcMemberDefinition . You should not provide input for both of these parameters in a single request.
For workforces created using Amazon Cognito, private work teams correspond to Amazon Cognito user groups within the user pool used to create a workforce. All of the CognitoMemberDefinition objects that make up the member definition must have the same ClientId and UserPool values. To add a Amazon Cognito user group to an existing worker pool, see Adding groups to a User Pool . For more information about user pools, see `Amazon Cognito User Pools .
For workforces created using your own OIDC IdP, specify the user groups that you want to include in your private work team in OidcMemberDefinition by listing those groups in Groups . Be aware that user groups that are already in the work team must also be listed in Groups when you make this request to remain on the work team. If you do not include these user groups, they will no longer be associated with the work team you update.
Defines an Amazon Cognito or your own OIDC IdP user group that is part of a work team.
The Amazon Cognito user group that is part of the work team.
An identifier for a user pool. The user pool must be in the same region as the service that you are calling.
An identifier for a user group.
An identifier for an application client. You must create the app client ID using Amazon Cognito.
A list user groups that exist in your OIDC Identity Provider (IdP). One to ten groups can be used to create a single private work team. When you add a user group to the list of Groups , you can add that user group to one or more private work teams. If you add a user group to a private work team, all workers in that user group are added to the work team.
A list of comma seperated strings that identifies user groups in your OIDC IdP. Each user group is made up of a group of private workers.
Configures SNS topic notifications for available or expiring work items
The ARN for the SNS topic to which notifications should be published.
dict
Response Syntax
{
'Workteam': {
'WorkteamName': 'string',
'MemberDefinitions': [
{
'CognitoMemberDefinition': {
'UserPool': 'string',
'UserGroup': 'string',
'ClientId': 'string'
},
'OidcMemberDefinition': {
'Groups': [
'string',
]
}
},
],
'WorkteamArn': 'string',
'WorkforceArn': 'string',
'ProductListingIds': [
'string',
],
'Description': 'string',
'SubDomain': 'string',
'CreateDate': datetime(2015, 1, 1),
'LastUpdatedDate': datetime(2015, 1, 1),
'NotificationConfiguration': {
'NotificationTopicArn': 'string'
}
}
}
Response Structure
(dict) --
Workteam (dict) --
A Workteam object that describes the updated work team.
WorkteamName (string) --
The name of the work team.
MemberDefinitions (list) --
A list of MemberDefinition objects that contains objects that identify the workers that make up the work team.
Workforces can be created using Amazon Cognito or your own OIDC Identity Provider (IdP). For private workforces created using Amazon Cognito use CognitoMemberDefinition . For workforces created using your own OIDC identity provider (IdP) use OidcMemberDefinition .
(dict) --
Defines an Amazon Cognito or your own OIDC IdP user group that is part of a work team.
CognitoMemberDefinition (dict) --
The Amazon Cognito user group that is part of the work team.
UserPool (string) --
An identifier for a user pool. The user pool must be in the same region as the service that you are calling.
UserGroup (string) --
An identifier for a user group.
ClientId (string) --
An identifier for an application client. You must create the app client ID using Amazon Cognito.
OidcMemberDefinition (dict) --
A list user groups that exist in your OIDC Identity Provider (IdP). One to ten groups can be used to create a single private work team. When you add a user group to the list of Groups , you can add that user group to one or more private work teams. If you add a user group to a private work team, all workers in that user group are added to the work team.
Groups (list) --
A list of comma seperated strings that identifies user groups in your OIDC IdP. Each user group is made up of a group of private workers.
WorkteamArn (string) --
The Amazon Resource Name (ARN) that identifies the work team.
WorkforceArn (string) --
The Amazon Resource Name (ARN) of the workforce.
ProductListingIds (list) --
The Amazon Marketplace identifier for a vendor's work team.
Description (string) --
A description of the work team.
SubDomain (string) --
The URI of the labeling job's user interface. Workers open this URI to start labeling your data objects.
CreateDate (datetime) --
The date and time that the work team was created (timestamp).
LastUpdatedDate (datetime) --
The date and time that the work team was last updated (timestamp).
NotificationConfiguration (dict) --
Configures SNS notifications of available or expiring work items for work teams.
NotificationTopicArn (string) --
The ARN for the SNS topic to which notifications should be published.
Exceptions
The available paginators are:
paginator = client.get_paginator('list_actions')
Creates an iterator that will paginate through responses from SageMaker.Client.list_actions().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
SourceUri='string',
ActionType='string',
CreatedAfter=datetime(2015, 1, 1),
CreatedBefore=datetime(2015, 1, 1),
SortBy='Name'|'CreationTime',
SortOrder='Ascending'|'Descending',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'ActionSummaries': [
{
'ActionArn': 'string',
'ActionName': 'string',
'Source': {
'SourceUri': 'string',
'SourceType': 'string',
'SourceId': 'string'
},
'ActionType': 'string',
'Status': 'Unknown'|'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1)
},
],
}
Response Structure
(dict) --
ActionSummaries (list) --
A list of actions and their properties.
(dict) --
Lists the properties of an action . An action represents an action or activity. Some examples are a workflow step and a model deployment. Generally, an action involves at least one input artifact or output artifact.
ActionArn (string) --
The Amazon Resource Name (ARN) of the action.
ActionName (string) --
The name of the action.
Source (dict) --
The source of the action.
SourceUri (string) --
The URI of the source.
SourceType (string) --
The type of the source.
SourceId (string) --
The ID of the source.
ActionType (string) --
The type of the action.
Status (string) --
The status of the action.
CreationTime (datetime) --
When the action was created.
LastModifiedTime (datetime) --
When the action was last modified.
paginator = client.get_paginator('list_algorithms')
Creates an iterator that will paginate through responses from SageMaker.Client.list_algorithms().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
NameContains='string',
SortBy='Name'|'CreationTime',
SortOrder='Ascending'|'Descending',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'AlgorithmSummaryList': [
{
'AlgorithmName': 'string',
'AlgorithmArn': 'string',
'AlgorithmDescription': 'string',
'CreationTime': datetime(2015, 1, 1),
'AlgorithmStatus': 'Pending'|'InProgress'|'Completed'|'Failed'|'Deleting'
},
],
}
Response Structure
(dict) --
AlgorithmSummaryList (list) --
>An array of AlgorithmSummary objects, each of which lists an algorithm.
(dict) --
Provides summary information about an algorithm.
AlgorithmName (string) --
The name of the algorithm that is described by the summary.
AlgorithmArn (string) --
The Amazon Resource Name (ARN) of the algorithm.
AlgorithmDescription (string) --
A brief description of the algorithm.
CreationTime (datetime) --
A timestamp that shows when the algorithm was created.
AlgorithmStatus (string) --
The overall status of the algorithm.
paginator = client.get_paginator('list_app_image_configs')
Creates an iterator that will paginate through responses from SageMaker.Client.list_app_image_configs().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
NameContains='string',
CreationTimeBefore=datetime(2015, 1, 1),
CreationTimeAfter=datetime(2015, 1, 1),
ModifiedTimeBefore=datetime(2015, 1, 1),
ModifiedTimeAfter=datetime(2015, 1, 1),
SortBy='CreationTime'|'LastModifiedTime'|'Name',
SortOrder='Ascending'|'Descending',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'AppImageConfigs': [
{
'AppImageConfigArn': 'string',
'AppImageConfigName': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'KernelGatewayImageConfig': {
'KernelSpecs': [
{
'Name': 'string',
'DisplayName': 'string'
},
],
'FileSystemConfig': {
'MountPath': 'string',
'DefaultUid': 123,
'DefaultGid': 123
}
}
},
]
}
Response Structure
(dict) --
AppImageConfigs (list) --
A list of AppImageConfigs and their properties.
(dict) --
The configuration for running a SageMaker image as a KernelGateway app.
AppImageConfigArn (string) --
The Amazon Resource Name (ARN) of the AppImageConfig.
AppImageConfigName (string) --
The name of the AppImageConfig. Must be unique to your account.
CreationTime (datetime) --
When the AppImageConfig was created.
LastModifiedTime (datetime) --
When the AppImageConfig was last modified.
KernelGatewayImageConfig (dict) --
The configuration for the file system and kernels in the SageMaker image.
KernelSpecs (list) --
The specification of the Jupyter kernels in the image.
(dict) --
The specification of a Jupyter kernel.
Name (string) --
The name of the Jupyter kernel in the image. This value is case sensitive.
DisplayName (string) --
The display name of the kernel.
FileSystemConfig (dict) --
The Amazon Elastic File System (EFS) storage configuration for a SageMaker image.
MountPath (string) --
The path within the image to mount the user's EFS home directory. The directory should be empty. If not specified, defaults to /home/sagemaker-user .
DefaultUid (integer) --
The default POSIX user ID (UID). If not specified, defaults to 1000 .
DefaultGid (integer) --
The default POSIX group ID (GID). If not specified, defaults to 100 .
paginator = client.get_paginator('list_apps')
Creates an iterator that will paginate through responses from SageMaker.Client.list_apps().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
SortOrder='Ascending'|'Descending',
SortBy='CreationTime',
DomainIdEquals='string',
UserProfileNameEquals='string',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'Apps': [
{
'DomainId': 'string',
'UserProfileName': 'string',
'AppType': 'JupyterServer'|'KernelGateway'|'TensorBoard',
'AppName': 'string',
'Status': 'Deleted'|'Deleting'|'Failed'|'InService'|'Pending',
'CreationTime': datetime(2015, 1, 1)
},
],
}
Response Structure
(dict) --
Apps (list) --
The list of apps.
(dict) --
Details about an Amazon SageMaker app.
DomainId (string) --
The domain ID.
UserProfileName (string) --
The user profile name.
AppType (string) --
The type of app.
AppName (string) --
The name of the app.
Status (string) --
The status.
CreationTime (datetime) --
The creation time.
paginator = client.get_paginator('list_artifacts')
Creates an iterator that will paginate through responses from SageMaker.Client.list_artifacts().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
SourceUri='string',
ArtifactType='string',
CreatedAfter=datetime(2015, 1, 1),
CreatedBefore=datetime(2015, 1, 1),
SortBy='CreationTime',
SortOrder='Ascending'|'Descending',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'ArtifactSummaries': [
{
'ArtifactArn': 'string',
'ArtifactName': 'string',
'Source': {
'SourceUri': 'string',
'SourceTypes': [
{
'SourceIdType': 'MD5Hash'|'S3ETag'|'S3Version'|'Custom',
'Value': 'string'
},
]
},
'ArtifactType': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1)
},
],
}
Response Structure
(dict) --
ArtifactSummaries (list) --
A list of artifacts and their properties.
(dict) --
Lists a summary of the properties of an artifact. An artifact represents a URI addressable object or data. Some examples are a dataset and a model.
ArtifactArn (string) --
The Amazon Resource Name (ARN) of the artifact.
ArtifactName (string) --
The name of the artifact.
Source (dict) --
The source of the artifact.
SourceUri (string) --
The URI of the source.
SourceTypes (list) --
A list of source types.
(dict) --
The ID and ID type of an artifact source.
SourceIdType (string) --
The type of ID.
Value (string) --
The ID.
ArtifactType (string) --
The type of the artifact.
CreationTime (datetime) --
When the artifact was created.
LastModifiedTime (datetime) --
When the artifact was last modified.
paginator = client.get_paginator('list_associations')
Creates an iterator that will paginate through responses from SageMaker.Client.list_associations().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
SourceArn='string',
DestinationArn='string',
SourceType='string',
DestinationType='string',
AssociationType='ContributedTo'|'AssociatedWith'|'DerivedFrom'|'Produced',
CreatedAfter=datetime(2015, 1, 1),
CreatedBefore=datetime(2015, 1, 1),
SortBy='SourceArn'|'DestinationArn'|'SourceType'|'DestinationType'|'CreationTime',
SortOrder='Ascending'|'Descending',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'AssociationSummaries': [
{
'SourceArn': 'string',
'DestinationArn': 'string',
'SourceType': 'string',
'DestinationType': 'string',
'AssociationType': 'ContributedTo'|'AssociatedWith'|'DerivedFrom'|'Produced',
'SourceName': 'string',
'DestinationName': 'string',
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
}
},
],
}
Response Structure
(dict) --
AssociationSummaries (list) --
A list of associations and their properties.
(dict) --
Lists a summary of the properties of an association. An association is an entity that links other lineage or experiment entities. An example would be an association between a training job and a model.
SourceArn (string) --
The ARN of the source.
DestinationArn (string) --
The Amazon Resource Name (ARN) of the destination.
SourceType (string) --
The source type.
DestinationType (string) --
The destination type.
AssociationType (string) --
The type of the association.
SourceName (string) --
The name of the source.
DestinationName (string) --
The name of the destination.
CreationTime (datetime) --
When the association was created.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
paginator = client.get_paginator('list_auto_ml_jobs')
Creates an iterator that will paginate through responses from SageMaker.Client.list_auto_ml_jobs().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
LastModifiedTimeAfter=datetime(2015, 1, 1),
LastModifiedTimeBefore=datetime(2015, 1, 1),
NameContains='string',
StatusEquals='Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping',
SortOrder='Ascending'|'Descending',
SortBy='Name'|'CreationTime'|'Status',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'AutoMLJobSummaries': [
{
'AutoMLJobName': 'string',
'AutoMLJobArn': 'string',
'AutoMLJobStatus': 'Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping',
'AutoMLJobSecondaryStatus': 'Starting'|'AnalyzingData'|'FeatureEngineering'|'ModelTuning'|'MaxCandidatesReached'|'Failed'|'Stopped'|'MaxAutoMLJobRuntimeReached'|'Stopping'|'CandidateDefinitionsGenerated'|'GeneratingExplainabilityReport'|'Completed'|'ExplainabilityError'|'DeployingModel'|'ModelDeploymentError',
'CreationTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'FailureReason': 'string',
'PartialFailureReasons': [
{
'PartialFailureMessage': 'string'
},
]
},
],
}
Response Structure
(dict) --
AutoMLJobSummaries (list) --
Returns a summary list of jobs.
(dict) --
Provides a summary about an AutoML job.
AutoMLJobName (string) --
The name of the AutoML you are requesting.
AutoMLJobArn (string) --
The ARN of the AutoML job.
AutoMLJobStatus (string) --
The status of the AutoML job.
AutoMLJobSecondaryStatus (string) --
The secondary status of the AutoML job.
CreationTime (datetime) --
When the AutoML job was created.
EndTime (datetime) --
The end time of an AutoML job.
LastModifiedTime (datetime) --
When the AutoML job was last modified.
FailureReason (string) --
The failure reason of an AutoML job.
PartialFailureReasons (list) --
The list of reasons for partial failures within an AutoML job.
(dict) --
The reason for a partial failure of an AutoML job.
PartialFailureMessage (string) --
The message containing the reason for a partial failure of an AutoML job.
paginator = client.get_paginator('list_candidates_for_auto_ml_job')
Creates an iterator that will paginate through responses from SageMaker.Client.list_candidates_for_auto_ml_job().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
AutoMLJobName='string',
StatusEquals='Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping',
CandidateNameEquals='string',
SortOrder='Ascending'|'Descending',
SortBy='CreationTime'|'Status'|'FinalObjectiveMetricValue',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
[REQUIRED]
List the candidates created for the job by providing the job's name.
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'Candidates': [
{
'CandidateName': 'string',
'FinalAutoMLJobObjectiveMetric': {
'Type': 'Maximize'|'Minimize',
'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC',
'Value': ...
},
'ObjectiveStatus': 'Succeeded'|'Pending'|'Failed',
'CandidateSteps': [
{
'CandidateStepType': 'AWS::SageMaker::TrainingJob'|'AWS::SageMaker::TransformJob'|'AWS::SageMaker::ProcessingJob',
'CandidateStepArn': 'string',
'CandidateStepName': 'string'
},
],
'CandidateStatus': 'Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping',
'InferenceContainers': [
{
'Image': 'string',
'ModelDataUrl': 'string',
'Environment': {
'string': 'string'
}
},
],
'CreationTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'FailureReason': 'string',
'CandidateProperties': {
'CandidateArtifactLocations': {
'Explainability': 'string'
}
}
},
],
}
Response Structure
(dict) --
Candidates (list) --
Summaries about the AutoMLCandidates .
(dict) --
An Autopilot job returns recommendations, or candidates. Each candidate has futher details about the steps involved and the status.
CandidateName (string) --
The name of the candidate.
FinalAutoMLJobObjectiveMetric (dict) --
The best candidate result from an AutoML training job.
Type (string) --
The type of metric with the best result.
MetricName (string) --
The name of the metric with the best result. For a description of the possible objective metrics, see AutoMLJobObjective$MetricName .
Value (float) --
The value of the metric with the best result.
ObjectiveStatus (string) --
The objective's status.
CandidateSteps (list) --
Information about the candidate's steps.
(dict) --
Information about the steps for a candidate and what step it is working on.
CandidateStepType (string) --
Whether the candidate is at the transform, training, or processing step.
CandidateStepArn (string) --
The ARN for the candidate's step.
CandidateStepName (string) --
The name for the candidate's step.
CandidateStatus (string) --
The candidate's status.
InferenceContainers (list) --
Information about the inference container definitions.
(dict) --
A list of container definitions that describe the different containers that make up an AutoML candidate. For more information, see .
Image (string) --
The ECR path of the container. For more information, see .
ModelDataUrl (string) --
The location of the model artifacts. For more information, see .
Environment (dict) --
The environment variables to set in the container. For more information, see .
CreationTime (datetime) --
The creation time.
EndTime (datetime) --
The end time.
LastModifiedTime (datetime) --
The last modified time.
FailureReason (string) --
The failure reason.
CandidateProperties (dict) --
The AutoML candidate's properties.
CandidateArtifactLocations (dict) --
The Amazon S3 prefix to the artifacts generated for an AutoML candidate.
Explainability (string) --
The Amazon S3 prefix to the explainability artifacts generated for the AutoML candidate.
paginator = client.get_paginator('list_code_repositories')
Creates an iterator that will paginate through responses from SageMaker.Client.list_code_repositories().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
LastModifiedTimeAfter=datetime(2015, 1, 1),
LastModifiedTimeBefore=datetime(2015, 1, 1),
NameContains='string',
SortBy='Name'|'CreationTime'|'LastModifiedTime',
SortOrder='Ascending'|'Descending',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'CodeRepositorySummaryList': [
{
'CodeRepositoryName': 'string',
'CodeRepositoryArn': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'GitConfig': {
'RepositoryUrl': 'string',
'Branch': 'string',
'SecretArn': 'string'
}
},
],
}
Response Structure
(dict) --
CodeRepositorySummaryList (list) --
Gets a list of summaries of the Git repositories. Each summary specifies the following values for the repository:
(dict) --
Specifies summary information about a Git repository.
CodeRepositoryName (string) --
The name of the Git repository.
CodeRepositoryArn (string) --
The Amazon Resource Name (ARN) of the Git repository.
CreationTime (datetime) --
The date and time that the Git repository was created.
LastModifiedTime (datetime) --
The date and time that the Git repository was last modified.
GitConfig (dict) --
Configuration details for the Git repository, including the URL where it is located and the ARN of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the repository.
RepositoryUrl (string) --
The URL where the Git repository is located.
Branch (string) --
The default branch for the Git repository.
SecretArn (string) --
The Amazon Resource Name (ARN) of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the git repository. The secret must have a staging label of AWSCURRENT and must be in the following format:
{"username": *UserName* , "password": *Password* }
paginator = client.get_paginator('list_compilation_jobs')
Creates an iterator that will paginate through responses from SageMaker.Client.list_compilation_jobs().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
LastModifiedTimeAfter=datetime(2015, 1, 1),
LastModifiedTimeBefore=datetime(2015, 1, 1),
NameContains='string',
StatusEquals='INPROGRESS'|'COMPLETED'|'FAILED'|'STARTING'|'STOPPING'|'STOPPED',
SortBy='Name'|'CreationTime'|'Status',
SortOrder='Ascending'|'Descending',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'CompilationJobSummaries': [
{
'CompilationJobName': 'string',
'CompilationJobArn': 'string',
'CreationTime': datetime(2015, 1, 1),
'CompilationStartTime': datetime(2015, 1, 1),
'CompilationEndTime': datetime(2015, 1, 1),
'CompilationTargetDevice': 'lambda'|'ml_m4'|'ml_m5'|'ml_c4'|'ml_c5'|'ml_p2'|'ml_p3'|'ml_g4dn'|'ml_inf1'|'ml_eia2'|'jetson_tx1'|'jetson_tx2'|'jetson_nano'|'jetson_xavier'|'rasp3b'|'imx8qm'|'deeplens'|'rk3399'|'rk3288'|'aisage'|'sbe_c'|'qcs605'|'qcs603'|'sitara_am57x'|'amba_cv22'|'amba_cv25'|'x86_win32'|'x86_win64'|'coreml'|'jacinto_tda4vm',
'CompilationTargetPlatformOs': 'ANDROID'|'LINUX',
'CompilationTargetPlatformArch': 'X86_64'|'X86'|'ARM64'|'ARM_EABI'|'ARM_EABIHF',
'CompilationTargetPlatformAccelerator': 'INTEL_GRAPHICS'|'MALI'|'NVIDIA',
'LastModifiedTime': datetime(2015, 1, 1),
'CompilationJobStatus': 'INPROGRESS'|'COMPLETED'|'FAILED'|'STARTING'|'STOPPING'|'STOPPED'
},
],
}
Response Structure
(dict) --
CompilationJobSummaries (list) --
An array of CompilationJobSummary objects, each describing a model compilation job.
(dict) --
A summary of a model compilation job.
CompilationJobName (string) --
The name of the model compilation job that you want a summary for.
CompilationJobArn (string) --
The Amazon Resource Name (ARN) of the model compilation job.
CreationTime (datetime) --
The time when the model compilation job was created.
CompilationStartTime (datetime) --
The time when the model compilation job started.
CompilationEndTime (datetime) --
The time when the model compilation job completed.
CompilationTargetDevice (string) --
The type of device that the model will run on after the compilation job has completed.
CompilationTargetPlatformOs (string) --
The type of OS that the model will run on after the compilation job has completed.
CompilationTargetPlatformArch (string) --
The type of architecture that the model will run on after the compilation job has completed.
CompilationTargetPlatformAccelerator (string) --
The type of accelerator that the model will run on after the compilation job has completed.
LastModifiedTime (datetime) --
The time when the model compilation job was last modified.
CompilationJobStatus (string) --
The status of the model compilation job.
paginator = client.get_paginator('list_contexts')
Creates an iterator that will paginate through responses from SageMaker.Client.list_contexts().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
SourceUri='string',
ContextType='string',
CreatedAfter=datetime(2015, 1, 1),
CreatedBefore=datetime(2015, 1, 1),
SortBy='Name'|'CreationTime',
SortOrder='Ascending'|'Descending',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'ContextSummaries': [
{
'ContextArn': 'string',
'ContextName': 'string',
'Source': {
'SourceUri': 'string',
'SourceType': 'string',
'SourceId': 'string'
},
'ContextType': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1)
},
],
}
Response Structure
(dict) --
ContextSummaries (list) --
A list of contexts and their properties.
(dict) --
Lists a summary of the properties of a context. A context provides a logical grouping of other entities.
ContextArn (string) --
The Amazon Resource Name (ARN) of the context.
ContextName (string) --
The name of the context.
Source (dict) --
The source of the context.
SourceUri (string) --
The URI of the source.
SourceType (string) --
The type of the source.
SourceId (string) --
The ID of the source.
ContextType (string) --
The type of the context.
CreationTime (datetime) --
When the context was created.
LastModifiedTime (datetime) --
When the context was last modified.
paginator = client.get_paginator('list_data_quality_job_definitions')
Creates an iterator that will paginate through responses from SageMaker.Client.list_data_quality_job_definitions().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
EndpointName='string',
SortBy='Name'|'CreationTime',
SortOrder='Ascending'|'Descending',
NameContains='string',
CreationTimeBefore=datetime(2015, 1, 1),
CreationTimeAfter=datetime(2015, 1, 1),
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'JobDefinitionSummaries': [
{
'MonitoringJobDefinitionName': 'string',
'MonitoringJobDefinitionArn': 'string',
'CreationTime': datetime(2015, 1, 1),
'EndpointName': 'string'
},
],
}
Response Structure
(dict) --
JobDefinitionSummaries (list) --
A list of data quality monitoring job definitions.
(dict) --
Summary information about a monitoring job.
MonitoringJobDefinitionName (string) --
The name of the monitoring job.
MonitoringJobDefinitionArn (string) --
The Amazon Resource Name (ARN) of the monitoring job.
CreationTime (datetime) --
The time that the monitoring job was created.
EndpointName (string) --
The name of the endpoint that the job monitors.
paginator = client.get_paginator('list_device_fleets')
Creates an iterator that will paginate through responses from SageMaker.Client.list_device_fleets().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
LastModifiedTimeAfter=datetime(2015, 1, 1),
LastModifiedTimeBefore=datetime(2015, 1, 1),
NameContains='string',
SortBy='NAME'|'CREATION_TIME'|'LAST_MODIFIED_TIME',
SortOrder='Ascending'|'Descending',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'DeviceFleetSummaries': [
{
'DeviceFleetArn': 'string',
'DeviceFleetName': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1)
},
],
}
Response Structure
(dict) --
DeviceFleetSummaries (list) --
Summary of the device fleet.
(dict) --
Summary of the device fleet.
DeviceFleetArn (string) --
Amazon Resource Name (ARN) of the device fleet.
DeviceFleetName (string) --
Name of the device fleet.
CreationTime (datetime) --
Timestamp of when the device fleet was created.
LastModifiedTime (datetime) --
Timestamp of when the device fleet was last updated.
paginator = client.get_paginator('list_devices')
Creates an iterator that will paginate through responses from SageMaker.Client.list_devices().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
LatestHeartbeatAfter=datetime(2015, 1, 1),
ModelName='string',
DeviceFleetName='string',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'DeviceSummaries': [
{
'DeviceName': 'string',
'DeviceArn': 'string',
'Description': 'string',
'DeviceFleetName': 'string',
'IotThingName': 'string',
'RegistrationTime': datetime(2015, 1, 1),
'LatestHeartbeat': datetime(2015, 1, 1),
'Models': [
{
'ModelName': 'string',
'ModelVersion': 'string'
},
]
},
],
}
Response Structure
(dict) --
DeviceSummaries (list) --
Summary of devices.
(dict) --
Summary of the device.
DeviceName (string) --
The unique identifier of the device.
DeviceArn (string) --
Amazon Resource Name (ARN) of the device.
Description (string) --
A description of the device.
DeviceFleetName (string) --
The name of the fleet the device belongs to.
IotThingName (string) --
The Amazon Web Services Internet of Things (IoT) object thing name associated with the device..
RegistrationTime (datetime) --
The timestamp of the last registration or de-reregistration.
LatestHeartbeat (datetime) --
The last heartbeat received from the device.
Models (list) --
Models on the device.
(dict) --
Summary of model on edge device.
ModelName (string) --
The name of the model.
ModelVersion (string) --
The version model.
paginator = client.get_paginator('list_domains')
Creates an iterator that will paginate through responses from SageMaker.Client.list_domains().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
{
'Domains': [
{
'DomainArn': 'string',
'DomainId': 'string',
'DomainName': 'string',
'Status': 'Deleting'|'Failed'|'InService'|'Pending'|'Updating'|'Update_Failed'|'Delete_Failed',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'Url': 'string'
},
],
}
Response Structure
The list of domains.
The domain's details.
The domain's Amazon Resource Name (ARN).
The domain ID.
The domain name.
The status.
The creation time.
The last modified time.
The domain's URL.
paginator = client.get_paginator('list_edge_packaging_jobs')
Creates an iterator that will paginate through responses from SageMaker.Client.list_edge_packaging_jobs().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
LastModifiedTimeAfter=datetime(2015, 1, 1),
LastModifiedTimeBefore=datetime(2015, 1, 1),
NameContains='string',
ModelNameContains='string',
StatusEquals='STARTING'|'INPROGRESS'|'COMPLETED'|'FAILED'|'STOPPING'|'STOPPED',
SortBy='NAME'|'MODEL_NAME'|'CREATION_TIME'|'LAST_MODIFIED_TIME'|'STATUS',
SortOrder='Ascending'|'Descending',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'EdgePackagingJobSummaries': [
{
'EdgePackagingJobArn': 'string',
'EdgePackagingJobName': 'string',
'EdgePackagingJobStatus': 'STARTING'|'INPROGRESS'|'COMPLETED'|'FAILED'|'STOPPING'|'STOPPED',
'CompilationJobName': 'string',
'ModelName': 'string',
'ModelVersion': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1)
},
],
}
Response Structure
(dict) --
EdgePackagingJobSummaries (list) --
Summaries of edge packaging jobs.
(dict) --
Summary of edge packaging job.
EdgePackagingJobArn (string) --
The Amazon Resource Name (ARN) of the edge packaging job.
EdgePackagingJobName (string) --
The name of the edge packaging job.
EdgePackagingJobStatus (string) --
The status of the edge packaging job.
CompilationJobName (string) --
The name of the SageMaker Neo compilation job.
ModelName (string) --
The name of the model.
ModelVersion (string) --
The version of the model.
CreationTime (datetime) --
The timestamp of when the job was created.
LastModifiedTime (datetime) --
The timestamp of when the edge packaging job was last updated.
paginator = client.get_paginator('list_endpoint_configs')
Creates an iterator that will paginate through responses from SageMaker.Client.list_endpoint_configs().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
SortBy='Name'|'CreationTime',
SortOrder='Ascending'|'Descending',
NameContains='string',
CreationTimeBefore=datetime(2015, 1, 1),
CreationTimeAfter=datetime(2015, 1, 1),
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'EndpointConfigs': [
{
'EndpointConfigName': 'string',
'EndpointConfigArn': 'string',
'CreationTime': datetime(2015, 1, 1)
},
],
}
Response Structure
(dict) --
EndpointConfigs (list) --
An array of endpoint configurations.
(dict) --
Provides summary information for an endpoint configuration.
EndpointConfigName (string) --
The name of the endpoint configuration.
EndpointConfigArn (string) --
The Amazon Resource Name (ARN) of the endpoint configuration.
CreationTime (datetime) --
A timestamp that shows when the endpoint configuration was created.
paginator = client.get_paginator('list_endpoints')
Creates an iterator that will paginate through responses from SageMaker.Client.list_endpoints().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
SortBy='Name'|'CreationTime'|'Status',
SortOrder='Ascending'|'Descending',
NameContains='string',
CreationTimeBefore=datetime(2015, 1, 1),
CreationTimeAfter=datetime(2015, 1, 1),
LastModifiedTimeBefore=datetime(2015, 1, 1),
LastModifiedTimeAfter=datetime(2015, 1, 1),
StatusEquals='OutOfService'|'Creating'|'Updating'|'SystemUpdating'|'RollingBack'|'InService'|'Deleting'|'Failed',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'Endpoints': [
{
'EndpointName': 'string',
'EndpointArn': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'EndpointStatus': 'OutOfService'|'Creating'|'Updating'|'SystemUpdating'|'RollingBack'|'InService'|'Deleting'|'Failed'
},
],
}
Response Structure
(dict) --
Endpoints (list) --
An array or endpoint objects.
(dict) --
Provides summary information for an endpoint.
EndpointName (string) --
The name of the endpoint.
EndpointArn (string) --
The Amazon Resource Name (ARN) of the endpoint.
CreationTime (datetime) --
A timestamp that shows when the endpoint was created.
LastModifiedTime (datetime) --
A timestamp that shows when the endpoint was last modified.
EndpointStatus (string) --
The status of the endpoint.
To get a list of endpoints with a specified status, use the ListEndpointsInput$StatusEquals filter.
paginator = client.get_paginator('list_experiments')
Creates an iterator that will paginate through responses from SageMaker.Client.list_experiments().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
CreatedAfter=datetime(2015, 1, 1),
CreatedBefore=datetime(2015, 1, 1),
SortBy='Name'|'CreationTime',
SortOrder='Ascending'|'Descending',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'ExperimentSummaries': [
{
'ExperimentArn': 'string',
'ExperimentName': 'string',
'DisplayName': 'string',
'ExperimentSource': {
'SourceArn': 'string',
'SourceType': 'string'
},
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1)
},
],
}
Response Structure
(dict) --
ExperimentSummaries (list) --
A list of the summaries of your experiments.
(dict) --
A summary of the properties of an experiment. To get the complete set of properties, call the DescribeExperiment API and provide the ExperimentName .
ExperimentArn (string) --
The Amazon Resource Name (ARN) of the experiment.
ExperimentName (string) --
The name of the experiment.
DisplayName (string) --
The name of the experiment as displayed. If DisplayName isn't specified, ExperimentName is displayed.
ExperimentSource (dict) --
The source of the experiment.
SourceArn (string) --
The Amazon Resource Name (ARN) of the source.
SourceType (string) --
The source type.
CreationTime (datetime) --
When the experiment was created.
LastModifiedTime (datetime) --
When the experiment was last modified.
paginator = client.get_paginator('list_feature_groups')
Creates an iterator that will paginate through responses from SageMaker.Client.list_feature_groups().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
NameContains='string',
FeatureGroupStatusEquals='Creating'|'Created'|'CreateFailed'|'Deleting'|'DeleteFailed',
OfflineStoreStatusEquals='Active'|'Blocked'|'Disabled',
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
SortOrder='Ascending'|'Descending',
SortBy='Name'|'FeatureGroupStatus'|'OfflineStoreStatus'|'CreationTime',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'FeatureGroupSummaries': [
{
'FeatureGroupName': 'string',
'FeatureGroupArn': 'string',
'CreationTime': datetime(2015, 1, 1),
'FeatureGroupStatus': 'Creating'|'Created'|'CreateFailed'|'Deleting'|'DeleteFailed',
'OfflineStoreStatus': {
'Status': 'Active'|'Blocked'|'Disabled',
'BlockedReason': 'string'
}
},
],
}
Response Structure
(dict) --
FeatureGroupSummaries (list) --
A summary of feature groups.
(dict) --
The name, Arn, CreationTime , FeatureGroup values, LastUpdatedTime and EnableOnlineStorage status of a FeatureGroup .
FeatureGroupName (string) --
The name of FeatureGroup .
FeatureGroupArn (string) --
Unique identifier for the FeatureGroup .
CreationTime (datetime) --
A timestamp indicating the time of creation time of the FeatureGroup .
FeatureGroupStatus (string) --
The status of a FeatureGroup. The status can be any of the following: Creating , Created , CreateFail , Deleting or DetailFail .
OfflineStoreStatus (dict) --
Notifies you if replicating data into the OfflineStore has failed. Returns either: Active or Blocked .
Status (string) --
An OfflineStore status.
BlockedReason (string) --
The justification for why the OfflineStoreStatus is Blocked (if applicable).
paginator = client.get_paginator('list_flow_definitions')
Creates an iterator that will paginate through responses from SageMaker.Client.list_flow_definitions().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
SortOrder='Ascending'|'Descending',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'FlowDefinitionSummaries': [
{
'FlowDefinitionName': 'string',
'FlowDefinitionArn': 'string',
'FlowDefinitionStatus': 'Initializing'|'Active'|'Failed'|'Deleting',
'CreationTime': datetime(2015, 1, 1),
'FailureReason': 'string'
},
],
}
Response Structure
(dict) --
FlowDefinitionSummaries (list) --
An array of objects describing the flow definitions.
(dict) --
Contains summary information about the flow definition.
FlowDefinitionName (string) --
The name of the flow definition.
FlowDefinitionArn (string) --
The Amazon Resource Name (ARN) of the flow definition.
FlowDefinitionStatus (string) --
The status of the flow definition. Valid values:
CreationTime (datetime) --
The timestamp when SageMaker created the flow definition.
FailureReason (string) --
The reason why the flow definition creation failed. A failure reason is returned only when the flow definition status is Failed .
paginator = client.get_paginator('list_human_task_uis')
Creates an iterator that will paginate through responses from SageMaker.Client.list_human_task_uis().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
SortOrder='Ascending'|'Descending',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'HumanTaskUiSummaries': [
{
'HumanTaskUiName': 'string',
'HumanTaskUiArn': 'string',
'CreationTime': datetime(2015, 1, 1)
},
],
}
Response Structure
(dict) --
HumanTaskUiSummaries (list) --
An array of objects describing the human task user interfaces.
(dict) --
Container for human task user interface information.
HumanTaskUiName (string) --
The name of the human task user interface.
HumanTaskUiArn (string) --
The Amazon Resource Name (ARN) of the human task user interface.
CreationTime (datetime) --
A timestamp when SageMaker created the human task user interface.
paginator = client.get_paginator('list_hyper_parameter_tuning_jobs')
Creates an iterator that will paginate through responses from SageMaker.Client.list_hyper_parameter_tuning_jobs().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
SortBy='Name'|'Status'|'CreationTime',
SortOrder='Ascending'|'Descending',
NameContains='string',
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
LastModifiedTimeAfter=datetime(2015, 1, 1),
LastModifiedTimeBefore=datetime(2015, 1, 1),
StatusEquals='Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'HyperParameterTuningJobSummaries': [
{
'HyperParameterTuningJobName': 'string',
'HyperParameterTuningJobArn': 'string',
'HyperParameterTuningJobStatus': 'Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping',
'Strategy': 'Bayesian'|'Random',
'CreationTime': datetime(2015, 1, 1),
'HyperParameterTuningEndTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'TrainingJobStatusCounters': {
'Completed': 123,
'InProgress': 123,
'RetryableError': 123,
'NonRetryableError': 123,
'Stopped': 123
},
'ObjectiveStatusCounters': {
'Succeeded': 123,
'Pending': 123,
'Failed': 123
},
'ResourceLimits': {
'MaxNumberOfTrainingJobs': 123,
'MaxParallelTrainingJobs': 123
}
},
],
}
Response Structure
(dict) --
HyperParameterTuningJobSummaries (list) --
A list of HyperParameterTuningJobSummary objects that describe the tuning jobs that the ListHyperParameterTuningJobs request returned.
(dict) --
Provides summary information about a hyperparameter tuning job.
HyperParameterTuningJobName (string) --
The name of the tuning job.
HyperParameterTuningJobArn (string) --
The Amazon Resource Name (ARN) of the tuning job.
HyperParameterTuningJobStatus (string) --
The status of the tuning job.
Strategy (string) --
Specifies the search strategy hyperparameter tuning uses to choose which hyperparameters to use for each iteration. Currently, the only valid value is Bayesian.
CreationTime (datetime) --
The date and time that the tuning job was created.
HyperParameterTuningEndTime (datetime) --
The date and time that the tuning job ended.
LastModifiedTime (datetime) --
The date and time that the tuning job was modified.
TrainingJobStatusCounters (dict) --
The TrainingJobStatusCounters object that specifies the numbers of training jobs, categorized by status, that this tuning job launched.
Completed (integer) --
The number of completed training jobs launched by the hyperparameter tuning job.
InProgress (integer) --
The number of in-progress training jobs launched by a hyperparameter tuning job.
RetryableError (integer) --
The number of training jobs that failed, but can be retried. A failed training job can be retried only if it failed because an internal service error occurred.
NonRetryableError (integer) --
The number of training jobs that failed and can't be retried. A failed training job can't be retried if it failed because a client error occurred.
Stopped (integer) --
The number of training jobs launched by a hyperparameter tuning job that were manually stopped.
ObjectiveStatusCounters (dict) --
The ObjectiveStatusCounters object that specifies the numbers of training jobs, categorized by objective metric status, that this tuning job launched.
Succeeded (integer) --
The number of training jobs whose final objective metric was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.
Pending (integer) --
The number of training jobs that are in progress and pending evaluation of their final objective metric.
Failed (integer) --
The number of training jobs whose final objective metric was not evaluated and used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.
ResourceLimits (dict) --
The ResourceLimits object that specifies the maximum number of training jobs and parallel training jobs allowed for this tuning job.
MaxNumberOfTrainingJobs (integer) --
The maximum number of training jobs that a hyperparameter tuning job can launch.
MaxParallelTrainingJobs (integer) --
The maximum number of concurrent training jobs that a hyperparameter tuning job can launch.
paginator = client.get_paginator('list_image_versions')
Creates an iterator that will paginate through responses from SageMaker.Client.list_image_versions().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
ImageName='string',
LastModifiedTimeAfter=datetime(2015, 1, 1),
LastModifiedTimeBefore=datetime(2015, 1, 1),
SortBy='CREATION_TIME'|'LAST_MODIFIED_TIME'|'VERSION',
SortOrder='ASCENDING'|'DESCENDING',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
[REQUIRED]
The name of the image to list the versions of.
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'ImageVersions': [
{
'CreationTime': datetime(2015, 1, 1),
'FailureReason': 'string',
'ImageArn': 'string',
'ImageVersionArn': 'string',
'ImageVersionStatus': 'CREATING'|'CREATED'|'CREATE_FAILED'|'DELETING'|'DELETE_FAILED',
'LastModifiedTime': datetime(2015, 1, 1),
'Version': 123
},
],
}
Response Structure
(dict) --
ImageVersions (list) --
A list of versions and their properties.
(dict) --
A version of a SageMaker Image . A version represents an existing container image.
CreationTime (datetime) --
When the version was created.
FailureReason (string) --
When a create or delete operation fails, the reason for the failure.
ImageArn (string) --
The Amazon Resource Name (ARN) of the image the version is based on.
ImageVersionArn (string) --
The ARN of the version.
ImageVersionStatus (string) --
The status of the version.
LastModifiedTime (datetime) --
When the version was last modified.
Version (integer) --
The version number.
paginator = client.get_paginator('list_images')
Creates an iterator that will paginate through responses from SageMaker.Client.list_images().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
LastModifiedTimeAfter=datetime(2015, 1, 1),
LastModifiedTimeBefore=datetime(2015, 1, 1),
NameContains='string',
SortBy='CREATION_TIME'|'LAST_MODIFIED_TIME'|'IMAGE_NAME',
SortOrder='ASCENDING'|'DESCENDING',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'Images': [
{
'CreationTime': datetime(2015, 1, 1),
'Description': 'string',
'DisplayName': 'string',
'FailureReason': 'string',
'ImageArn': 'string',
'ImageName': 'string',
'ImageStatus': 'CREATING'|'CREATED'|'CREATE_FAILED'|'UPDATING'|'UPDATE_FAILED'|'DELETING'|'DELETE_FAILED',
'LastModifiedTime': datetime(2015, 1, 1)
},
],
}
Response Structure
(dict) --
Images (list) --
A list of images and their properties.
(dict) --
A SageMaker image. A SageMaker image represents a set of container images that are derived from a common base container image. Each of these container images is represented by a SageMaker ImageVersion .
CreationTime (datetime) --
When the image was created.
Description (string) --
The description of the image.
DisplayName (string) --
The name of the image as displayed.
FailureReason (string) --
When a create, update, or delete operation fails, the reason for the failure.
ImageArn (string) --
The Amazon Resource Name (ARN) of the image.
ImageName (string) --
The name of the image.
ImageStatus (string) --
The status of the image.
LastModifiedTime (datetime) --
When the image was last modified.
paginator = client.get_paginator('list_labeling_jobs')
Creates an iterator that will paginate through responses from SageMaker.Client.list_labeling_jobs().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
LastModifiedTimeAfter=datetime(2015, 1, 1),
LastModifiedTimeBefore=datetime(2015, 1, 1),
NameContains='string',
SortBy='Name'|'CreationTime'|'Status',
SortOrder='Ascending'|'Descending',
StatusEquals='Initializing'|'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'LabelingJobSummaryList': [
{
'LabelingJobName': 'string',
'LabelingJobArn': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'LabelingJobStatus': 'Initializing'|'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'LabelCounters': {
'TotalLabeled': 123,
'HumanLabeled': 123,
'MachineLabeled': 123,
'FailedNonRetryableError': 123,
'Unlabeled': 123
},
'WorkteamArn': 'string',
'PreHumanTaskLambdaArn': 'string',
'AnnotationConsolidationLambdaArn': 'string',
'FailureReason': 'string',
'LabelingJobOutput': {
'OutputDatasetS3Uri': 'string',
'FinalActiveLearningModelArn': 'string'
},
'InputConfig': {
'DataSource': {
'S3DataSource': {
'ManifestS3Uri': 'string'
},
'SnsDataSource': {
'SnsTopicArn': 'string'
}
},
'DataAttributes': {
'ContentClassifiers': [
'FreeOfPersonallyIdentifiableInformation'|'FreeOfAdultContent',
]
}
}
},
],
}
Response Structure
(dict) --
LabelingJobSummaryList (list) --
An array of LabelingJobSummary objects, each describing a labeling job.
(dict) --
Provides summary information about a labeling job.
LabelingJobName (string) --
The name of the labeling job.
LabelingJobArn (string) --
The Amazon Resource Name (ARN) assigned to the labeling job when it was created.
CreationTime (datetime) --
The date and time that the job was created (timestamp).
LastModifiedTime (datetime) --
The date and time that the job was last modified (timestamp).
LabelingJobStatus (string) --
The current status of the labeling job.
LabelCounters (dict) --
Counts showing the progress of the labeling job.
TotalLabeled (integer) --
The total number of objects labeled.
HumanLabeled (integer) --
The total number of objects labeled by a human worker.
MachineLabeled (integer) --
The total number of objects labeled by automated data labeling.
FailedNonRetryableError (integer) --
The total number of objects that could not be labeled due to an error.
Unlabeled (integer) --
The total number of objects not yet labeled.
WorkteamArn (string) --
The Amazon Resource Name (ARN) of the work team assigned to the job.
PreHumanTaskLambdaArn (string) --
The Amazon Resource Name (ARN) of a Lambda function. The function is run before each data object is sent to a worker.
AnnotationConsolidationLambdaArn (string) --
The Amazon Resource Name (ARN) of the Lambda function used to consolidate the annotations from individual workers into a label for a data object. For more information, see Annotation Consolidation .
FailureReason (string) --
If the LabelingJobStatus field is Failed , this field contains a description of the error.
LabelingJobOutput (dict) --
The location of the output produced by the labeling job.
OutputDatasetS3Uri (string) --
The Amazon S3 bucket location of the manifest file for labeled data.
FinalActiveLearningModelArn (string) --
The Amazon Resource Name (ARN) for the most recent Amazon SageMaker model trained as part of automated data labeling.
InputConfig (dict) --
Input configuration for the labeling job.
DataSource (dict) --
The location of the input data.
S3DataSource (dict) --
The Amazon S3 location of the input data objects.
ManifestS3Uri (string) --
The Amazon S3 location of the manifest file that describes the input data objects.
The input manifest file referenced in ManifestS3Uri must contain one of the following keys: source-ref or source . The value of the keys are interpreted as follows:
If you are a new user of Ground Truth, it is recommended you review Use an Input Manifest File in the Amazon SageMaker Developer Guide to learn how to create an input manifest file.
SnsDataSource (dict) --
An Amazon SNS data source used for streaming labeling jobs. To learn more, see Send Data to a Streaming Labeling Job .
SnsTopicArn (string) --
The Amazon SNS input topic Amazon Resource Name (ARN). Specify the ARN of the input topic you will use to send new data objects to a streaming labeling job.
DataAttributes (dict) --
Attributes of the data specified by the customer.
ContentClassifiers (list) --
Declares that your content is free of personally identifiable information or adult content. Amazon SageMaker may restrict the Amazon Mechanical Turk workers that can view your task based on this information.
paginator = client.get_paginator('list_labeling_jobs_for_workteam')
Creates an iterator that will paginate through responses from SageMaker.Client.list_labeling_jobs_for_workteam().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
WorkteamArn='string',
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
JobReferenceCodeContains='string',
SortBy='CreationTime',
SortOrder='Ascending'|'Descending',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
[REQUIRED]
The Amazon Resource Name (ARN) of the work team for which you want to see labeling jobs for.
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'LabelingJobSummaryList': [
{
'LabelingJobName': 'string',
'JobReferenceCode': 'string',
'WorkRequesterAccountId': 'string',
'CreationTime': datetime(2015, 1, 1),
'LabelCounters': {
'HumanLabeled': 123,
'PendingHuman': 123,
'Total': 123
},
'NumberOfHumanWorkersPerDataObject': 123
},
],
}
Response Structure
(dict) --
LabelingJobSummaryList (list) --
An array of LabelingJobSummary objects, each describing a labeling job.
(dict) --
Provides summary information for a work team.
LabelingJobName (string) --
The name of the labeling job that the work team is assigned to.
JobReferenceCode (string) --
A unique identifier for a labeling job. You can use this to refer to a specific labeling job.
WorkRequesterAccountId (string) --
The Amazon Web Services account ID of the account used to start the labeling job.
CreationTime (datetime) --
The date and time that the labeling job was created.
LabelCounters (dict) --
Provides information about the progress of a labeling job.
HumanLabeled (integer) --
The total number of data objects labeled by a human worker.
PendingHuman (integer) --
The total number of data objects that need to be labeled by a human worker.
Total (integer) --
The total number of tasks in the labeling job.
NumberOfHumanWorkersPerDataObject (integer) --
The configured number of workers per data object.
paginator = client.get_paginator('list_model_bias_job_definitions')
Creates an iterator that will paginate through responses from SageMaker.Client.list_model_bias_job_definitions().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
EndpointName='string',
SortBy='Name'|'CreationTime',
SortOrder='Ascending'|'Descending',
NameContains='string',
CreationTimeBefore=datetime(2015, 1, 1),
CreationTimeAfter=datetime(2015, 1, 1),
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'JobDefinitionSummaries': [
{
'MonitoringJobDefinitionName': 'string',
'MonitoringJobDefinitionArn': 'string',
'CreationTime': datetime(2015, 1, 1),
'EndpointName': 'string'
},
],
}
Response Structure
(dict) --
JobDefinitionSummaries (list) --
A JSON array in which each element is a summary for a model bias jobs.
(dict) --
Summary information about a monitoring job.
MonitoringJobDefinitionName (string) --
The name of the monitoring job.
MonitoringJobDefinitionArn (string) --
The Amazon Resource Name (ARN) of the monitoring job.
CreationTime (datetime) --
The time that the monitoring job was created.
EndpointName (string) --
The name of the endpoint that the job monitors.
paginator = client.get_paginator('list_model_explainability_job_definitions')
Creates an iterator that will paginate through responses from SageMaker.Client.list_model_explainability_job_definitions().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
EndpointName='string',
SortBy='Name'|'CreationTime',
SortOrder='Ascending'|'Descending',
NameContains='string',
CreationTimeBefore=datetime(2015, 1, 1),
CreationTimeAfter=datetime(2015, 1, 1),
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'JobDefinitionSummaries': [
{
'MonitoringJobDefinitionName': 'string',
'MonitoringJobDefinitionArn': 'string',
'CreationTime': datetime(2015, 1, 1),
'EndpointName': 'string'
},
],
}
Response Structure
(dict) --
JobDefinitionSummaries (list) --
A JSON array in which each element is a summary for a explainability bias jobs.
(dict) --
Summary information about a monitoring job.
MonitoringJobDefinitionName (string) --
The name of the monitoring job.
MonitoringJobDefinitionArn (string) --
The Amazon Resource Name (ARN) of the monitoring job.
CreationTime (datetime) --
The time that the monitoring job was created.
EndpointName (string) --
The name of the endpoint that the job monitors.
paginator = client.get_paginator('list_model_package_groups')
Creates an iterator that will paginate through responses from SageMaker.Client.list_model_package_groups().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
NameContains='string',
SortBy='Name'|'CreationTime',
SortOrder='Ascending'|'Descending',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'ModelPackageGroupSummaryList': [
{
'ModelPackageGroupName': 'string',
'ModelPackageGroupArn': 'string',
'ModelPackageGroupDescription': 'string',
'CreationTime': datetime(2015, 1, 1),
'ModelPackageGroupStatus': 'Pending'|'InProgress'|'Completed'|'Failed'|'Deleting'|'DeleteFailed'
},
],
}
Response Structure
(dict) --
ModelPackageGroupSummaryList (list) --
A list of summaries of the model groups in your Amazon Web Services account.
(dict) --
Summary information about a model group.
ModelPackageGroupName (string) --
The name of the model group.
ModelPackageGroupArn (string) --
The Amazon Resource Name (ARN) of the model group.
ModelPackageGroupDescription (string) --
A description of the model group.
CreationTime (datetime) --
The time that the model group was created.
ModelPackageGroupStatus (string) --
The status of the model group.
paginator = client.get_paginator('list_model_packages')
Creates an iterator that will paginate through responses from SageMaker.Client.list_model_packages().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
NameContains='string',
ModelApprovalStatus='Approved'|'Rejected'|'PendingManualApproval',
ModelPackageGroupName='string',
ModelPackageType='Versioned'|'Unversioned'|'Both',
SortBy='Name'|'CreationTime',
SortOrder='Ascending'|'Descending',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A filter that returns onlyl the model packages of the specified type. This can be one of the following values.
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'ModelPackageSummaryList': [
{
'ModelPackageName': 'string',
'ModelPackageGroupName': 'string',
'ModelPackageVersion': 123,
'ModelPackageArn': 'string',
'ModelPackageDescription': 'string',
'CreationTime': datetime(2015, 1, 1),
'ModelPackageStatus': 'Pending'|'InProgress'|'Completed'|'Failed'|'Deleting',
'ModelApprovalStatus': 'Approved'|'Rejected'|'PendingManualApproval'
},
],
}
Response Structure
(dict) --
ModelPackageSummaryList (list) --
An array of ModelPackageSummary objects, each of which lists a model package.
(dict) --
Provides summary information about a model package.
ModelPackageName (string) --
The name of the model package.
ModelPackageGroupName (string) --
If the model package is a versioned model, the model group that the versioned model belongs to.
ModelPackageVersion (integer) --
If the model package is a versioned model, the version of the model.
ModelPackageArn (string) --
The Amazon Resource Name (ARN) of the model package.
ModelPackageDescription (string) --
A brief description of the model package.
CreationTime (datetime) --
A timestamp that shows when the model package was created.
ModelPackageStatus (string) --
The overall status of the model package.
ModelApprovalStatus (string) --
The approval status of the model. This can be one of the following values.
paginator = client.get_paginator('list_model_quality_job_definitions')
Creates an iterator that will paginate through responses from SageMaker.Client.list_model_quality_job_definitions().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
EndpointName='string',
SortBy='Name'|'CreationTime',
SortOrder='Ascending'|'Descending',
NameContains='string',
CreationTimeBefore=datetime(2015, 1, 1),
CreationTimeAfter=datetime(2015, 1, 1),
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'JobDefinitionSummaries': [
{
'MonitoringJobDefinitionName': 'string',
'MonitoringJobDefinitionArn': 'string',
'CreationTime': datetime(2015, 1, 1),
'EndpointName': 'string'
},
],
}
Response Structure
(dict) --
JobDefinitionSummaries (list) --
A list of summaries of model quality monitoring job definitions.
(dict) --
Summary information about a monitoring job.
MonitoringJobDefinitionName (string) --
The name of the monitoring job.
MonitoringJobDefinitionArn (string) --
The Amazon Resource Name (ARN) of the monitoring job.
CreationTime (datetime) --
The time that the monitoring job was created.
EndpointName (string) --
The name of the endpoint that the job monitors.
paginator = client.get_paginator('list_models')
Creates an iterator that will paginate through responses from SageMaker.Client.list_models().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
SortBy='Name'|'CreationTime',
SortOrder='Ascending'|'Descending',
NameContains='string',
CreationTimeBefore=datetime(2015, 1, 1),
CreationTimeAfter=datetime(2015, 1, 1),
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'Models': [
{
'ModelName': 'string',
'ModelArn': 'string',
'CreationTime': datetime(2015, 1, 1)
},
],
}
Response Structure
(dict) --
Models (list) --
An array of ModelSummary objects, each of which lists a model.
(dict) --
Provides summary information about a model.
ModelName (string) --
The name of the model that you want a summary for.
ModelArn (string) --
The Amazon Resource Name (ARN) of the model.
CreationTime (datetime) --
A timestamp that indicates when the model was created.
paginator = client.get_paginator('list_monitoring_executions')
Creates an iterator that will paginate through responses from SageMaker.Client.list_monitoring_executions().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
MonitoringScheduleName='string',
EndpointName='string',
SortBy='CreationTime'|'ScheduledTime'|'Status',
SortOrder='Ascending'|'Descending',
ScheduledTimeBefore=datetime(2015, 1, 1),
ScheduledTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
CreationTimeAfter=datetime(2015, 1, 1),
LastModifiedTimeBefore=datetime(2015, 1, 1),
LastModifiedTimeAfter=datetime(2015, 1, 1),
StatusEquals='Pending'|'Completed'|'CompletedWithViolations'|'InProgress'|'Failed'|'Stopping'|'Stopped',
MonitoringJobDefinitionName='string',
MonitoringTypeEquals='DataQuality'|'ModelQuality'|'ModelBias'|'ModelExplainability',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'MonitoringExecutionSummaries': [
{
'MonitoringScheduleName': 'string',
'ScheduledTime': datetime(2015, 1, 1),
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'MonitoringExecutionStatus': 'Pending'|'Completed'|'CompletedWithViolations'|'InProgress'|'Failed'|'Stopping'|'Stopped',
'ProcessingJobArn': 'string',
'EndpointName': 'string',
'FailureReason': 'string',
'MonitoringJobDefinitionName': 'string',
'MonitoringType': 'DataQuality'|'ModelQuality'|'ModelBias'|'ModelExplainability'
},
],
}
Response Structure
(dict) --
MonitoringExecutionSummaries (list) --
A JSON array in which each element is a summary for a monitoring execution.
(dict) --
Summary of information about the last monitoring job to run.
MonitoringScheduleName (string) --
The name of the monitoring schedule.
ScheduledTime (datetime) --
The time the monitoring job was scheduled.
CreationTime (datetime) --
The time at which the monitoring job was created.
LastModifiedTime (datetime) --
A timestamp that indicates the last time the monitoring job was modified.
MonitoringExecutionStatus (string) --
The status of the monitoring job.
ProcessingJobArn (string) --
The Amazon Resource Name (ARN) of the monitoring job.
EndpointName (string) --
The name of the endpoint used to run the monitoring job.
FailureReason (string) --
Contains the reason a monitoring job failed, if it failed.
MonitoringJobDefinitionName (string) --
The name of the monitoring job.
MonitoringType (string) --
The type of the monitoring job.
paginator = client.get_paginator('list_monitoring_schedules')
Creates an iterator that will paginate through responses from SageMaker.Client.list_monitoring_schedules().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
EndpointName='string',
SortBy='Name'|'CreationTime'|'Status',
SortOrder='Ascending'|'Descending',
NameContains='string',
CreationTimeBefore=datetime(2015, 1, 1),
CreationTimeAfter=datetime(2015, 1, 1),
LastModifiedTimeBefore=datetime(2015, 1, 1),
LastModifiedTimeAfter=datetime(2015, 1, 1),
StatusEquals='Pending'|'Failed'|'Scheduled'|'Stopped',
MonitoringJobDefinitionName='string',
MonitoringTypeEquals='DataQuality'|'ModelQuality'|'ModelBias'|'ModelExplainability',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'MonitoringScheduleSummaries': [
{
'MonitoringScheduleName': 'string',
'MonitoringScheduleArn': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'MonitoringScheduleStatus': 'Pending'|'Failed'|'Scheduled'|'Stopped',
'EndpointName': 'string',
'MonitoringJobDefinitionName': 'string',
'MonitoringType': 'DataQuality'|'ModelQuality'|'ModelBias'|'ModelExplainability'
},
],
}
Response Structure
(dict) --
MonitoringScheduleSummaries (list) --
A JSON array in which each element is a summary for a monitoring schedule.
(dict) --
Summarizes the monitoring schedule.
MonitoringScheduleName (string) --
The name of the monitoring schedule.
MonitoringScheduleArn (string) --
The Amazon Resource Name (ARN) of the monitoring schedule.
CreationTime (datetime) --
The creation time of the monitoring schedule.
LastModifiedTime (datetime) --
The last time the monitoring schedule was modified.
MonitoringScheduleStatus (string) --
The status of the monitoring schedule.
EndpointName (string) --
The name of the endpoint using the monitoring schedule.
MonitoringJobDefinitionName (string) --
The name of the monitoring job definition that the schedule is for.
MonitoringType (string) --
The type of the monitoring job definition that the schedule is for.
paginator = client.get_paginator('list_notebook_instance_lifecycle_configs')
Creates an iterator that will paginate through responses from SageMaker.Client.list_notebook_instance_lifecycle_configs().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
SortBy='Name'|'CreationTime'|'LastModifiedTime',
SortOrder='Ascending'|'Descending',
NameContains='string',
CreationTimeBefore=datetime(2015, 1, 1),
CreationTimeAfter=datetime(2015, 1, 1),
LastModifiedTimeBefore=datetime(2015, 1, 1),
LastModifiedTimeAfter=datetime(2015, 1, 1),
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'NotebookInstanceLifecycleConfigs': [
{
'NotebookInstanceLifecycleConfigName': 'string',
'NotebookInstanceLifecycleConfigArn': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1)
},
]
}
Response Structure
(dict) --
NotebookInstanceLifecycleConfigs (list) --
An array of NotebookInstanceLifecycleConfiguration objects, each listing a lifecycle configuration.
(dict) --
Provides a summary of a notebook instance lifecycle configuration.
NotebookInstanceLifecycleConfigName (string) --
The name of the lifecycle configuration.
NotebookInstanceLifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the lifecycle configuration.
CreationTime (datetime) --
A timestamp that tells when the lifecycle configuration was created.
LastModifiedTime (datetime) --
A timestamp that tells when the lifecycle configuration was last modified.
paginator = client.get_paginator('list_notebook_instances')
Creates an iterator that will paginate through responses from SageMaker.Client.list_notebook_instances().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
SortBy='Name'|'CreationTime'|'Status',
SortOrder='Ascending'|'Descending',
NameContains='string',
CreationTimeBefore=datetime(2015, 1, 1),
CreationTimeAfter=datetime(2015, 1, 1),
LastModifiedTimeBefore=datetime(2015, 1, 1),
LastModifiedTimeAfter=datetime(2015, 1, 1),
StatusEquals='Pending'|'InService'|'Stopping'|'Stopped'|'Failed'|'Deleting'|'Updating',
NotebookInstanceLifecycleConfigNameContains='string',
DefaultCodeRepositoryContains='string',
AdditionalCodeRepositoryEquals='string',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'NotebookInstances': [
{
'NotebookInstanceName': 'string',
'NotebookInstanceArn': 'string',
'NotebookInstanceStatus': 'Pending'|'InService'|'Stopping'|'Stopped'|'Failed'|'Deleting'|'Updating',
'Url': 'string',
'InstanceType': 'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'NotebookInstanceLifecycleConfigName': 'string',
'DefaultCodeRepository': 'string',
'AdditionalCodeRepositories': [
'string',
]
},
]
}
Response Structure
(dict) --
NotebookInstances (list) --
An array of NotebookInstanceSummary objects, one for each notebook instance.
(dict) --
Provides summary information for an Amazon SageMaker notebook instance.
NotebookInstanceName (string) --
The name of the notebook instance that you want a summary for.
NotebookInstanceArn (string) --
The Amazon Resource Name (ARN) of the notebook instance.
NotebookInstanceStatus (string) --
The status of the notebook instance.
Url (string) --
The URL that you use to connect to the Jupyter instance running in your notebook instance.
InstanceType (string) --
The type of ML compute instance that the notebook instance is running on.
CreationTime (datetime) --
A timestamp that shows when the notebook instance was created.
LastModifiedTime (datetime) --
A timestamp that shows when the notebook instance was last modified.
NotebookInstanceLifecycleConfigName (string) --
The name of a notebook instance lifecycle configuration associated with this notebook instance.
For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance .
DefaultCodeRepository (string) --
The Git repository associated with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in Amazon Web Services CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances .
AdditionalCodeRepositories (list) --
An array of up to three Git repositories associated with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in Amazon Web Services CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances .
paginator = client.get_paginator('list_pipeline_execution_steps')
Creates an iterator that will paginate through responses from SageMaker.Client.list_pipeline_execution_steps().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
PipelineExecutionArn='string',
SortOrder='Ascending'|'Descending',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'PipelineExecutionSteps': [
{
'StepName': 'string',
'StartTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1),
'StepStatus': 'Starting'|'Executing'|'Stopping'|'Stopped'|'Failed'|'Succeeded',
'CacheHitResult': {
'SourcePipelineExecutionArn': 'string'
},
'FailureReason': 'string',
'Metadata': {
'TrainingJob': {
'Arn': 'string'
},
'ProcessingJob': {
'Arn': 'string'
},
'TransformJob': {
'Arn': 'string'
},
'TuningJob': {
'Arn': 'string'
},
'Model': {
'Arn': 'string'
},
'RegisterModel': {
'Arn': 'string'
},
'Condition': {
'Outcome': 'True'|'False'
},
'Callback': {
'CallbackToken': 'string',
'SqsQueueUrl': 'string',
'OutputParameters': [
{
'Name': 'string',
'Value': 'string'
},
]
}
}
},
],
}
Response Structure
(dict) --
PipelineExecutionSteps (list) --
A list of PipeLineExecutionStep objects. Each PipeLineExecutionStep consists of StepName, StartTime, EndTime, StepStatus, and Metadata. Metadata is an object with properties for each job that contains relevant information about the job created by the step.
(dict) --
An execution of a step in a pipeline.
StepName (string) --
The name of the step that is executed.
StartTime (datetime) --
The time that the step started executing.
EndTime (datetime) --
The time that the step stopped executing.
StepStatus (string) --
The status of the step execution.
CacheHitResult (dict) --
If this pipeline execution step was cached, details on the cache hit.
SourcePipelineExecutionArn (string) --
The Amazon Resource Name (ARN) of the pipeline execution.
FailureReason (string) --
The reason why the step failed execution. This is only returned if the step failed its execution.
Metadata (dict) --
Metadata for the step execution.
TrainingJob (dict) --
The Amazon Resource Name (ARN) of the training job that was run by this step execution.
Arn (string) --
The Amazon Resource Name (ARN) of the training job that was run by this step execution.
ProcessingJob (dict) --
The Amazon Resource Name (ARN) of the processing job that was run by this step execution.
Arn (string) --
The Amazon Resource Name (ARN) of the processing job.
TransformJob (dict) --
The Amazon Resource Name (ARN) of the transform job that was run by this step execution.
Arn (string) --
The Amazon Resource Name (ARN) of the transform job that was run by this step execution.
TuningJob (dict) --
The Amazon Resource Name (ARN) of the tuning job that was run by this step execution.
Arn (string) --
The Amazon Resource Name (ARN) of the tuning job that was run by this step execution.
Model (dict) --
Metadata for the Model step.
Arn (string) --
The Amazon Resource Name (ARN) of the created model.
RegisterModel (dict) --
Metadata for the RegisterModel step.
Arn (string) --
The Amazon Resource Name (ARN) of the model package.
Condition (dict) --
If this is a Condition step metadata object, details on the condition.
Outcome (string) --
The outcome of the Condition step evaluation.
Callback (dict) --
Metadata about a callback step.
CallbackToken (string) --
The pipeline generated token from the Amazon SQS queue.
SqsQueueUrl (string) --
The URL of the Amazon Simple Queue Service (Amazon SQS) queue used by the callback step.
OutputParameters (list) --
A list of the output parameters of the callback step.
(dict) --
An output parameter of a pipeline step.
Name (string) --
The name of the output parameter.
Value (string) --
The value of the output parameter.
paginator = client.get_paginator('list_pipeline_executions')
Creates an iterator that will paginate through responses from SageMaker.Client.list_pipeline_executions().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
PipelineName='string',
CreatedAfter=datetime(2015, 1, 1),
CreatedBefore=datetime(2015, 1, 1),
SortBy='CreationTime'|'PipelineExecutionArn',
SortOrder='Ascending'|'Descending',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
[REQUIRED]
The name of the pipeline.
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'PipelineExecutionSummaries': [
{
'PipelineExecutionArn': 'string',
'StartTime': datetime(2015, 1, 1),
'PipelineExecutionStatus': 'Executing'|'Stopping'|'Stopped'|'Failed'|'Succeeded',
'PipelineExecutionDescription': 'string',
'PipelineExecutionDisplayName': 'string'
},
],
}
Response Structure
(dict) --
PipelineExecutionSummaries (list) --
Contains a sorted list of pipeline execution summary objects matching the specified filters. Each run summary includes the Amazon Resource Name (ARN) of the pipeline execution, the run date, and the status. This list can be empty.
(dict) --
A pipeline execution summary.
PipelineExecutionArn (string) --
The Amazon Resource Name (ARN) of the pipeline execution.
StartTime (datetime) --
The start time of the pipeline execution.
PipelineExecutionStatus (string) --
The status of the pipeline execution.
PipelineExecutionDescription (string) --
The description of the pipeline execution.
PipelineExecutionDisplayName (string) --
The display name of the pipeline execution.
paginator = client.get_paginator('list_pipeline_parameters_for_execution')
Creates an iterator that will paginate through responses from SageMaker.Client.list_pipeline_parameters_for_execution().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
PipelineExecutionArn='string',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
[REQUIRED]
The Amazon Resource Name (ARN) of the pipeline execution.
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'PipelineParameters': [
{
'Name': 'string',
'Value': 'string'
},
],
}
Response Structure
(dict) --
PipelineParameters (list) --
Contains a list of pipeline parameters. This list can be empty.
(dict) --
Assigns a value to a named Pipeline parameter.
Name (string) --
The name of the parameter to assign a value to. This parameter name must match a named parameter in the pipeline definition.
Value (string) --
The literal value for the parameter.
paginator = client.get_paginator('list_pipelines')
Creates an iterator that will paginate through responses from SageMaker.Client.list_pipelines().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
PipelineNamePrefix='string',
CreatedAfter=datetime(2015, 1, 1),
CreatedBefore=datetime(2015, 1, 1),
SortBy='Name'|'CreationTime',
SortOrder='Ascending'|'Descending',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'PipelineSummaries': [
{
'PipelineArn': 'string',
'PipelineName': 'string',
'PipelineDisplayName': 'string',
'PipelineDescription': 'string',
'RoleArn': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'LastExecutionTime': datetime(2015, 1, 1)
},
],
}
Response Structure
(dict) --
PipelineSummaries (list) --
Contains a sorted list of PipelineSummary objects matching the specified filters. Each PipelineSummary consists of PipelineArn, PipelineName, ExperimentName, PipelineDescription, CreationTime, LastModifiedTime, LastRunTime, and RoleArn. This list can be empty.
(dict) --
A summary of a pipeline.
PipelineArn (string) --
The Amazon Resource Name (ARN) of the pipeline.
PipelineName (string) --
The name of the pipeline.
PipelineDisplayName (string) --
The display name of the pipeline.
PipelineDescription (string) --
The description of the pipeline.
RoleArn (string) --
The Amazon Resource Name (ARN) that the pipeline used to execute.
CreationTime (datetime) --
The creation time of the pipeline.
LastModifiedTime (datetime) --
The time that the pipeline was last modified.
LastExecutionTime (datetime) --
The last time that a pipeline execution began.
paginator = client.get_paginator('list_processing_jobs')
Creates an iterator that will paginate through responses from SageMaker.Client.list_processing_jobs().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
LastModifiedTimeAfter=datetime(2015, 1, 1),
LastModifiedTimeBefore=datetime(2015, 1, 1),
NameContains='string',
StatusEquals='InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
SortBy='Name'|'CreationTime'|'Status',
SortOrder='Ascending'|'Descending',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'ProcessingJobSummaries': [
{
'ProcessingJobName': 'string',
'ProcessingJobArn': 'string',
'CreationTime': datetime(2015, 1, 1),
'ProcessingEndTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'ProcessingJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'FailureReason': 'string',
'ExitMessage': 'string'
},
],
}
Response Structure
(dict) --
ProcessingJobSummaries (list) --
An array of ProcessingJobSummary objects, each listing a processing job.
(dict) --
Summary of information about a processing job.
ProcessingJobName (string) --
The name of the processing job.
ProcessingJobArn (string) --
The Amazon Resource Name (ARN) of the processing job..
CreationTime (datetime) --
The time at which the processing job was created.
ProcessingEndTime (datetime) --
The time at which the processing job completed.
LastModifiedTime (datetime) --
A timestamp that indicates the last time the processing job was modified.
ProcessingJobStatus (string) --
The status of the processing job.
FailureReason (string) --
A string, up to one KB in size, that contains the reason a processing job failed, if it failed.
ExitMessage (string) --
An optional string, up to one KB in size, that contains metadata from the processing container when the processing job exits.
paginator = client.get_paginator('list_subscribed_workteams')
Creates an iterator that will paginate through responses from SageMaker.Client.list_subscribed_workteams().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
NameContains='string',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'SubscribedWorkteams': [
{
'WorkteamArn': 'string',
'MarketplaceTitle': 'string',
'SellerName': 'string',
'MarketplaceDescription': 'string',
'ListingId': 'string'
},
],
}
Response Structure
(dict) --
SubscribedWorkteams (list) --
An array of Workteam objects, each describing a work team.
(dict) --
Describes a work team of a vendor that does the a labelling job.
WorkteamArn (string) --
The Amazon Resource Name (ARN) of the vendor that you have subscribed.
MarketplaceTitle (string) --
The title of the service provided by the vendor in the Amazon Marketplace.
SellerName (string) --
The name of the vendor in the Amazon Marketplace.
MarketplaceDescription (string) --
The description of the vendor from the Amazon Marketplace.
ListingId (string) --
Marketplace product listing ID.
paginator = client.get_paginator('list_tags')
Creates an iterator that will paginate through responses from SageMaker.Client.list_tags().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
ResourceArn='string',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
[REQUIRED]
The Amazon Resource Name (ARN) of the resource whose tags you want to retrieve.
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
],
}
Response Structure
(dict) --
Tags (list) --
An array of Tag objects, each with a tag key and a value.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
paginator = client.get_paginator('list_training_jobs')
Creates an iterator that will paginate through responses from SageMaker.Client.list_training_jobs().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
LastModifiedTimeAfter=datetime(2015, 1, 1),
LastModifiedTimeBefore=datetime(2015, 1, 1),
NameContains='string',
StatusEquals='InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
SortBy='Name'|'CreationTime'|'Status',
SortOrder='Ascending'|'Descending',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'TrainingJobSummaries': [
{
'TrainingJobName': 'string',
'TrainingJobArn': 'string',
'CreationTime': datetime(2015, 1, 1),
'TrainingEndTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'TrainingJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped'
},
],
}
Response Structure
(dict) --
TrainingJobSummaries (list) --
An array of TrainingJobSummary objects, each listing a training job.
(dict) --
Provides summary information about a training job.
TrainingJobName (string) --
The name of the training job that you want a summary for.
TrainingJobArn (string) --
The Amazon Resource Name (ARN) of the training job.
CreationTime (datetime) --
A timestamp that shows when the training job was created.
TrainingEndTime (datetime) --
A timestamp that shows when the training job ended. This field is set only if the training job has one of the terminal statuses (Completed , Failed , or Stopped ).
LastModifiedTime (datetime) --
Timestamp when the training job was last modified.
TrainingJobStatus (string) --
The status of the training job.
paginator = client.get_paginator('list_training_jobs_for_hyper_parameter_tuning_job')
Creates an iterator that will paginate through responses from SageMaker.Client.list_training_jobs_for_hyper_parameter_tuning_job().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
HyperParameterTuningJobName='string',
StatusEquals='InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
SortBy='Name'|'CreationTime'|'Status'|'FinalObjectiveMetricValue',
SortOrder='Ascending'|'Descending',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
[REQUIRED]
The name of the tuning job whose training jobs you want to list.
The field to sort results by. The default is Name .
If the value of this field is FinalObjectiveMetricValue , any training jobs that did not return an objective metric are not listed.
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'TrainingJobSummaries': [
{
'TrainingJobDefinitionName': 'string',
'TrainingJobName': 'string',
'TrainingJobArn': 'string',
'TuningJobName': 'string',
'CreationTime': datetime(2015, 1, 1),
'TrainingStartTime': datetime(2015, 1, 1),
'TrainingEndTime': datetime(2015, 1, 1),
'TrainingJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'TunedHyperParameters': {
'string': 'string'
},
'FailureReason': 'string',
'FinalHyperParameterTuningJobObjectiveMetric': {
'Type': 'Maximize'|'Minimize',
'MetricName': 'string',
'Value': ...
},
'ObjectiveStatus': 'Succeeded'|'Pending'|'Failed'
},
],
}
Response Structure
(dict) --
TrainingJobSummaries (list) --
A list of TrainingJobSummary objects that describe the training jobs that the ListTrainingJobsForHyperParameterTuningJob request returned.
(dict) --
Specifies summary information about a training job.
TrainingJobDefinitionName (string) --
The training job definition name.
TrainingJobName (string) --
The name of the training job.
TrainingJobArn (string) --
The Amazon Resource Name (ARN) of the training job.
TuningJobName (string) --
The HyperParameter tuning job that launched the training job.
CreationTime (datetime) --
The date and time that the training job was created.
TrainingStartTime (datetime) --
The date and time that the training job started.
TrainingEndTime (datetime) --
Specifies the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when Amazon SageMaker detects a job failure.
TrainingJobStatus (string) --
The status of the training job.
TunedHyperParameters (dict) --
A list of the hyperparameters for which you specified ranges to search.
FailureReason (string) --
The reason that the training job failed.
FinalHyperParameterTuningJobObjectiveMetric (dict) --
The FinalHyperParameterTuningJobObjectiveMetric object that specifies the value of the objective metric of the tuning job that launched this training job.
Type (string) --
Whether to minimize or maximize the objective metric. Valid values are Minimize and Maximize.
MetricName (string) --
The name of the objective metric.
Value (float) --
The value of the objective metric.
ObjectiveStatus (string) --
The status of the objective metric for the training job:
paginator = client.get_paginator('list_transform_jobs')
Creates an iterator that will paginate through responses from SageMaker.Client.list_transform_jobs().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
LastModifiedTimeAfter=datetime(2015, 1, 1),
LastModifiedTimeBefore=datetime(2015, 1, 1),
NameContains='string',
StatusEquals='InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
SortBy='Name'|'CreationTime'|'Status',
SortOrder='Ascending'|'Descending',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'TransformJobSummaries': [
{
'TransformJobName': 'string',
'TransformJobArn': 'string',
'CreationTime': datetime(2015, 1, 1),
'TransformEndTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'TransformJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'FailureReason': 'string'
},
],
}
Response Structure
(dict) --
TransformJobSummaries (list) --
An array of TransformJobSummary objects.
(dict) --
Provides a summary of a transform job. Multiple TransformJobSummary objects are returned as a list after in response to a ListTransformJobs call.
TransformJobName (string) --
The name of the transform job.
TransformJobArn (string) --
The Amazon Resource Name (ARN) of the transform job.
CreationTime (datetime) --
A timestamp that shows when the transform Job was created.
TransformEndTime (datetime) --
Indicates when the transform job ends on compute instances. For successful jobs and stopped jobs, this is the exact time recorded after the results are uploaded. For failed jobs, this is when Amazon SageMaker detected that the job failed.
LastModifiedTime (datetime) --
Indicates when the transform job was last modified.
TransformJobStatus (string) --
The status of the transform job.
FailureReason (string) --
If the transform job failed, the reason it failed.
paginator = client.get_paginator('list_trial_components')
Creates an iterator that will paginate through responses from SageMaker.Client.list_trial_components().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
ExperimentName='string',
TrialName='string',
SourceArn='string',
CreatedAfter=datetime(2015, 1, 1),
CreatedBefore=datetime(2015, 1, 1),
SortBy='Name'|'CreationTime',
SortOrder='Ascending'|'Descending',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'TrialComponentSummaries': [
{
'TrialComponentName': 'string',
'TrialComponentArn': 'string',
'DisplayName': 'string',
'TrialComponentSource': {
'SourceArn': 'string',
'SourceType': 'string'
},
'Status': {
'PrimaryStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'Message': 'string'
},
'StartTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1),
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'LastModifiedTime': datetime(2015, 1, 1),
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
}
},
],
}
Response Structure
(dict) --
TrialComponentSummaries (list) --
A list of the summaries of your trial components.
(dict) --
A summary of the properties of a trial component. To get all the properties, call the DescribeTrialComponent API and provide the TrialComponentName .
TrialComponentName (string) --
The name of the trial component.
TrialComponentArn (string) --
The ARN of the trial component.
DisplayName (string) --
The name of the component as displayed. If DisplayName isn't specified, TrialComponentName is displayed.
TrialComponentSource (dict) --
The Amazon Resource Name (ARN) and job type of the source of a trial component.
SourceArn (string) --
The source ARN.
SourceType (string) --
The source job type.
Status (dict) --
The status of the component. States include:
PrimaryStatus (string) --
The status of the trial component.
Message (string) --
If the component failed, a message describing why.
StartTime (datetime) --
When the component started.
EndTime (datetime) --
When the component ended.
CreationTime (datetime) --
When the component was created.
CreatedBy (dict) --
Who created the component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
LastModifiedTime (datetime) --
When the component was last modified.
LastModifiedBy (dict) --
Who last modified the component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
paginator = client.get_paginator('list_trials')
Creates an iterator that will paginate through responses from SageMaker.Client.list_trials().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
ExperimentName='string',
TrialComponentName='string',
CreatedAfter=datetime(2015, 1, 1),
CreatedBefore=datetime(2015, 1, 1),
SortBy='Name'|'CreationTime',
SortOrder='Ascending'|'Descending',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'TrialSummaries': [
{
'TrialArn': 'string',
'TrialName': 'string',
'DisplayName': 'string',
'TrialSource': {
'SourceArn': 'string',
'SourceType': 'string'
},
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1)
},
],
}
Response Structure
(dict) --
TrialSummaries (list) --
A list of the summaries of your trials.
(dict) --
A summary of the properties of a trial. To get the complete set of properties, call the DescribeTrial API and provide the TrialName .
TrialArn (string) --
The Amazon Resource Name (ARN) of the trial.
TrialName (string) --
The name of the trial.
DisplayName (string) --
The name of the trial as displayed. If DisplayName isn't specified, TrialName is displayed.
TrialSource (dict) --
The source of the trial.
SourceArn (string) --
The Amazon Resource Name (ARN) of the source.
SourceType (string) --
The source job type.
CreationTime (datetime) --
When the trial was created.
LastModifiedTime (datetime) --
When the trial was last modified.
paginator = client.get_paginator('list_user_profiles')
Creates an iterator that will paginate through responses from SageMaker.Client.list_user_profiles().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
SortOrder='Ascending'|'Descending',
SortBy='CreationTime'|'LastModifiedTime',
DomainIdEquals='string',
UserProfileNameContains='string',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'UserProfiles': [
{
'DomainId': 'string',
'UserProfileName': 'string',
'Status': 'Deleting'|'Failed'|'InService'|'Pending'|'Updating'|'Update_Failed'|'Delete_Failed',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1)
},
],
}
Response Structure
(dict) --
UserProfiles (list) --
The list of user profiles.
(dict) --
The user profile details.
DomainId (string) --
The domain ID.
UserProfileName (string) --
The user profile name.
Status (string) --
The status.
CreationTime (datetime) --
The creation time.
LastModifiedTime (datetime) --
The last modified time.
paginator = client.get_paginator('list_workforces')
Creates an iterator that will paginate through responses from SageMaker.Client.list_workforces().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
SortBy='Name'|'CreateDate',
SortOrder='Ascending'|'Descending',
NameContains='string',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'Workforces': [
{
'WorkforceName': 'string',
'WorkforceArn': 'string',
'LastUpdatedDate': datetime(2015, 1, 1),
'SourceIpConfig': {
'Cidrs': [
'string',
]
},
'SubDomain': 'string',
'CognitoConfig': {
'UserPool': 'string',
'ClientId': 'string'
},
'OidcConfig': {
'ClientId': 'string',
'Issuer': 'string',
'AuthorizationEndpoint': 'string',
'TokenEndpoint': 'string',
'UserInfoEndpoint': 'string',
'LogoutEndpoint': 'string',
'JwksUri': 'string'
},
'CreateDate': datetime(2015, 1, 1)
},
],
}
Response Structure
(dict) --
Workforces (list) --
A list containing information about your workforce.
(dict) --
A single private workforce, which is automatically created when you create your first private work team. You can create one private work force in each Amazon Web Services Region. By default, any workforce-related API operation used in a specific region will apply to the workforce created in that region. To learn how to create a private workforce, see Create a Private Workforce .
WorkforceName (string) --
The name of the private workforce.
WorkforceArn (string) --
The Amazon Resource Name (ARN) of the private workforce.
LastUpdatedDate (datetime) --
The most recent date that was used to successfully add one or more IP address ranges (CIDRs ) to a private workforce's allow list.
SourceIpConfig (dict) --
A list of one to ten IP address ranges (CIDRs ) to be added to the workforce allow list. By default, a workforce isn't restricted to specific IP addresses.
Cidrs (list) --
A list of one to ten Classless Inter-Domain Routing (CIDR) values.
Maximum: Ten CIDR values
Note
The following Length Constraints apply to individual CIDR values in the CIDR value list.
SubDomain (string) --
The subdomain for your OIDC Identity Provider.
CognitoConfig (dict) --
The configuration of an Amazon Cognito workforce. A single Cognito workforce is created using and corresponds to a single Amazon Cognito user pool .
UserPool (string) --
A user pool is a user directory in Amazon Cognito. With a user pool, your users can sign in to your web or mobile app through Amazon Cognito. Your users can also sign in through social identity providers like Google, Facebook, Amazon, or Apple, and through SAML identity providers.
ClientId (string) --
The client ID for your Amazon Cognito user pool.
OidcConfig (dict) --
The configuration of an OIDC Identity Provider (IdP) private workforce.
ClientId (string) --
The OIDC IdP client ID used to configure your private workforce.
Issuer (string) --
The OIDC IdP issuer used to configure your private workforce.
AuthorizationEndpoint (string) --
The OIDC IdP authorization endpoint used to configure your private workforce.
TokenEndpoint (string) --
The OIDC IdP token endpoint used to configure your private workforce.
UserInfoEndpoint (string) --
The OIDC IdP user information endpoint used to configure your private workforce.
LogoutEndpoint (string) --
The OIDC IdP logout endpoint used to configure your private workforce.
JwksUri (string) --
The OIDC IdP JSON Web Key Set (Jwks) URI used to configure your private workforce.
CreateDate (datetime) --
The date that the workforce is created.
paginator = client.get_paginator('list_workteams')
Creates an iterator that will paginate through responses from SageMaker.Client.list_workteams().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
SortBy='Name'|'CreateDate',
SortOrder='Ascending'|'Descending',
NameContains='string',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'Workteams': [
{
'WorkteamName': 'string',
'MemberDefinitions': [
{
'CognitoMemberDefinition': {
'UserPool': 'string',
'UserGroup': 'string',
'ClientId': 'string'
},
'OidcMemberDefinition': {
'Groups': [
'string',
]
}
},
],
'WorkteamArn': 'string',
'WorkforceArn': 'string',
'ProductListingIds': [
'string',
],
'Description': 'string',
'SubDomain': 'string',
'CreateDate': datetime(2015, 1, 1),
'LastUpdatedDate': datetime(2015, 1, 1),
'NotificationConfiguration': {
'NotificationTopicArn': 'string'
}
},
],
}
Response Structure
(dict) --
Workteams (list) --
An array of Workteam objects, each describing a work team.
(dict) --
Provides details about a labeling work team.
WorkteamName (string) --
The name of the work team.
MemberDefinitions (list) --
A list of MemberDefinition objects that contains objects that identify the workers that make up the work team.
Workforces can be created using Amazon Cognito or your own OIDC Identity Provider (IdP). For private workforces created using Amazon Cognito use CognitoMemberDefinition . For workforces created using your own OIDC identity provider (IdP) use OidcMemberDefinition .
(dict) --
Defines an Amazon Cognito or your own OIDC IdP user group that is part of a work team.
CognitoMemberDefinition (dict) --
The Amazon Cognito user group that is part of the work team.
UserPool (string) --
An identifier for a user pool. The user pool must be in the same region as the service that you are calling.
UserGroup (string) --
An identifier for a user group.
ClientId (string) --
An identifier for an application client. You must create the app client ID using Amazon Cognito.
OidcMemberDefinition (dict) --
A list user groups that exist in your OIDC Identity Provider (IdP). One to ten groups can be used to create a single private work team. When you add a user group to the list of Groups , you can add that user group to one or more private work teams. If you add a user group to a private work team, all workers in that user group are added to the work team.
Groups (list) --
A list of comma seperated strings that identifies user groups in your OIDC IdP. Each user group is made up of a group of private workers.
WorkteamArn (string) --
The Amazon Resource Name (ARN) that identifies the work team.
WorkforceArn (string) --
The Amazon Resource Name (ARN) of the workforce.
ProductListingIds (list) --
The Amazon Marketplace identifier for a vendor's work team.
Description (string) --
A description of the work team.
SubDomain (string) --
The URI of the labeling job's user interface. Workers open this URI to start labeling your data objects.
CreateDate (datetime) --
The date and time that the work team was created (timestamp).
LastUpdatedDate (datetime) --
The date and time that the work team was last updated (timestamp).
NotificationConfiguration (dict) --
Configures SNS notifications of available or expiring work items for work teams.
NotificationTopicArn (string) --
The ARN for the SNS topic to which notifications should be published.
paginator = client.get_paginator('search')
Creates an iterator that will paginate through responses from SageMaker.Client.search().
See also: AWS API Documentation
Request Syntax
response_iterator = paginator.paginate(
Resource='TrainingJob'|'Experiment'|'ExperimentTrial'|'ExperimentTrialComponent'|'Endpoint'|'ModelPackage'|'ModelPackageGroup'|'Pipeline'|'PipelineExecution'|'FeatureGroup',
SearchExpression={
'Filters': [
{
'Name': 'string',
'Operator': 'Equals'|'NotEquals'|'GreaterThan'|'GreaterThanOrEqualTo'|'LessThan'|'LessThanOrEqualTo'|'Contains'|'Exists'|'NotExists'|'In',
'Value': 'string'
},
],
'NestedFilters': [
{
'NestedPropertyName': 'string',
'Filters': [
{
'Name': 'string',
'Operator': 'Equals'|'NotEquals'|'GreaterThan'|'GreaterThanOrEqualTo'|'LessThan'|'LessThanOrEqualTo'|'Contains'|'Exists'|'NotExists'|'In',
'Value': 'string'
},
]
},
],
'SubExpressions': [
{'... recursive ...'},
],
'Operator': 'And'|'Or'
},
SortBy='string',
SortOrder='Ascending'|'Descending',
PaginationConfig={
'MaxItems': 123,
'PageSize': 123,
'StartingToken': 'string'
}
)
[REQUIRED]
The name of the Amazon SageMaker resource to search for.
A Boolean conditional statement. Resources must satisfy this condition to be included in search results. You must provide at least one subexpression, filter, or nested filter. The maximum number of recursive SubExpressions , NestedFilters , and Filters that can be included in a SearchExpression object is 50.
A list of filter objects.
A conditional statement for a search expression that includes a resource property, a Boolean operator, and a value. Resources that match the statement are returned in the results from the Search API.
If you specify a Value , but not an Operator , Amazon SageMaker uses the equals operator.
In search, there are several property types:
Metrics
To define a metric filter, enter a value using the form "Metrics.<name>" , where <name> is a metric name. For example, the following filter searches for training jobs with an "accuracy" metric greater than "0.9" :
{"Name": "Metrics.accuracy",
"Operator": "GreaterThan",
"Value": "0.9"
}
HyperParameters
To define a hyperparameter filter, enter a value with the form "HyperParameters.<name>" . Decimal hyperparameter values are treated as a decimal in a comparison if the specified Value is also a decimal value. If the specified Value is an integer, the decimal hyperparameter values are treated as integers. For example, the following filter is satisfied by training jobs with a "learning_rate" hyperparameter that is less than "0.5" :
{"Name": "HyperParameters.learning_rate",
"Operator": "LessThan",
"Value": "0.5"
}
Tags
To define a tag filter, enter a value with the form Tags.<key> .
A resource property name. For example, TrainingJobName . For valid property names, see SearchRecord . You must specify a valid property for the resource.
A Boolean binary operator that is used to evaluate the filter. The operator field contains one of the following values:
Equals
The value of Name equals Value .
NotEquals
The value of Name doesn't equal Value .
Exists
The Name property exists.
NotExists
The Name property does not exist.
GreaterThan
The value of Name is greater than Value . Not supported for text properties.
GreaterThanOrEqualTo
The value of Name is greater than or equal to Value . Not supported for text properties.
LessThan
The value of Name is less than Value . Not supported for text properties.
LessThanOrEqualTo
The value of Name is less than or equal to Value . Not supported for text properties.
In
The value of Name is one of the comma delimited strings in Value . Only supported for text properties.
Contains
The value of Name contains the string Value . Only supported for text properties.
A SearchExpression can include the Contains operator multiple times when the value of Name is one of the following:
A SearchExpression can include only one Contains operator for all other values of Name . In these cases, if you include multiple Contains operators in the SearchExpression , the result is the following error message: "'CONTAINS' operator usage limit of 1 exceeded. "
A value used with Name and Operator to determine which resources satisfy the filter's condition. For numerical properties, Value must be an integer or floating-point decimal. For timestamp properties, Value must be an ISO 8601 date-time string of the following format: YYYY-mm-dd'T'HH:MM:SS .
A list of nested filter objects.
A list of nested Filter objects. A resource must satisfy the conditions of all filters to be included in the results returned from the Search API.
For example, to filter on a training job's InputDataConfig property with a specific channel name and S3Uri prefix, define the following filters:
The name of the property to use in the nested filters. The value must match a listed property name, such as InputDataConfig .
A list of filters. Each filter acts on a property. Filters must contain at least one Filters value. For example, a NestedFilters call might include a filter on the PropertyName parameter of the InputDataConfig property: InputDataConfig.DataSource.S3DataSource.S3Uri .
A conditional statement for a search expression that includes a resource property, a Boolean operator, and a value. Resources that match the statement are returned in the results from the Search API.
If you specify a Value , but not an Operator , Amazon SageMaker uses the equals operator.
In search, there are several property types:
Metrics
To define a metric filter, enter a value using the form "Metrics.<name>" , where <name> is a metric name. For example, the following filter searches for training jobs with an "accuracy" metric greater than "0.9" :
{"Name": "Metrics.accuracy",
"Operator": "GreaterThan",
"Value": "0.9"
}
HyperParameters
To define a hyperparameter filter, enter a value with the form "HyperParameters.<name>" . Decimal hyperparameter values are treated as a decimal in a comparison if the specified Value is also a decimal value. If the specified Value is an integer, the decimal hyperparameter values are treated as integers. For example, the following filter is satisfied by training jobs with a "learning_rate" hyperparameter that is less than "0.5" :
{"Name": "HyperParameters.learning_rate",
"Operator": "LessThan",
"Value": "0.5"
}
Tags
To define a tag filter, enter a value with the form Tags.<key> .
A resource property name. For example, TrainingJobName . For valid property names, see SearchRecord . You must specify a valid property for the resource.
A Boolean binary operator that is used to evaluate the filter. The operator field contains one of the following values:
Equals
The value of Name equals Value .
NotEquals
The value of Name doesn't equal Value .
Exists
The Name property exists.
NotExists
The Name property does not exist.
GreaterThan
The value of Name is greater than Value . Not supported for text properties.
GreaterThanOrEqualTo
The value of Name is greater than or equal to Value . Not supported for text properties.
LessThan
The value of Name is less than Value . Not supported for text properties.
LessThanOrEqualTo
The value of Name is less than or equal to Value . Not supported for text properties.
In
The value of Name is one of the comma delimited strings in Value . Only supported for text properties.
Contains
The value of Name contains the string Value . Only supported for text properties.
A SearchExpression can include the Contains operator multiple times when the value of Name is one of the following:
A SearchExpression can include only one Contains operator for all other values of Name . In these cases, if you include multiple Contains operators in the SearchExpression , the result is the following error message: "'CONTAINS' operator usage limit of 1 exceeded. "
A value used with Name and Operator to determine which resources satisfy the filter's condition. For numerical properties, Value must be an integer or floating-point decimal. For timestamp properties, Value must be an ISO 8601 date-time string of the following format: YYYY-mm-dd'T'HH:MM:SS .
A list of search expression objects.
A multi-expression that searches for the specified resource or resources in a search. All resource objects that satisfy the expression's condition are included in the search results. You must specify at least one subexpression, filter, or nested filter. A SearchExpression can contain up to twenty elements.
A SearchExpression contains the following components:
A Boolean operator used to evaluate the search expression. If you want every conditional statement in all lists to be satisfied for the entire search expression to be true, specify And . If only a single conditional statement needs to be true for the entire search expression to be true, specify Or . The default value is And .
A dictionary that provides parameters to control pagination.
The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
The size of each page.
A token to specify where to start paginating. This is the NextToken from a previous response.
dict
Response Syntax
{
'Results': [
{
'TrainingJob': {
'TrainingJobName': 'string',
'TrainingJobArn': 'string',
'TuningJobArn': 'string',
'LabelingJobArn': 'string',
'AutoMLJobArn': 'string',
'ModelArtifacts': {
'S3ModelArtifacts': 'string'
},
'TrainingJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'SecondaryStatus': 'Starting'|'LaunchingMLInstances'|'PreparingTrainingStack'|'Downloading'|'DownloadingTrainingImage'|'Training'|'Uploading'|'Stopping'|'Stopped'|'MaxRuntimeExceeded'|'Completed'|'Failed'|'Interrupted'|'MaxWaitTimeExceeded'|'Updating'|'Restarting',
'FailureReason': 'string',
'HyperParameters': {
'string': 'string'
},
'AlgorithmSpecification': {
'TrainingImage': 'string',
'AlgorithmName': 'string',
'TrainingInputMode': 'Pipe'|'File',
'MetricDefinitions': [
{
'Name': 'string',
'Regex': 'string'
},
],
'EnableSageMakerMetricsTimeSeries': True|False
},
'RoleArn': 'string',
'InputDataConfig': [
{
'ChannelName': 'string',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'AttributeNames': [
'string',
]
},
'FileSystemDataSource': {
'FileSystemId': 'string',
'FileSystemAccessMode': 'rw'|'ro',
'FileSystemType': 'EFS'|'FSxLustre',
'DirectoryPath': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File',
'ShuffleConfig': {
'Seed': 123
}
},
],
'OutputDataConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string'
},
'ResourceConfig': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
},
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123
},
'CreationTime': datetime(2015, 1, 1),
'TrainingStartTime': datetime(2015, 1, 1),
'TrainingEndTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'SecondaryStatusTransitions': [
{
'Status': 'Starting'|'LaunchingMLInstances'|'PreparingTrainingStack'|'Downloading'|'DownloadingTrainingImage'|'Training'|'Uploading'|'Stopping'|'Stopped'|'MaxRuntimeExceeded'|'Completed'|'Failed'|'Interrupted'|'MaxWaitTimeExceeded'|'Updating'|'Restarting',
'StartTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1),
'StatusMessage': 'string'
},
],
'FinalMetricDataList': [
{
'MetricName': 'string',
'Value': ...,
'Timestamp': datetime(2015, 1, 1)
},
],
'EnableNetworkIsolation': True|False,
'EnableInterContainerTrafficEncryption': True|False,
'EnableManagedSpotTraining': True|False,
'CheckpointConfig': {
'S3Uri': 'string',
'LocalPath': 'string'
},
'TrainingTimeInSeconds': 123,
'BillableTimeInSeconds': 123,
'DebugHookConfig': {
'LocalPath': 'string',
'S3OutputPath': 'string',
'HookParameters': {
'string': 'string'
},
'CollectionConfigurations': [
{
'CollectionName': 'string',
'CollectionParameters': {
'string': 'string'
}
},
]
},
'ExperimentConfig': {
'ExperimentName': 'string',
'TrialName': 'string',
'TrialComponentDisplayName': 'string'
},
'DebugRuleConfigurations': [
{
'RuleConfigurationName': 'string',
'LocalPath': 'string',
'S3OutputPath': 'string',
'RuleEvaluatorImage': 'string',
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'RuleParameters': {
'string': 'string'
}
},
],
'TensorBoardOutputConfig': {
'LocalPath': 'string',
'S3OutputPath': 'string'
},
'DebugRuleEvaluationStatuses': [
{
'RuleConfigurationName': 'string',
'RuleEvaluationJobArn': 'string',
'RuleEvaluationStatus': 'InProgress'|'NoIssuesFound'|'IssuesFound'|'Error'|'Stopping'|'Stopped',
'StatusDetails': 'string',
'LastModifiedTime': datetime(2015, 1, 1)
},
],
'Environment': {
'string': 'string'
},
'RetryStrategy': {
'MaximumRetryAttempts': 123
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'Experiment': {
'ExperimentName': 'string',
'ExperimentArn': 'string',
'DisplayName': 'string',
'Source': {
'SourceArn': 'string',
'SourceType': 'string'
},
'Description': 'string',
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'LastModifiedTime': datetime(2015, 1, 1),
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'Trial': {
'TrialName': 'string',
'TrialArn': 'string',
'DisplayName': 'string',
'ExperimentName': 'string',
'Source': {
'SourceArn': 'string',
'SourceType': 'string'
},
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'LastModifiedTime': datetime(2015, 1, 1),
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'MetadataProperties': {
'CommitId': 'string',
'Repository': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string'
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
],
'TrialComponentSummaries': [
{
'TrialComponentName': 'string',
'TrialComponentArn': 'string',
'TrialComponentSource': {
'SourceArn': 'string',
'SourceType': 'string'
},
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
}
},
]
},
'TrialComponent': {
'TrialComponentName': 'string',
'DisplayName': 'string',
'TrialComponentArn': 'string',
'Source': {
'SourceArn': 'string',
'SourceType': 'string'
},
'Status': {
'PrimaryStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'Message': 'string'
},
'StartTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1),
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'LastModifiedTime': datetime(2015, 1, 1),
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'Parameters': {
'string': {
'StringValue': 'string',
'NumberValue': 123.0
}
},
'InputArtifacts': {
'string': {
'MediaType': 'string',
'Value': 'string'
}
},
'OutputArtifacts': {
'string': {
'MediaType': 'string',
'Value': 'string'
}
},
'Metrics': [
{
'MetricName': 'string',
'SourceArn': 'string',
'TimeStamp': datetime(2015, 1, 1),
'Max': 123.0,
'Min': 123.0,
'Last': 123.0,
'Count': 123,
'Avg': 123.0,
'StdDev': 123.0
},
],
'MetadataProperties': {
'CommitId': 'string',
'Repository': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string'
},
'SourceDetail': {
'SourceArn': 'string',
'TrainingJob': {
'TrainingJobName': 'string',
'TrainingJobArn': 'string',
'TuningJobArn': 'string',
'LabelingJobArn': 'string',
'AutoMLJobArn': 'string',
'ModelArtifacts': {
'S3ModelArtifacts': 'string'
},
'TrainingJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'SecondaryStatus': 'Starting'|'LaunchingMLInstances'|'PreparingTrainingStack'|'Downloading'|'DownloadingTrainingImage'|'Training'|'Uploading'|'Stopping'|'Stopped'|'MaxRuntimeExceeded'|'Completed'|'Failed'|'Interrupted'|'MaxWaitTimeExceeded'|'Updating'|'Restarting',
'FailureReason': 'string',
'HyperParameters': {
'string': 'string'
},
'AlgorithmSpecification': {
'TrainingImage': 'string',
'AlgorithmName': 'string',
'TrainingInputMode': 'Pipe'|'File',
'MetricDefinitions': [
{
'Name': 'string',
'Regex': 'string'
},
],
'EnableSageMakerMetricsTimeSeries': True|False
},
'RoleArn': 'string',
'InputDataConfig': [
{
'ChannelName': 'string',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'AttributeNames': [
'string',
]
},
'FileSystemDataSource': {
'FileSystemId': 'string',
'FileSystemAccessMode': 'rw'|'ro',
'FileSystemType': 'EFS'|'FSxLustre',
'DirectoryPath': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File',
'ShuffleConfig': {
'Seed': 123
}
},
],
'OutputDataConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string'
},
'ResourceConfig': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
},
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123
},
'CreationTime': datetime(2015, 1, 1),
'TrainingStartTime': datetime(2015, 1, 1),
'TrainingEndTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'SecondaryStatusTransitions': [
{
'Status': 'Starting'|'LaunchingMLInstances'|'PreparingTrainingStack'|'Downloading'|'DownloadingTrainingImage'|'Training'|'Uploading'|'Stopping'|'Stopped'|'MaxRuntimeExceeded'|'Completed'|'Failed'|'Interrupted'|'MaxWaitTimeExceeded'|'Updating'|'Restarting',
'StartTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1),
'StatusMessage': 'string'
},
],
'FinalMetricDataList': [
{
'MetricName': 'string',
'Value': ...,
'Timestamp': datetime(2015, 1, 1)
},
],
'EnableNetworkIsolation': True|False,
'EnableInterContainerTrafficEncryption': True|False,
'EnableManagedSpotTraining': True|False,
'CheckpointConfig': {
'S3Uri': 'string',
'LocalPath': 'string'
},
'TrainingTimeInSeconds': 123,
'BillableTimeInSeconds': 123,
'DebugHookConfig': {
'LocalPath': 'string',
'S3OutputPath': 'string',
'HookParameters': {
'string': 'string'
},
'CollectionConfigurations': [
{
'CollectionName': 'string',
'CollectionParameters': {
'string': 'string'
}
},
]
},
'ExperimentConfig': {
'ExperimentName': 'string',
'TrialName': 'string',
'TrialComponentDisplayName': 'string'
},
'DebugRuleConfigurations': [
{
'RuleConfigurationName': 'string',
'LocalPath': 'string',
'S3OutputPath': 'string',
'RuleEvaluatorImage': 'string',
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'RuleParameters': {
'string': 'string'
}
},
],
'TensorBoardOutputConfig': {
'LocalPath': 'string',
'S3OutputPath': 'string'
},
'DebugRuleEvaluationStatuses': [
{
'RuleConfigurationName': 'string',
'RuleEvaluationJobArn': 'string',
'RuleEvaluationStatus': 'InProgress'|'NoIssuesFound'|'IssuesFound'|'Error'|'Stopping'|'Stopped',
'StatusDetails': 'string',
'LastModifiedTime': datetime(2015, 1, 1)
},
],
'Environment': {
'string': 'string'
},
'RetryStrategy': {
'MaximumRetryAttempts': 123
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'ProcessingJob': {
'ProcessingInputs': [
{
'InputName': 'string',
'AppManaged': True|False,
'S3Input': {
'S3Uri': 'string',
'LocalPath': 'string',
'S3DataType': 'ManifestFile'|'S3Prefix',
'S3InputMode': 'Pipe'|'File',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'S3CompressionType': 'None'|'Gzip'
},
'DatasetDefinition': {
'AthenaDatasetDefinition': {
'Catalog': 'string',
'Database': 'string',
'QueryString': 'string',
'WorkGroup': 'string',
'OutputS3Uri': 'string',
'KmsKeyId': 'string',
'OutputFormat': 'PARQUET'|'ORC'|'AVRO'|'JSON'|'TEXTFILE',
'OutputCompression': 'GZIP'|'SNAPPY'|'ZLIB'
},
'RedshiftDatasetDefinition': {
'ClusterId': 'string',
'Database': 'string',
'DbUser': 'string',
'QueryString': 'string',
'ClusterRoleArn': 'string',
'OutputS3Uri': 'string',
'KmsKeyId': 'string',
'OutputFormat': 'PARQUET'|'CSV',
'OutputCompression': 'None'|'GZIP'|'BZIP2'|'ZSTD'|'SNAPPY'
},
'LocalPath': 'string',
'DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'InputMode': 'Pipe'|'File'
}
},
],
'ProcessingOutputConfig': {
'Outputs': [
{
'OutputName': 'string',
'S3Output': {
'S3Uri': 'string',
'LocalPath': 'string',
'S3UploadMode': 'Continuous'|'EndOfJob'
},
'FeatureStoreOutput': {
'FeatureGroupName': 'string'
},
'AppManaged': True|False
},
],
'KmsKeyId': 'string'
},
'ProcessingJobName': 'string',
'ProcessingResources': {
'ClusterConfig': {
'InstanceCount': 123,
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
}
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123
},
'AppSpecification': {
'ImageUri': 'string',
'ContainerEntrypoint': [
'string',
],
'ContainerArguments': [
'string',
]
},
'Environment': {
'string': 'string'
},
'NetworkConfig': {
'EnableInterContainerTrafficEncryption': True|False,
'EnableNetworkIsolation': True|False,
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
},
'RoleArn': 'string',
'ExperimentConfig': {
'ExperimentName': 'string',
'TrialName': 'string',
'TrialComponentDisplayName': 'string'
},
'ProcessingJobArn': 'string',
'ProcessingJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'ExitMessage': 'string',
'FailureReason': 'string',
'ProcessingEndTime': datetime(2015, 1, 1),
'ProcessingStartTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'CreationTime': datetime(2015, 1, 1),
'MonitoringScheduleArn': 'string',
'AutoMLJobArn': 'string',
'TrainingJobArn': 'string',
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'TransformJob': {
'TransformJobName': 'string',
'TransformJobArn': 'string',
'TransformJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'FailureReason': 'string',
'ModelName': 'string',
'MaxConcurrentTransforms': 123,
'ModelClientConfig': {
'InvocationsTimeoutInSeconds': 123,
'InvocationsMaxRetries': 123
},
'MaxPayloadInMB': 123,
'BatchStrategy': 'MultiRecord'|'SingleRecord',
'Environment': {
'string': 'string'
},
'TransformInput': {
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'SplitType': 'None'|'Line'|'RecordIO'|'TFRecord'
},
'TransformOutput': {
'S3OutputPath': 'string',
'Accept': 'string',
'AssembleWith': 'None'|'Line',
'KmsKeyId': 'string'
},
'TransformResources': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'InstanceCount': 123,
'VolumeKmsKeyId': 'string'
},
'CreationTime': datetime(2015, 1, 1),
'TransformStartTime': datetime(2015, 1, 1),
'TransformEndTime': datetime(2015, 1, 1),
'LabelingJobArn': 'string',
'AutoMLJobArn': 'string',
'DataProcessing': {
'InputFilter': 'string',
'OutputFilter': 'string',
'JoinSource': 'Input'|'None'
},
'ExperimentConfig': {
'ExperimentName': 'string',
'TrialName': 'string',
'TrialComponentDisplayName': 'string'
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
}
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
],
'Parents': [
{
'TrialName': 'string',
'ExperimentName': 'string'
},
]
},
'Endpoint': {
'EndpointName': 'string',
'EndpointArn': 'string',
'EndpointConfigName': 'string',
'ProductionVariants': [
{
'VariantName': 'string',
'DeployedImages': [
{
'SpecifiedImage': 'string',
'ResolvedImage': 'string',
'ResolutionTime': datetime(2015, 1, 1)
},
],
'CurrentWeight': ...,
'DesiredWeight': ...,
'CurrentInstanceCount': 123,
'DesiredInstanceCount': 123
},
],
'DataCaptureConfig': {
'EnableCapture': True|False,
'CaptureStatus': 'Started'|'Stopped',
'CurrentSamplingPercentage': 123,
'DestinationS3Uri': 'string',
'KmsKeyId': 'string'
},
'EndpointStatus': 'OutOfService'|'Creating'|'Updating'|'SystemUpdating'|'RollingBack'|'InService'|'Deleting'|'Failed',
'FailureReason': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'MonitoringSchedules': [
{
'MonitoringScheduleArn': 'string',
'MonitoringScheduleName': 'string',
'MonitoringScheduleStatus': 'Pending'|'Failed'|'Scheduled'|'Stopped',
'MonitoringType': 'DataQuality'|'ModelQuality'|'ModelBias'|'ModelExplainability',
'FailureReason': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'MonitoringScheduleConfig': {
'ScheduleConfig': {
'ScheduleExpression': 'string'
},
'MonitoringJobDefinition': {
'BaselineConfig': {
'BaseliningJobName': 'string',
'ConstraintsResource': {
'S3Uri': 'string'
},
'StatisticsResource': {
'S3Uri': 'string'
}
},
'MonitoringInputs': [
{
'EndpointInput': {
'EndpointName': 'string',
'LocalPath': 'string',
'S3InputMode': 'Pipe'|'File',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'FeaturesAttribute': 'string',
'InferenceAttribute': 'string',
'ProbabilityAttribute': 'string',
'ProbabilityThresholdAttribute': 123.0,
'StartTimeOffset': 'string',
'EndTimeOffset': 'string'
}
},
],
'MonitoringOutputConfig': {
'MonitoringOutputs': [
{
'S3Output': {
'S3Uri': 'string',
'LocalPath': 'string',
'S3UploadMode': 'Continuous'|'EndOfJob'
}
},
],
'KmsKeyId': 'string'
},
'MonitoringResources': {
'ClusterConfig': {
'InstanceCount': 123,
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
}
},
'MonitoringAppSpecification': {
'ImageUri': 'string',
'ContainerEntrypoint': [
'string',
],
'ContainerArguments': [
'string',
],
'RecordPreprocessorSourceUri': 'string',
'PostAnalyticsProcessorSourceUri': 'string'
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123
},
'Environment': {
'string': 'string'
},
'NetworkConfig': {
'EnableInterContainerTrafficEncryption': True|False,
'EnableNetworkIsolation': True|False,
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
},
'RoleArn': 'string'
},
'MonitoringJobDefinitionName': 'string',
'MonitoringType': 'DataQuality'|'ModelQuality'|'ModelBias'|'ModelExplainability'
},
'EndpointName': 'string',
'LastMonitoringExecutionSummary': {
'MonitoringScheduleName': 'string',
'ScheduledTime': datetime(2015, 1, 1),
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'MonitoringExecutionStatus': 'Pending'|'Completed'|'CompletedWithViolations'|'InProgress'|'Failed'|'Stopping'|'Stopped',
'ProcessingJobArn': 'string',
'EndpointName': 'string',
'FailureReason': 'string',
'MonitoringJobDefinitionName': 'string',
'MonitoringType': 'DataQuality'|'ModelQuality'|'ModelBias'|'ModelExplainability'
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
],
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'ModelPackage': {
'ModelPackageName': 'string',
'ModelPackageGroupName': 'string',
'ModelPackageVersion': 123,
'ModelPackageArn': 'string',
'ModelPackageDescription': 'string',
'CreationTime': datetime(2015, 1, 1),
'InferenceSpecification': {
'Containers': [
{
'ContainerHostname': 'string',
'Image': 'string',
'ImageDigest': 'string',
'ModelDataUrl': 'string',
'ProductId': 'string',
'Environment': {
'string': 'string'
}
},
],
'SupportedTransformInstanceTypes': [
'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
],
'SupportedRealtimeInferenceInstanceTypes': [
'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge',
],
'SupportedContentTypes': [
'string',
],
'SupportedResponseMIMETypes': [
'string',
]
},
'SourceAlgorithmSpecification': {
'SourceAlgorithms': [
{
'ModelDataUrl': 'string',
'AlgorithmName': 'string'
},
]
},
'ValidationSpecification': {
'ValidationRole': 'string',
'ValidationProfiles': [
{
'ProfileName': 'string',
'TransformJobDefinition': {
'MaxConcurrentTransforms': 123,
'MaxPayloadInMB': 123,
'BatchStrategy': 'MultiRecord'|'SingleRecord',
'Environment': {
'string': 'string'
},
'TransformInput': {
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'SplitType': 'None'|'Line'|'RecordIO'|'TFRecord'
},
'TransformOutput': {
'S3OutputPath': 'string',
'Accept': 'string',
'AssembleWith': 'None'|'Line',
'KmsKeyId': 'string'
},
'TransformResources': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'InstanceCount': 123,
'VolumeKmsKeyId': 'string'
}
}
},
]
},
'ModelPackageStatus': 'Pending'|'InProgress'|'Completed'|'Failed'|'Deleting',
'ModelPackageStatusDetails': {
'ValidationStatuses': [
{
'Name': 'string',
'Status': 'NotStarted'|'InProgress'|'Completed'|'Failed',
'FailureReason': 'string'
},
],
'ImageScanStatuses': [
{
'Name': 'string',
'Status': 'NotStarted'|'InProgress'|'Completed'|'Failed',
'FailureReason': 'string'
},
]
},
'CertifyForMarketplace': True|False,
'ModelApprovalStatus': 'Approved'|'Rejected'|'PendingManualApproval',
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'MetadataProperties': {
'CommitId': 'string',
'Repository': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string'
},
'ModelMetrics': {
'ModelQuality': {
'Statistics': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'Constraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'ModelDataQuality': {
'Statistics': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'Constraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'Bias': {
'Report': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'Explainability': {
'Report': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
}
},
'LastModifiedTime': datetime(2015, 1, 1),
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'ApprovalDescription': 'string',
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'ModelPackageGroup': {
'ModelPackageGroupName': 'string',
'ModelPackageGroupArn': 'string',
'ModelPackageGroupDescription': 'string',
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'ModelPackageGroupStatus': 'Pending'|'InProgress'|'Completed'|'Failed'|'Deleting'|'DeleteFailed',
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'Pipeline': {
'PipelineArn': 'string',
'PipelineName': 'string',
'PipelineDisplayName': 'string',
'PipelineDescription': 'string',
'RoleArn': 'string',
'PipelineStatus': 'Active',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'LastRunTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'PipelineExecution': {
'PipelineArn': 'string',
'PipelineExecutionArn': 'string',
'PipelineExecutionDisplayName': 'string',
'PipelineExecutionStatus': 'Executing'|'Stopping'|'Stopped'|'Failed'|'Succeeded',
'PipelineExecutionDescription': 'string',
'PipelineExperimentConfig': {
'ExperimentName': 'string',
'TrialName': 'string'
},
'FailureReason': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string'
},
'PipelineParameters': [
{
'Name': 'string',
'Value': 'string'
},
]
},
'FeatureGroup': {
'FeatureGroupArn': 'string',
'FeatureGroupName': 'string',
'RecordIdentifierFeatureName': 'string',
'EventTimeFeatureName': 'string',
'FeatureDefinitions': [
{
'FeatureName': 'string',
'FeatureType': 'Integral'|'Fractional'|'String'
},
],
'CreationTime': datetime(2015, 1, 1),
'OnlineStoreConfig': {
'SecurityConfig': {
'KmsKeyId': 'string'
},
'EnableOnlineStore': True|False
},
'OfflineStoreConfig': {
'S3StorageConfig': {
'S3Uri': 'string',
'KmsKeyId': 'string',
'ResolvedOutputS3Uri': 'string'
},
'DisableGlueTableCreation': True|False,
'DataCatalogConfig': {
'TableName': 'string',
'Catalog': 'string',
'Database': 'string'
}
},
'RoleArn': 'string',
'FeatureGroupStatus': 'Creating'|'Created'|'CreateFailed'|'Deleting'|'DeleteFailed',
'OfflineStoreStatus': {
'Status': 'Active'|'Blocked'|'Disabled',
'BlockedReason': 'string'
},
'FailureReason': 'string',
'Description': 'string',
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
}
},
],
}
Response Structure
(dict) --
Results (list) --
A list of SearchRecord objects.
(dict) --
A single resource returned as part of the Search API response.
TrainingJob (dict) --
The properties of a training job.
TrainingJobName (string) --
The name of the training job.
TrainingJobArn (string) --
The Amazon Resource Name (ARN) of the training job.
TuningJobArn (string) --
The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.
LabelingJobArn (string) --
The Amazon Resource Name (ARN) of the labeling job.
AutoMLJobArn (string) --
The Amazon Resource Name (ARN) of the job.
ModelArtifacts (dict) --
Information about the Amazon S3 location that is configured for storing model artifacts.
S3ModelArtifacts (string) --
The path of the S3 object that contains the model artifacts. For example, s3://bucket-name/keynameprefix/model.tar.gz .
TrainingJobStatus (string) --
The status of the training job.
Training job statuses are:
For more detailed information, see SecondaryStatus .
SecondaryStatus (string) --
Provides detailed information about the state of the training job. For detailed information about the secondary status of the training job, see StatusMessage under SecondaryStatusTransition .
Amazon SageMaker provides primary statuses and secondary statuses that apply to each of them:
InProgress
Starting - Starting the training job.
Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.
Training - Training is in progress.
Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.
Completed
Completed - The training job has completed.
Failed
Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse .
Stopped
MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.
Stopped - The training job has stopped.
Stopping
Stopping - Stopping the training job.
Warning
Valid values for SecondaryStatus are subject to change.
We no longer support the following secondary statuses:
FailureReason (string) --
If the training job failed, the reason it failed.
HyperParameters (dict) --
Algorithm-specific parameters.
AlgorithmSpecification (dict) --
Information about the algorithm used for training, and algorithm metadata.
TrainingImage (string) --
The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters . Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
AlgorithmName (string) --
The name of the algorithm resource to use for the training job. This must be an algorithm resource that you created or subscribe to on Amazon Web Services Marketplace. If you specify a value for this parameter, you can't specify a value for TrainingImage .
TrainingInputMode (string) --
The input mode that the algorithm supports. For the input modes that Amazon SageMaker algorithms support, see Algorithms . If an algorithm supports the File input mode, Amazon SageMaker downloads the training data from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an algorithm supports the Pipe input mode, Amazon SageMaker streams data directly from S3 to the container.
In File mode, make sure you provision ML storage volume with sufficient capacity to accommodate the data download from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container use ML storage volume to also store intermediate information, if any.
For distributed algorithms using File mode, training data is distributed uniformly, and your training duration is predictable if the input data objects size is approximately same. Amazon SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed where one host in a training cluster is overloaded, thus becoming bottleneck in training.
MetricDefinitions (list) --
A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. Amazon SageMaker publishes each metric to Amazon CloudWatch.
(dict) --
Specifies a metric that the training algorithm writes to stderr or stdout . Amazon SageMakerhyperparameter tuning captures all defined metrics. You specify one metric that a hyperparameter tuning job uses as its objective metric to choose the best training job.
Name (string) --
The name of the metric.
Regex (string) --
A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining Objective Metrics .
EnableSageMakerMetricsTimeSeries (boolean) --
To generate and save time-series metrics during training, set to true . The default is false and time-series metrics aren't generated except in the following cases:
RoleArn (string) --
The Amazon Web Services Identity and Access Management (IAM) role configured for the training job.
InputDataConfig (list) --
An array of Channel objects that describes each data input channel.
(dict) --
A channel is a named input source that training algorithms can consume.
ChannelName (string) --
The name of the channel.
DataSource (dict) --
The location of the channel data.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) --
If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects that match the specified key name prefix for model training.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for model training.
If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .
S3Uri (string) --
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
S3DataDistributionType (string) --
If you want Amazon SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .
If you want Amazon SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.
AttributeNames (list) --
A list of one or more attribute names to use that are found in a specified augmented manifest file.
FileSystemDataSource (dict) --
The file system that is associated with a channel.
FileSystemId (string) --
The file system id.
FileSystemAccessMode (string) --
The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.
FileSystemType (string) --
The file system type.
DirectoryPath (string) --
The full path to the directory to associate with the channel.
ContentType (string) --
The MIME type of the data.
CompressionType (string) --
If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
RecordWrapperType (string) --
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, Amazon SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO .
In File mode, leave this field unset or set it to None.
InputMode (string) --
(Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , Amazon SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
To use a model for incremental training, choose File input model.
ShuffleConfig (dict) --
A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
Seed (integer) --
Determines the shuffling order in ShuffleConfig value.
OutputDataConfig (dict) --
The S3 path where model artifacts that you configured when creating the job are stored. Amazon SageMaker creates subfolders for model artifacts.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
If you use a KMS key ID or an alias of your master key, the Amazon SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. Amazon SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig . If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
S3OutputPath (string) --
Identifies the S3 path where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
ResourceConfig (dict) --
Resources, including ML compute instances and ML storage volumes, that are configured for model training.
InstanceType (string) --
The ML compute instance type.
InstanceCount (integer) --
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB (integer) --
The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.
You must specify sufficient ML storage for your scenario.
Note
Amazon SageMaker supports only the General Purpose SSD (gp2) ML storage volume type.
Note
Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for training, Amazon SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can't request a VolumeSizeInGB greater than the total size of the local instance storage.
For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes .
VolumeKmsKeyId (string) --
The Amazon Web Services KMS key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be in any of the following formats:
VpcConfig (dict) --
A VpcConfig object that specifies the VPC that this training job has access to. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud .
SecurityGroupIds (list) --
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
Subnets (list) --
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
StoppingCondition (dict) --
Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.
MaxRuntimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can run. If the job does not complete during this time, Amazon SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
The default value is 1 day. The maximum value is 28 days.
MaxWaitTimeInSeconds (integer) --
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, Amazon SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
CreationTime (datetime) --
A timestamp that indicates when the training job was created.
TrainingStartTime (datetime) --
Indicates the time when the training job starts on training instances. You are billed for the time interval between this time and the value of TrainingEndTime . The start time in CloudWatch Logs might be later than this time. The difference is due to the time it takes to download the training data and to the size of the training container.
TrainingEndTime (datetime) --
Indicates the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when Amazon SageMaker detects a job failure.
LastModifiedTime (datetime) --
A timestamp that indicates when the status of the training job was last modified.
SecondaryStatusTransitions (list) --
A history of all of the secondary statuses that the training job has transitioned through.
(dict) --
An array element of DescribeTrainingJobResponse$SecondaryStatusTransitions . It provides additional details about a status that the training job has transitioned through. A training job can be in one of several states, for example, starting, downloading, training, or uploading. Within each state, there are a number of intermediate states. For example, within the starting state, Amazon SageMaker could be starting the training job or launching the ML instances. These transitional states are referred to as the job's secondary status.
Status (string) --
Contains a secondary status information from a training job.
Status might be one of the following secondary statuses:
InProgress
Starting - Starting the training job.
Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.
Training - Training is in progress.
Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.
Completed
Completed - The training job has completed.
Failed
Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse .
Stopped
MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.
Stopped - The training job has stopped.
Stopping
Stopping - Stopping the training job.
We no longer support the following secondary statuses:
StartTime (datetime) --
A timestamp that shows when the training job transitioned to the current secondary status state.
EndTime (datetime) --
A timestamp that shows when the training job transitioned out of this secondary status state into another secondary status state or when the training job has ended.
StatusMessage (string) --
A detailed description of the progress within a secondary status.
Amazon SageMaker provides secondary statuses and status messages that apply to each of them:
Starting
Starting the training job.
Launching requested ML instances.
Insufficient capacity error from EC2 while launching instances, retrying!
Launched instance was unhealthy, replacing it!
Preparing the instances for training.
Training
Downloading the training image.
Training image download completed. Training in progress.
Warning
Status messages are subject to change. Therefore, we recommend not including them in code that programmatically initiates actions. For examples, don't use status messages in if statements.
To have an overview of your training job's progress, view TrainingJobStatus and SecondaryStatus in DescribeTrainingJob , and StatusMessage together. For example, at the start of a training job, you might see the following:
FinalMetricDataList (list) --
A list of final metric values that are set when the training job completes. Used only if the training job was configured to use metrics.
(dict) --
The name, value, and date and time of a metric that was emitted to Amazon CloudWatch.
MetricName (string) --
The name of the metric.
Value (float) --
The value of the metric.
Timestamp (datetime) --
The date and time that the algorithm emitted the metric.
EnableNetworkIsolation (boolean) --
If the TrainingJob was created with network isolation, the value is set to true . If network isolation is enabled, nodes can't communicate beyond the VPC they run in.
EnableInterContainerTrafficEncryption (boolean) --
To encrypt all communications between ML compute instances in distributed training, choose True . Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.
EnableManagedSpotTraining (boolean) --
When true, enables managed spot training using Amazon EC2 Spot instances to run training jobs instead of on-demand instances. For more information, see Managed Spot Training .
CheckpointConfig (dict) --
Contains information about the output location for managed spot training checkpoint data.
S3Uri (string) --
Identifies the S3 path where you want Amazon SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix .
LocalPath (string) --
(Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/ .
TrainingTimeInSeconds (integer) --
The training time in seconds.
BillableTimeInSeconds (integer) --
The billable time in seconds.
DebugHookConfig (dict) --
Configuration information for the Debugger hook parameters, metric and tensor collections, and storage paths. To learn more about how to configure the DebugHookConfig parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
LocalPath (string) --
Path to local storage location for metrics and tensors. Defaults to /opt/ml/output/tensors/ .
S3OutputPath (string) --
Path to Amazon S3 storage location for metrics and tensors.
HookParameters (dict) --
Configuration information for the Debugger hook parameters.
CollectionConfigurations (list) --
Configuration information for Debugger tensor collections. To learn more about how to configure the CollectionConfiguration parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
(dict) --
Configuration information for the Debugger output tensor collections.
CollectionName (string) --
The name of the tensor collection. The name must be unique relative to other rule configuration names.
CollectionParameters (dict) --
Parameter values for the tensor collection. The allowed parameters are "name" , "include_regex" , "reduction_config" , "save_config" , "tensor_names" , and "save_histogram" .
ExperimentConfig (dict) --
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
ExperimentName (string) --
The name of an existing experiment to associate the trial component with.
TrialName (string) --
The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
TrialComponentDisplayName (string) --
The display name for the trial component. If this key isn't specified, the display name is the trial component name.
DebugRuleConfigurations (list) --
Information about the debug rule configuration.
(dict) --
Configuration information for SageMaker Debugger rules for debugging. To learn more about how to configure the DebugRuleConfiguration parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
RuleConfigurationName (string) --
The name of the rule configuration. It must be unique relative to other rule configuration names.
LocalPath (string) --
Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/ .
S3OutputPath (string) --
Path to Amazon S3 storage location for rules.
RuleEvaluatorImage (string) --
The Amazon Elastic Container (ECR) Image for the managed rule evaluation.
InstanceType (string) --
The instance type to deploy a Debugger custom rule for debugging a training job.
VolumeSizeInGB (integer) --
The size, in GB, of the ML storage volume attached to the processing instance.
RuleParameters (dict) --
Runtime configuration for rule container.
TensorBoardOutputConfig (dict) --
Configuration of storage locations for the Debugger TensorBoard output data.
LocalPath (string) --
Path to local storage location for tensorBoard output. Defaults to /opt/ml/output/tensorboard .
S3OutputPath (string) --
Path to Amazon S3 storage location for TensorBoard output.
DebugRuleEvaluationStatuses (list) --
Information about the evaluation status of the rules for the training job.
(dict) --
Information about the status of the rule evaluation.
RuleConfigurationName (string) --
The name of the rule configuration.
RuleEvaluationJobArn (string) --
The Amazon Resource Name (ARN) of the rule evaluation job.
RuleEvaluationStatus (string) --
Status of the rule evaluation.
StatusDetails (string) --
Details from the rule evaluation.
LastModifiedTime (datetime) --
Timestamp when the rule evaluation status was last modified.
Environment (dict) --
The environment variables to set in the Docker container.
RetryStrategy (dict) --
The number of times to retry the job when the job fails due to an InternalServerError .
MaximumRetryAttempts (integer) --
The number of times to retry the job. When the job is retried, it's SecondaryStatus is changed to STARTING .
Tags (list) --
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
Experiment (dict) --
The properties of an experiment.
ExperimentName (string) --
The name of the experiment.
ExperimentArn (string) --
The Amazon Resource Name (ARN) of the experiment.
DisplayName (string) --
The name of the experiment as displayed. If DisplayName isn't specified, ExperimentName is displayed.
Source (dict) --
The source of the experiment.
SourceArn (string) --
The Amazon Resource Name (ARN) of the source.
SourceType (string) --
The source type.
Description (string) --
The description of the experiment.
CreationTime (datetime) --
When the experiment was created.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
LastModifiedTime (datetime) --
When the experiment was last modified.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
Tags (list) --
The list of tags that are associated with the experiment. You can use Search API to search on the tags.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
Trial (dict) --
The properties of a trial.
TrialName (string) --
The name of the trial.
TrialArn (string) --
The Amazon Resource Name (ARN) of the trial.
DisplayName (string) --
The name of the trial as displayed. If DisplayName isn't specified, TrialName is displayed.
ExperimentName (string) --
The name of the experiment the trial is part of.
Source (dict) --
The source of the trial.
SourceArn (string) --
The Amazon Resource Name (ARN) of the source.
SourceType (string) --
The source job type.
CreationTime (datetime) --
When the trial was created.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
LastModifiedTime (datetime) --
Who last modified the trial.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
MetadataProperties (dict) --
Metadata properties of the tracking entity, trial, or trial component.
CommitId (string) --
The commit ID.
Repository (string) --
The repository.
GeneratedBy (string) --
The entity this entity was generated by.
ProjectId (string) --
The project ID.
Tags (list) --
The list of tags that are associated with the trial. You can use Search API to search on the tags.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
TrialComponentSummaries (list) --
A list of the components associated with the trial. For each component, a summary of the component's properties is included.
(dict) --
A short summary of a trial component.
TrialComponentName (string) --
The name of the trial component.
TrialComponentArn (string) --
The Amazon Resource Name (ARN) of the trial component.
TrialComponentSource (dict) --
The Amazon Resource Name (ARN) and job type of the source of a trial component.
SourceArn (string) --
The source ARN.
SourceType (string) --
The source job type.
CreationTime (datetime) --
When the component was created.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
TrialComponent (dict) --
The properties of a trial component.
TrialComponentName (string) --
The name of the trial component.
DisplayName (string) --
The name of the component as displayed. If DisplayName isn't specified, TrialComponentName is displayed.
TrialComponentArn (string) --
The Amazon Resource Name (ARN) of the trial component.
Source (dict) --
The Amazon Resource Name (ARN) and job type of the source of the component.
SourceArn (string) --
The source ARN.
SourceType (string) --
The source job type.
Status (dict) --
The status of the trial component.
PrimaryStatus (string) --
The status of the trial component.
Message (string) --
If the component failed, a message describing why.
StartTime (datetime) --
When the component started.
EndTime (datetime) --
When the component ended.
CreationTime (datetime) --
When the component was created.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
LastModifiedTime (datetime) --
When the component was last modified.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
Parameters (dict) --
The hyperparameters of the component.
(string) --
(dict) --
The value of a hyperparameter. Only one of NumberValue or StringValue can be specified.
This object is specified in the CreateTrialComponent request.
StringValue (string) --
The string value of a categorical hyperparameter. If you specify a value for this parameter, you can't specify the NumberValue parameter.
NumberValue (float) --
The numeric value of a numeric hyperparameter. If you specify a value for this parameter, you can't specify the StringValue parameter.
InputArtifacts (dict) --
The input artifacts of the component.
(string) --
(dict) --
Represents an input or output artifact of a trial component. You specify TrialComponentArtifact as part of the InputArtifacts and OutputArtifacts parameters in the CreateTrialComponent request.
Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and instance types. Examples of output artifacts are metrics, snapshots, logs, and images.
MediaType (string) --
The media type of the artifact, which indicates the type of data in the artifact file. The media type consists of a type and a subtype concatenated with a slash (/) character, for example, text/csv, image/jpeg, and s3/uri. The type specifies the category of the media. The subtype specifies the kind of data.
Value (string) --
The location of the artifact.
OutputArtifacts (dict) --
The output artifacts of the component.
(string) --
(dict) --
Represents an input or output artifact of a trial component. You specify TrialComponentArtifact as part of the InputArtifacts and OutputArtifacts parameters in the CreateTrialComponent request.
Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and instance types. Examples of output artifacts are metrics, snapshots, logs, and images.
MediaType (string) --
The media type of the artifact, which indicates the type of data in the artifact file. The media type consists of a type and a subtype concatenated with a slash (/) character, for example, text/csv, image/jpeg, and s3/uri. The type specifies the category of the media. The subtype specifies the kind of data.
Value (string) --
The location of the artifact.
Metrics (list) --
The metrics for the component.
(dict) --
A summary of the metrics of a trial component.
MetricName (string) --
The name of the metric.
SourceArn (string) --
The Amazon Resource Name (ARN) of the source.
TimeStamp (datetime) --
When the metric was last updated.
Max (float) --
The maximum value of the metric.
Min (float) --
The minimum value of the metric.
Last (float) --
The most recent value of the metric.
Count (integer) --
The number of samples used to generate the metric.
Avg (float) --
The average value of the metric.
StdDev (float) --
The standard deviation of the metric.
MetadataProperties (dict) --
Metadata properties of the tracking entity, trial, or trial component.
CommitId (string) --
The commit ID.
Repository (string) --
The repository.
GeneratedBy (string) --
The entity this entity was generated by.
ProjectId (string) --
The project ID.
SourceDetail (dict) --
Details of the source of the component.
SourceArn (string) --
The Amazon Resource Name (ARN) of the source.
TrainingJob (dict) --
Information about a training job that's the source of a trial component.
TrainingJobName (string) --
The name of the training job.
TrainingJobArn (string) --
The Amazon Resource Name (ARN) of the training job.
TuningJobArn (string) --
The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.
LabelingJobArn (string) --
The Amazon Resource Name (ARN) of the labeling job.
AutoMLJobArn (string) --
The Amazon Resource Name (ARN) of the job.
ModelArtifacts (dict) --
Information about the Amazon S3 location that is configured for storing model artifacts.
S3ModelArtifacts (string) --
The path of the S3 object that contains the model artifacts. For example, s3://bucket-name/keynameprefix/model.tar.gz .
TrainingJobStatus (string) --
The status of the training job.
Training job statuses are:
For more detailed information, see SecondaryStatus .
SecondaryStatus (string) --
Provides detailed information about the state of the training job. For detailed information about the secondary status of the training job, see StatusMessage under SecondaryStatusTransition .
Amazon SageMaker provides primary statuses and secondary statuses that apply to each of them:
InProgress
Starting - Starting the training job.
Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.
Training - Training is in progress.
Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.
Completed
Completed - The training job has completed.
Failed
Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse .
Stopped
MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.
Stopped - The training job has stopped.
Stopping
Stopping - Stopping the training job.
Warning
Valid values for SecondaryStatus are subject to change.
We no longer support the following secondary statuses:
FailureReason (string) --
If the training job failed, the reason it failed.
HyperParameters (dict) --
Algorithm-specific parameters.
AlgorithmSpecification (dict) --
Information about the algorithm used for training, and algorithm metadata.
TrainingImage (string) --
The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters . Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
AlgorithmName (string) --
The name of the algorithm resource to use for the training job. This must be an algorithm resource that you created or subscribe to on Amazon Web Services Marketplace. If you specify a value for this parameter, you can't specify a value for TrainingImage .
TrainingInputMode (string) --
The input mode that the algorithm supports. For the input modes that Amazon SageMaker algorithms support, see Algorithms . If an algorithm supports the File input mode, Amazon SageMaker downloads the training data from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an algorithm supports the Pipe input mode, Amazon SageMaker streams data directly from S3 to the container.
In File mode, make sure you provision ML storage volume with sufficient capacity to accommodate the data download from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container use ML storage volume to also store intermediate information, if any.
For distributed algorithms using File mode, training data is distributed uniformly, and your training duration is predictable if the input data objects size is approximately same. Amazon SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed where one host in a training cluster is overloaded, thus becoming bottleneck in training.
MetricDefinitions (list) --
A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. Amazon SageMaker publishes each metric to Amazon CloudWatch.
(dict) --
Specifies a metric that the training algorithm writes to stderr or stdout . Amazon SageMakerhyperparameter tuning captures all defined metrics. You specify one metric that a hyperparameter tuning job uses as its objective metric to choose the best training job.
Name (string) --
The name of the metric.
Regex (string) --
A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining Objective Metrics .
EnableSageMakerMetricsTimeSeries (boolean) --
To generate and save time-series metrics during training, set to true . The default is false and time-series metrics aren't generated except in the following cases:
RoleArn (string) --
The Amazon Web Services Identity and Access Management (IAM) role configured for the training job.
InputDataConfig (list) --
An array of Channel objects that describes each data input channel.
(dict) --
A channel is a named input source that training algorithms can consume.
ChannelName (string) --
The name of the channel.
DataSource (dict) --
The location of the channel data.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) --
If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects that match the specified key name prefix for model training.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for model training.
If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .
S3Uri (string) --
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
S3DataDistributionType (string) --
If you want Amazon SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .
If you want Amazon SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.
AttributeNames (list) --
A list of one or more attribute names to use that are found in a specified augmented manifest file.
FileSystemDataSource (dict) --
The file system that is associated with a channel.
FileSystemId (string) --
The file system id.
FileSystemAccessMode (string) --
The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.
FileSystemType (string) --
The file system type.
DirectoryPath (string) --
The full path to the directory to associate with the channel.
ContentType (string) --
The MIME type of the data.
CompressionType (string) --
If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
RecordWrapperType (string) --
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, Amazon SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO .
In File mode, leave this field unset or set it to None.
InputMode (string) --
(Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , Amazon SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
To use a model for incremental training, choose File input model.
ShuffleConfig (dict) --
A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
Seed (integer) --
Determines the shuffling order in ShuffleConfig value.
OutputDataConfig (dict) --
The S3 path where model artifacts that you configured when creating the job are stored. Amazon SageMaker creates subfolders for model artifacts.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
If you use a KMS key ID or an alias of your master key, the Amazon SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. Amazon SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig . If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
S3OutputPath (string) --
Identifies the S3 path where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
ResourceConfig (dict) --
Resources, including ML compute instances and ML storage volumes, that are configured for model training.
InstanceType (string) --
The ML compute instance type.
InstanceCount (integer) --
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB (integer) --
The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.
You must specify sufficient ML storage for your scenario.
Note
Amazon SageMaker supports only the General Purpose SSD (gp2) ML storage volume type.
Note
Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for training, Amazon SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can't request a VolumeSizeInGB greater than the total size of the local instance storage.
For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes .
VolumeKmsKeyId (string) --
The Amazon Web Services KMS key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be in any of the following formats:
VpcConfig (dict) --
A VpcConfig object that specifies the VPC that this training job has access to. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud .
SecurityGroupIds (list) --
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
Subnets (list) --
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
StoppingCondition (dict) --
Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.
MaxRuntimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can run. If the job does not complete during this time, Amazon SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
The default value is 1 day. The maximum value is 28 days.
MaxWaitTimeInSeconds (integer) --
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, Amazon SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
CreationTime (datetime) --
A timestamp that indicates when the training job was created.
TrainingStartTime (datetime) --
Indicates the time when the training job starts on training instances. You are billed for the time interval between this time and the value of TrainingEndTime . The start time in CloudWatch Logs might be later than this time. The difference is due to the time it takes to download the training data and to the size of the training container.
TrainingEndTime (datetime) --
Indicates the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when Amazon SageMaker detects a job failure.
LastModifiedTime (datetime) --
A timestamp that indicates when the status of the training job was last modified.
SecondaryStatusTransitions (list) --
A history of all of the secondary statuses that the training job has transitioned through.
(dict) --
An array element of DescribeTrainingJobResponse$SecondaryStatusTransitions . It provides additional details about a status that the training job has transitioned through. A training job can be in one of several states, for example, starting, downloading, training, or uploading. Within each state, there are a number of intermediate states. For example, within the starting state, Amazon SageMaker could be starting the training job or launching the ML instances. These transitional states are referred to as the job's secondary status.
Status (string) --
Contains a secondary status information from a training job.
Status might be one of the following secondary statuses:
InProgress
Starting - Starting the training job.
Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.
Training - Training is in progress.
Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.
Completed
Completed - The training job has completed.
Failed
Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse .
Stopped
MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.
Stopped - The training job has stopped.
Stopping
Stopping - Stopping the training job.
We no longer support the following secondary statuses:
StartTime (datetime) --
A timestamp that shows when the training job transitioned to the current secondary status state.
EndTime (datetime) --
A timestamp that shows when the training job transitioned out of this secondary status state into another secondary status state or when the training job has ended.
StatusMessage (string) --
A detailed description of the progress within a secondary status.
Amazon SageMaker provides secondary statuses and status messages that apply to each of them:
Starting
Starting the training job.
Launching requested ML instances.
Insufficient capacity error from EC2 while launching instances, retrying!
Launched instance was unhealthy, replacing it!
Preparing the instances for training.
Training
Downloading the training image.
Training image download completed. Training in progress.
Warning
Status messages are subject to change. Therefore, we recommend not including them in code that programmatically initiates actions. For examples, don't use status messages in if statements.
To have an overview of your training job's progress, view TrainingJobStatus and SecondaryStatus in DescribeTrainingJob , and StatusMessage together. For example, at the start of a training job, you might see the following:
FinalMetricDataList (list) --
A list of final metric values that are set when the training job completes. Used only if the training job was configured to use metrics.
(dict) --
The name, value, and date and time of a metric that was emitted to Amazon CloudWatch.
MetricName (string) --
The name of the metric.
Value (float) --
The value of the metric.
Timestamp (datetime) --
The date and time that the algorithm emitted the metric.
EnableNetworkIsolation (boolean) --
If the TrainingJob was created with network isolation, the value is set to true . If network isolation is enabled, nodes can't communicate beyond the VPC they run in.
EnableInterContainerTrafficEncryption (boolean) --
To encrypt all communications between ML compute instances in distributed training, choose True . Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.
EnableManagedSpotTraining (boolean) --
When true, enables managed spot training using Amazon EC2 Spot instances to run training jobs instead of on-demand instances. For more information, see Managed Spot Training .
CheckpointConfig (dict) --
Contains information about the output location for managed spot training checkpoint data.
S3Uri (string) --
Identifies the S3 path where you want Amazon SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix .
LocalPath (string) --
(Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/ .
TrainingTimeInSeconds (integer) --
The training time in seconds.
BillableTimeInSeconds (integer) --
The billable time in seconds.
DebugHookConfig (dict) --
Configuration information for the Debugger hook parameters, metric and tensor collections, and storage paths. To learn more about how to configure the DebugHookConfig parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
LocalPath (string) --
Path to local storage location for metrics and tensors. Defaults to /opt/ml/output/tensors/ .
S3OutputPath (string) --
Path to Amazon S3 storage location for metrics and tensors.
HookParameters (dict) --
Configuration information for the Debugger hook parameters.
CollectionConfigurations (list) --
Configuration information for Debugger tensor collections. To learn more about how to configure the CollectionConfiguration parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
(dict) --
Configuration information for the Debugger output tensor collections.
CollectionName (string) --
The name of the tensor collection. The name must be unique relative to other rule configuration names.
CollectionParameters (dict) --
Parameter values for the tensor collection. The allowed parameters are "name" , "include_regex" , "reduction_config" , "save_config" , "tensor_names" , and "save_histogram" .
ExperimentConfig (dict) --
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
ExperimentName (string) --
The name of an existing experiment to associate the trial component with.
TrialName (string) --
The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
TrialComponentDisplayName (string) --
The display name for the trial component. If this key isn't specified, the display name is the trial component name.
DebugRuleConfigurations (list) --
Information about the debug rule configuration.
(dict) --
Configuration information for SageMaker Debugger rules for debugging. To learn more about how to configure the DebugRuleConfiguration parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
RuleConfigurationName (string) --
The name of the rule configuration. It must be unique relative to other rule configuration names.
LocalPath (string) --
Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/ .
S3OutputPath (string) --
Path to Amazon S3 storage location for rules.
RuleEvaluatorImage (string) --
The Amazon Elastic Container (ECR) Image for the managed rule evaluation.
InstanceType (string) --
The instance type to deploy a Debugger custom rule for debugging a training job.
VolumeSizeInGB (integer) --
The size, in GB, of the ML storage volume attached to the processing instance.
RuleParameters (dict) --
Runtime configuration for rule container.
TensorBoardOutputConfig (dict) --
Configuration of storage locations for the Debugger TensorBoard output data.
LocalPath (string) --
Path to local storage location for tensorBoard output. Defaults to /opt/ml/output/tensorboard .
S3OutputPath (string) --
Path to Amazon S3 storage location for TensorBoard output.
DebugRuleEvaluationStatuses (list) --
Information about the evaluation status of the rules for the training job.
(dict) --
Information about the status of the rule evaluation.
RuleConfigurationName (string) --
The name of the rule configuration.
RuleEvaluationJobArn (string) --
The Amazon Resource Name (ARN) of the rule evaluation job.
RuleEvaluationStatus (string) --
Status of the rule evaluation.
StatusDetails (string) --
Details from the rule evaluation.
LastModifiedTime (datetime) --
Timestamp when the rule evaluation status was last modified.
Environment (dict) --
The environment variables to set in the Docker container.
RetryStrategy (dict) --
The number of times to retry the job when the job fails due to an InternalServerError .
MaximumRetryAttempts (integer) --
The number of times to retry the job. When the job is retried, it's SecondaryStatus is changed to STARTING .
Tags (list) --
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
ProcessingJob (dict) --
Information about a processing job that's the source of a trial component.
ProcessingInputs (list) --
List of input configurations for the processing job.
(dict) --
The inputs for a processing job. The processing input must specify exactly one of either S3Input or DatasetDefinition types.
InputName (string) --
The name for the processing job input.
AppManaged (boolean) --
When True , input operations such as data download are managed natively by the processing job application. When False (default), input operations are managed by Amazon SageMaker.
S3Input (dict) --
Configuration for downloading input data from Amazon S3 into the processing container.
S3Uri (string) --
The URI of the Amazon S3 prefix Amazon SageMaker downloads data required to run a processing job.
LocalPath (string) --
The local path in your container where you want Amazon SageMaker to write input data to. LocalPath is an absolute path to the input data and must begin with /opt/ml/processing/ . LocalPath is a required parameter when AppManaged is False (default).
S3DataType (string) --
Whether you use an S3Prefix or a ManifestFile for the data type. If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for the processing job. If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for the processing job.
S3InputMode (string) --
Whether to use File or Pipe input mode. In File mode, Amazon SageMaker copies the data from the input source onto the local ML storage volume before starting your processing container. This is the most commonly used input mode. In Pipe mode, Amazon SageMaker streams input data from the source directly to your processing container into named pipes without using the ML storage volume.
S3DataDistributionType (string) --
Whether to distribute the data from Amazon S3 to all processing instances with FullyReplicated , or whether the data from Amazon S3 is shared by Amazon S3 key, downloading one shard of data to each processing instance.
S3CompressionType (string) --
Whether to GZIP-decompress the data in Amazon S3 as it is streamed into the processing container. Gzip can only be used when Pipe mode is specified as the S3InputMode . In Pipe mode, Amazon SageMaker streams input data from the source directly to your container without using the EBS volume.
DatasetDefinition (dict) --
Configuration for a Dataset Definition input.
AthenaDatasetDefinition (dict) --
Configuration for Athena Dataset Definition input.
Catalog (string) --
The name of the data catalog used in Athena query execution.
Database (string) --
The name of the database used in the Athena query execution.
QueryString (string) --
The SQL query statements, to be executed.
WorkGroup (string) --
The name of the workgroup in which the Athena query is being started.
OutputS3Uri (string) --
The location in Amazon S3 where Athena query results are stored.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data generated from an Athena query execution.
OutputFormat (string) --
The data storage format for Athena query results.
OutputCompression (string) --
The compression used for Athena query results.
RedshiftDatasetDefinition (dict) --
Configuration for Redshift Dataset Definition input.
ClusterId (string) --
The Redshift cluster Identifier.
Database (string) --
The name of the Redshift database used in Redshift query execution.
DbUser (string) --
The database user name used in Redshift query execution.
QueryString (string) --
The SQL query statements to be executed.
ClusterRoleArn (string) --
The IAM role attached to your Redshift cluster that Amazon SageMaker uses to generate datasets.
OutputS3Uri (string) --
The location in Amazon S3 where the Redshift query results are stored.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data from a Redshift execution.
OutputFormat (string) --
The data storage format for Redshift query results.
OutputCompression (string) --
The compression used for Redshift query results.
LocalPath (string) --
The local path where you want Amazon SageMaker to download the Dataset Definition inputs to run a processing job. LocalPath is an absolute path to the input data. This is a required parameter when AppManaged is False (default).
DataDistributionType (string) --
Whether the generated dataset is FullyReplicated or ShardedByS3Key (default).
InputMode (string) --
Whether to use File or Pipe input mode. In File (default) mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode. In Pipe mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume.
ProcessingOutputConfig (dict) --
Configuration for uploading output from the processing container.
Outputs (list) --
An array of outputs configuring the data to upload from the processing container.
(dict) --
Describes the results of a processing job. The processing output must specify exactly one of either S3Output or FeatureStoreOutput types.
OutputName (string) --
The name for the processing job output.
S3Output (dict) --
Configuration for processing job outputs in Amazon S3.
S3Uri (string) --
A URI that identifies the Amazon S3 bucket where you want Amazon SageMaker to save the results of a processing job.
LocalPath (string) --
The local path of a directory where you want Amazon SageMaker to upload its contents to Amazon S3. LocalPath is an absolute path to a directory containing output files. This directory will be created by the platform and exist when your container's entrypoint is invoked.
S3UploadMode (string) --
Whether to upload the results of the processing job continuously or after the job completes.
FeatureStoreOutput (dict) --
Configuration for processing job outputs in Amazon SageMaker Feature Store. This processing output type is only supported when AppManaged is specified.
FeatureGroupName (string) --
The name of the Amazon SageMaker FeatureGroup to use as the destination for processing job output. Note that your processing script is responsible for putting records into your Feature Store.
AppManaged (boolean) --
When True , output operations such as data upload are managed natively by the processing job application. When False (default), output operations are managed by Amazon SageMaker.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the processing job output. KmsKeyId can be an ID of a KMS key, ARN of a KMS key, alias of a KMS key, or alias of a KMS key. The KmsKeyId is applied to all outputs.
ProcessingJobName (string) --
The name of the processing job.
ProcessingResources (dict) --
Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job. In distributed training, you specify more than one instance.
ClusterConfig (dict) --
The configuration for the resources in a cluster used to run the processing job.
InstanceCount (integer) --
The number of ML compute instances to use in the processing job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
InstanceType (string) --
The ML compute instance type for the processing job.
VolumeSizeInGB (integer) --
The size of the ML storage volume in gigabytes that you want to provision. You must specify sufficient ML storage for your scenario.
Note
Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for processing, Amazon SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can't request a VolumeSizeInGB greater than the total size of the local instance storage.
For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes .
VolumeKmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the processing job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
StoppingCondition (dict) --
Configures conditions under which the processing job should be stopped, such as how long the processing job has been running. After the condition is met, the processing job is stopped.
MaxRuntimeInSeconds (integer) --
Specifies the maximum runtime in seconds.
AppSpecification (dict) --
Configuration to run a processing job in a specified container image.
ImageUri (string) --
The container image to be run by the processing job.
ContainerEntrypoint (list) --
The entrypoint for a container used to run a processing job.
ContainerArguments (list) --
The arguments for a container used to run a processing job.
Environment (dict) --
Sets the environment variables in the Docker container.
NetworkConfig (dict) --
Networking options for a job, such as network traffic encryption between containers, whether to allow inbound and outbound network calls to and from containers, and the VPC subnets and security groups to use for VPC-enabled jobs.
EnableInterContainerTrafficEncryption (boolean) --
Whether to encrypt all communications between distributed processing jobs. Choose True to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer.
EnableNetworkIsolation (boolean) --
Whether to allow inbound and outbound network calls to and from the containers used for the processing job.
VpcConfig (dict) --
Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud .
SecurityGroupIds (list) --
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
Subnets (list) --
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
RoleArn (string) --
The ARN of the role used to create the processing job.
ExperimentConfig (dict) --
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
ExperimentName (string) --
The name of an existing experiment to associate the trial component with.
TrialName (string) --
The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
TrialComponentDisplayName (string) --
The display name for the trial component. If this key isn't specified, the display name is the trial component name.
ProcessingJobArn (string) --
The ARN of the processing job.
ProcessingJobStatus (string) --
The status of the processing job.
ExitMessage (string) --
A string, up to one KB in size, that contains metadata from the processing container when the processing job exits.
FailureReason (string) --
A string, up to one KB in size, that contains the reason a processing job failed, if it failed.
ProcessingEndTime (datetime) --
The time that the processing job ended.
ProcessingStartTime (datetime) --
The time that the processing job started.
LastModifiedTime (datetime) --
The time the processing job was last modified.
CreationTime (datetime) --
The time the processing job was created.
MonitoringScheduleArn (string) --
The ARN of a monitoring schedule for an endpoint associated with this processing job.
AutoMLJobArn (string) --
The Amazon Resource Name (ARN) of the AutoML job associated with this processing job.
TrainingJobArn (string) --
The ARN of the training job associated with this processing job.
Tags (list) --
An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
TransformJob (dict) --
Information about a transform job that's the source of a trial component.
TransformJobName (string) --
The name of the transform job.
TransformJobArn (string) --
The Amazon Resource Name (ARN) of the transform job.
TransformJobStatus (string) --
The status of the transform job.
Transform job statuses are:
FailureReason (string) --
If the transform job failed, the reason it failed.
ModelName (string) --
The name of the model associated with the transform job.
MaxConcurrentTransforms (integer) --
The maximum number of parallel requests that can be sent to each instance in a transform job. If MaxConcurrentTransforms is set to 0 or left unset, SageMaker checks the optional execution-parameters to determine the settings for your chosen algorithm. If the execution-parameters endpoint is not enabled, the default value is 1. For built-in algorithms, you don't need to set a value for MaxConcurrentTransforms .
ModelClientConfig (dict) --
Configures the timeout and maximum number of retries for processing a transform job invocation.
InvocationsTimeoutInSeconds (integer) --
The timeout value in seconds for an invocation request.
InvocationsMaxRetries (integer) --
The maximum number of retries when invocation requests are failing.
MaxPayloadInMB (integer) --
The maximum allowed size of the payload, in MB. A payload is the data portion of a record (without metadata). The value in MaxPayloadInMB must be greater than, or equal to, the size of a single record. To estimate the size of a record in MB, divide the size of your dataset by the number of records. To ensure that the records fit within the maximum payload size, we recommend using a slightly larger value. The default value is 6 MB. For cases where the payload might be arbitrarily large and is transmitted using HTTP chunked encoding, set the value to 0. This feature works only in supported algorithms. Currently, SageMaker built-in algorithms do not support HTTP chunked encoding.
BatchStrategy (string) --
Specifies the number of records to include in a mini-batch for an HTTP inference request. A record is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record.
Environment (dict) --
The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
TransformInput (dict) --
Describes the input source of a transform job and the way the transform job consumes it.
DataSource (dict) --
Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) --
If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.
The following values are compatible: ManifestFile , S3Prefix
The following value is not compatible: AugmentedManifestFile
S3Uri (string) --
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
ContentType (string) --
The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
CompressionType (string) --
If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None .
SplitType (string) --
The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None , which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:
When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord , Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord , Amazon SageMaker sends individual records in each request.
Note
Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord . Padding is not removed if the value of BatchStrategy is set to MultiRecord .
For more information about RecordIO , see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord , see Consuming TFRecord data in the TensorFlow documentation.
TransformOutput (dict) --
Describes the results of a transform job.
S3OutputPath (string) --
The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, s3://bucket-name/key-name-prefix .
For every S3 object used as input for the transform job, batch transform stores the transformed data with an .``out`` suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at s3://bucket-name/input-name-prefix/dataset01/data.csv , batch transform stores the transformed data at s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out . Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .``out`` file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.
Accept (string) --
The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
AssembleWith (string) --
Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify None . To add a newline character at the end of every transformed record, specify Line .
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
TransformResources (dict) --
Describes the resources, including ML instance types and ML instance count, to use for transform job.
InstanceType (string) --
The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or ml.m5.large instance types.
InstanceCount (integer) --
The number of ML compute instances to use in the transform job. For distributed transform jobs, specify a value greater than 1. The default value is 1 .
VolumeKmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be any of the following formats:
CreationTime (datetime) --
A timestamp that shows when the transform Job was created.
TransformStartTime (datetime) --
Indicates when the transform job starts on ML instances. You are billed for the time interval between this time and the value of TransformEndTime .
TransformEndTime (datetime) --
Indicates when the transform job has been completed, or has stopped or failed. You are billed for the time interval between this time and the value of TransformStartTime .
LabelingJobArn (string) --
The Amazon Resource Name (ARN) of the labeling job that created the transform job.
AutoMLJobArn (string) --
The Amazon Resource Name (ARN) of the AutoML job that created the transform job.
DataProcessing (dict) --
The data structure used to specify the data to be used for inference in a batch transform job and to associate the data that is relevant to the prediction results in the output. The input filter provided allows you to exclude input data that is not needed for inference in a batch transform job. The output filter provided allows you to include input data relevant to interpreting the predictions in the output from the job. For more information, see Associate Prediction Results with their Corresponding Input Records .
InputFilter (string) --
A JSONPath expression used to select a portion of the input data to pass to the algorithm. Use the InputFilter parameter to exclude fields, such as an ID column, from the input. If you want Amazon SageMaker to pass the entire input dataset to the algorithm, accept the default value $ .
Examples: "$" , "$[1:]" , "$.features"
OutputFilter (string) --
A JSONPath expression used to select a portion of the joined dataset to save in the output file for a batch transform job. If you want Amazon SageMaker to store the entire input dataset in the output file, leave the default value, $ . If you specify indexes that aren't within the dimension size of the joined dataset, you get an error.
Examples: "$" , "$[0,5:]" , "$['id','SageMakerOutput']"
JoinSource (string) --
Specifies the source of the data to join with the transformed data. The valid values are None and Input . The default value is None , which specifies not to join the input with the transformed data. If you want the batch transform job to join the original input data with the transformed data, set JoinSource to Input . You can specify OutputFilter as an additional filter to select a portion of the joined dataset and store it in the output file.
For JSON or JSONLines objects, such as a JSON array, Amazon SageMaker adds the transformed data to the input JSON object in an attribute called SageMakerOutput . The joined result for JSON must be a key-value pair object. If the input is not a key-value pair object, Amazon SageMaker creates a new JSON file. In the new JSON file, and the input data is stored under the SageMakerInput key and the results are stored in SageMakerOutput .
For CSV data, Amazon SageMaker takes each row as a JSON array and joins the transformed data with the input by appending each transformed row to the end of the input. The joined data has the original input data followed by the transformed data and the output is a CSV file.
For information on how joining in applied, see Workflow for Associating Inferences with Input Records .
ExperimentConfig (dict) --
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
ExperimentName (string) --
The name of an existing experiment to associate the trial component with.
TrialName (string) --
The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
TrialComponentDisplayName (string) --
The display name for the trial component. If this key isn't specified, the display name is the trial component name.
Tags (list) --
A list of tags associated with the transform job.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
Tags (list) --
The list of tags that are associated with the component. You can use Search API to search on the tags.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
Parents (list) --
An array of the parents of the component. A parent is a trial the component is associated with and the experiment the trial is part of. A component might not have any parents.
(dict) --
The trial that a trial component is associated with and the experiment the trial is part of. A component might not be associated with a trial. A component can be associated with multiple trials.
TrialName (string) --
The name of the trial.
ExperimentName (string) --
The name of the experiment.
Endpoint (dict) --
A hosted endpoint for real-time inference.
EndpointName (string) --
The name of the endpoint.
EndpointArn (string) --
The Amazon Resource Name (ARN) of the endpoint.
EndpointConfigName (string) --
The endpoint configuration associated with the endpoint.
ProductionVariants (list) --
A list of the production variants hosted on the endpoint. Each production variant is a model.
(dict) --
Describes weight and capacities for a production variant associated with an endpoint. If you sent a request to the UpdateEndpointWeightsAndCapacities API and the endpoint status is Updating , you get different desired and current values.
VariantName (string) --
The name of the variant.
DeployedImages (list) --
An array of DeployedImage objects that specify the Amazon EC2 Container Registry paths of the inference images deployed on instances of this ProductionVariant .
(dict) --
Gets the Amazon EC2 Container Registry path of the docker image of the model that is hosted in this ProductionVariant .
If you used the registry/repository[:tag] form to specify the image path of the primary container when you created the model hosted in this ProductionVariant , the path resolves to a path of the form registry/repository[@digest] . A digest is a hash value that identifies a specific version of an image. For information about Amazon ECR paths, see Pulling an Image in the Amazon ECR User Guide .
SpecifiedImage (string) --
The image path you specified when you created the model.
ResolvedImage (string) --
The specific digest path of the image hosted in this ProductionVariant .
ResolutionTime (datetime) --
The date and time when the image path for the model resolved to the ResolvedImage
CurrentWeight (float) --
The weight associated with the variant.
DesiredWeight (float) --
The requested weight, as specified in the UpdateEndpointWeightsAndCapacities request.
CurrentInstanceCount (integer) --
The number of instances associated with the variant.
DesiredInstanceCount (integer) --
The number of instances requested in the UpdateEndpointWeightsAndCapacities request.
DataCaptureConfig (dict) --
EndpointStatus (string) --
The status of the endpoint.
FailureReason (string) --
If the endpoint failed, the reason it failed.
CreationTime (datetime) --
The time that the endpoint was created.
LastModifiedTime (datetime) --
The last time the endpoint was modified.
MonitoringSchedules (list) --
A list of monitoring schedules for the endpoint. For information about model monitoring, see Amazon SageMaker Model Monitor .
(dict) --
A schedule for a model monitoring job. For information about model monitor, see Amazon SageMaker Model Monitor .
MonitoringScheduleArn (string) --
The Amazon Resource Name (ARN) of the monitoring schedule.
MonitoringScheduleName (string) --
The name of the monitoring schedule.
MonitoringScheduleStatus (string) --
The status of the monitoring schedule. This can be one of the following values.
MonitoringType (string) --
The type of the monitoring job definition to schedule.
FailureReason (string) --
If the monitoring schedule failed, the reason it failed.
CreationTime (datetime) --
The time that the monitoring schedule was created.
LastModifiedTime (datetime) --
The last time the monitoring schedule was changed.
MonitoringScheduleConfig (dict) --
Configures the monitoring schedule and defines the monitoring job.
ScheduleConfig (dict) --
Configures the monitoring schedule.
ScheduleExpression (string) --
A cron expression that describes details about the monitoring schedule.
Currently the only supported cron expressions are:
For example, the following are valid cron expressions:
To support running every 6, 12 hours, the following are also supported:
cron(0 [00-23]/[01-24] ? * * *)
For example, the following are valid cron expressions:
Note
MonitoringJobDefinition (dict) --
Defines the monitoring job.
BaselineConfig (dict) --
Baseline configuration used to validate that the data conforms to the specified constraints and statistics
BaseliningJobName (string) --
The name of the job that performs baselining for the monitoring job.
ConstraintsResource (dict) --
The baseline constraint file in Amazon S3 that the current monitoring job should validated against.
S3Uri (string) --
The Amazon S3 URI for the constraints resource.
StatisticsResource (dict) --
The baseline statistics file in Amazon S3 that the current monitoring job should be validated against.
S3Uri (string) --
The Amazon S3 URI for the statistics resource.
MonitoringInputs (list) --
The array of inputs for the monitoring job. Currently we support monitoring an Amazon SageMaker Endpoint.
(dict) --
The inputs for a monitoring job.
EndpointInput (dict) --
The endpoint for a monitoring job.
EndpointName (string) --
An endpoint in customer's account which has enabled DataCaptureConfig enabled.
LocalPath (string) --
Path to the filesystem where the endpoint data is available to the container.
S3InputMode (string) --
Whether the Pipe or File is used as the input mode for transfering data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .
S3DataDistributionType (string) --
Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated
FeaturesAttribute (string) --
The attributes of the input data that are the input features.
InferenceAttribute (string) --
The attribute of the input data that represents the ground truth label.
ProbabilityAttribute (string) --
In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute (float) --
The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset (string) --
If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
EndTimeOffset (string) --
If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
MonitoringOutputConfig (dict) --
The array of outputs from the monitoring job to be uploaded to Amazon Simple Storage Service (Amazon S3).
MonitoringOutputs (list) --
Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.
(dict) --
The output object for a monitoring job.
S3Output (dict) --
The Amazon S3 storage location where the results of a monitoring job are saved.
S3Uri (string) --
A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.
LocalPath (string) --
The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.
S3UploadMode (string) --
Whether to upload the results of the monitoring job continuously or after the job completes.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.
MonitoringResources (dict) --
Identifies the resources, ML compute instances, and ML storage volumes to deploy for a monitoring job. In distributed processing, you specify more than one instance.
ClusterConfig (dict) --
The configuration for the cluster resources used to run the processing job.
InstanceCount (integer) --
The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
InstanceType (string) --
The ML compute instance type for the processing job.
VolumeSizeInGB (integer) --
The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.
VolumeKmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job.
MonitoringAppSpecification (dict) --
Configures the monitoring job to run a specified Docker container image.
ImageUri (string) --
The container image to be run by the monitoring job.
ContainerEntrypoint (list) --
Specifies the entrypoint for a container used to run the monitoring job.
ContainerArguments (list) --
An array of arguments for the container used to run the monitoring job.
RecordPreprocessorSourceUri (string) --
An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flatted json so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.
PostAnalyticsProcessorSourceUri (string) --
An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.
StoppingCondition (dict) --
Specifies a time limit for how long the monitoring job is allowed to run.
MaxRuntimeInSeconds (integer) --
The maximum runtime allowed in seconds.
Note
The MaxRuntimeInSeconds cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.
Environment (dict) --
Sets the environment variables in the Docker container.
NetworkConfig (dict) --
Specifies networking options for an monitoring job.
EnableInterContainerTrafficEncryption (boolean) --
Whether to encrypt all communications between distributed processing jobs. Choose True to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer.
EnableNetworkIsolation (boolean) --
Whether to allow inbound and outbound network calls to and from the containers used for the processing job.
VpcConfig (dict) --
Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud .
SecurityGroupIds (list) --
The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
Subnets (list) --
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
RoleArn (string) --
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
MonitoringJobDefinitionName (string) --
The name of the monitoring job definition to schedule.
MonitoringType (string) --
The type of the monitoring job definition to schedule.
EndpointName (string) --
The endpoint that hosts the model being monitored.
LastMonitoringExecutionSummary (dict) --
Summary of information about the last monitoring job to run.
MonitoringScheduleName (string) --
The name of the monitoring schedule.
ScheduledTime (datetime) --
The time the monitoring job was scheduled.
CreationTime (datetime) --
The time at which the monitoring job was created.
LastModifiedTime (datetime) --
A timestamp that indicates the last time the monitoring job was modified.
MonitoringExecutionStatus (string) --
The status of the monitoring job.
ProcessingJobArn (string) --
The Amazon Resource Name (ARN) of the monitoring job.
EndpointName (string) --
The name of the endpoint used to run the monitoring job.
FailureReason (string) --
Contains the reason a monitoring job failed, if it failed.
MonitoringJobDefinitionName (string) --
The name of the monitoring job.
MonitoringType (string) --
The type of the monitoring job.
Tags (list) --
A list of the tags associated with the monitoring schedlue. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
Tags (list) --
A list of the tags associated with the endpoint. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
ModelPackage (dict) --
A versioned model that can be deployed for SageMaker inference.
ModelPackageName (string) --
The name of the model.
ModelPackageGroupName (string) --
The model group to which the model belongs.
ModelPackageVersion (integer) --
The version number of a versioned model.
ModelPackageArn (string) --
The Amazon Resource Name (ARN) of the model package.
ModelPackageDescription (string) --
The description of the model package.
CreationTime (datetime) --
The time that the model package was created.
InferenceSpecification (dict) --
Defines how to perform inference generation after a training job is run.
Containers (list) --
The Amazon ECR registry path of the Docker image that contains the inference code.
(dict) --
Describes the Docker container for the model package.
ContainerHostname (string) --
The DNS host name for the Docker container.
Image (string) --
The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by Amazon SageMaker, the inference code must meet Amazon SageMaker requirements. Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
ImageDigest (string) --
An MD5 hash of the training algorithm that identifies the Docker image used for training.
ModelDataUrl (string) --
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
Note
The model artifacts must be in an S3 bucket that is in the same region as the model package.
ProductId (string) --
The Amazon Web Services Marketplace product ID of the model package.
Environment (dict) --
The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
SupportedTransformInstanceTypes (list) --
A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
This parameter is required for unversioned models, and optional for versioned models.
SupportedRealtimeInferenceInstanceTypes (list) --
A list of the instance types that are used to generate inferences in real-time.
This parameter is required for unversioned models, and optional for versioned models.
SupportedContentTypes (list) --
The supported MIME types for the input data.
SupportedResponseMIMETypes (list) --
The supported MIME types for the output data.
SourceAlgorithmSpecification (dict) --
A list of algorithms that were used to create a model package.
SourceAlgorithms (list) --
A list of the algorithms that were used to create a model package.
(dict) --
Specifies an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your Amazon SageMaker account or an algorithm in Amazon Web Services Marketplace that you are subscribed to.
ModelDataUrl (string) --
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
Note
The model artifacts must be in an S3 bucket that is in the same region as the algorithm.
AlgorithmName (string) --
The name of an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your Amazon SageMaker account or an algorithm in Amazon Web Services Marketplace that you are subscribed to.
ValidationSpecification (dict) --
Specifies batch transform jobs that Amazon SageMaker runs to validate your model package.
ValidationRole (string) --
The IAM roles to be used for the validation of the model package.
ValidationProfiles (list) --
An array of ModelPackageValidationProfile objects, each of which specifies a batch transform job that Amazon SageMaker runs to validate your model package.
(dict) --
Contains data, such as the inputs and targeted instance types that are used in the process of validating the model package.
The data provided in the validation profile is made available to your buyers on Amazon Web Services Marketplace.
ProfileName (string) --
The name of the profile for the model package.
TransformJobDefinition (dict) --
The TransformJobDefinition object that describes the transform job used for the validation of the model package.
MaxConcurrentTransforms (integer) --
The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1.
MaxPayloadInMB (integer) --
The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata).
BatchStrategy (string) --
A string that determines the number of records included in a single mini-batch.
SingleRecord means only one record is used per mini-batch. MultiRecord means a mini-batch is set to contain as many records that can fit within the MaxPayloadInMB limit.
Environment (dict) --
The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
TransformInput (dict) --
A description of the input source and the way the transform job consumes it.
DataSource (dict) --
Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) --
If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.
The following values are compatible: ManifestFile , S3Prefix
The following value is not compatible: AugmentedManifestFile
S3Uri (string) --
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
ContentType (string) --
The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
CompressionType (string) --
If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None .
SplitType (string) --
The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None , which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:
When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord , Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord , Amazon SageMaker sends individual records in each request.
Note
Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord . Padding is not removed if the value of BatchStrategy is set to MultiRecord .
For more information about RecordIO , see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord , see Consuming TFRecord data in the TensorFlow documentation.
TransformOutput (dict) --
Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
S3OutputPath (string) --
The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, s3://bucket-name/key-name-prefix .
For every S3 object used as input for the transform job, batch transform stores the transformed data with an .``out`` suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at s3://bucket-name/input-name-prefix/dataset01/data.csv , batch transform stores the transformed data at s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out . Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .``out`` file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.
Accept (string) --
The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
AssembleWith (string) --
Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify None . To add a newline character at the end of every transformed record, specify Line .
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
TransformResources (dict) --
Identifies the ML compute instances for the transform job.
InstanceType (string) --
The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or ml.m5.large instance types.
InstanceCount (integer) --
The number of ML compute instances to use in the transform job. For distributed transform jobs, specify a value greater than 1. The default value is 1 .
VolumeKmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be any of the following formats:
ModelPackageStatus (string) --
The status of the model package. This can be one of the following values.
ModelPackageStatusDetails (dict) --
Specifies the validation and image scan statuses of the model package.
ValidationStatuses (list) --
The validation status of the model package.
(dict) --
Represents the overall status of a model package.
Name (string) --
The name of the model package for which the overall status is being reported.
Status (string) --
The current status.
FailureReason (string) --
if the overall status is Failed , the reason for the failure.
ImageScanStatuses (list) --
The status of the scan of the Docker image container for the model package.
(dict) --
Represents the overall status of a model package.
Name (string) --
The name of the model package for which the overall status is being reported.
Status (string) --
The current status.
FailureReason (string) --
if the overall status is Failed , the reason for the failure.
CertifyForMarketplace (boolean) --
Whether the model package is to be certified to be listed on Amazon Web Services Marketplace. For information about listing model packages on Amazon Web Services Marketplace, see List Your Algorithm or Model Package on Amazon Web Services Marketplace .
ModelApprovalStatus (string) --
The approval status of the model. This can be one of the following values.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
MetadataProperties (dict) --
Metadata properties of the tracking entity, trial, or trial component.
CommitId (string) --
The commit ID.
Repository (string) --
The repository.
GeneratedBy (string) --
The entity this entity was generated by.
ProjectId (string) --
The project ID.
ModelMetrics (dict) --
Metrics for the model.
ModelQuality (dict) --
Metrics that measure the quality of a model.
Statistics (dict) --
Model quality statistics.
Constraints (dict) --
Model quality constraints.
ModelDataQuality (dict) --
Metrics that measure the quality of the input data for a model.
Statistics (dict) --
Data quality statistics for a model.
Constraints (dict) --
Data quality constraints for a model.
Bias (dict) --
Metrics that measure bais in a model.
Report (dict) --
The bias report for a model
Explainability (dict) --
Metrics that help explain a model.
Report (dict) --
The explainability report for a model.
LastModifiedTime (datetime) --
The last time the model package was modified.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
ApprovalDescription (string) --
A description provided when the model approval is set.
Tags (list) --
A list of the tags associated with the model package. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
ModelPackageGroup (dict) --
A group of versioned models in the model registry.
ModelPackageGroupName (string) --
The name of the model group.
ModelPackageGroupArn (string) --
The Amazon Resource Name (ARN) of the model group.
ModelPackageGroupDescription (string) --
The description for the model group.
CreationTime (datetime) --
The time that the model group was created.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
ModelPackageGroupStatus (string) --
The status of the model group. This can be one of the following values.
Tags (list) --
A list of the tags associated with the model group. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
Pipeline (dict) --
A SageMaker Model Building Pipeline instance.
PipelineArn (string) --
The Amazon Resource Name (ARN) of the pipeline.
PipelineName (string) --
The name of the pipeline.
PipelineDisplayName (string) --
The display name of the pipeline.
PipelineDescription (string) --
The description of the pipeline.
RoleArn (string) --
The Amazon Resource Name (ARN) of the role that created the pipeline.
PipelineStatus (string) --
The status of the pipeline.
CreationTime (datetime) --
The creation time of the pipeline.
LastModifiedTime (datetime) --
The time that the pipeline was last modified.
LastRunTime (datetime) --
The time when the pipeline was last run.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
Tags (list) --
A list of tags that apply to the pipeline.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
PipelineExecution (dict) --
An execution of a pipeline.
PipelineArn (string) --
The Amazon Resource Name (ARN) of the pipeline that was executed.
PipelineExecutionArn (string) --
The Amazon Resource Name (ARN) of the pipeline execution.
PipelineExecutionDisplayName (string) --
The display name of the pipeline execution.
PipelineExecutionStatus (string) --
The status of the pipeline status.
PipelineExecutionDescription (string) --
The description of the pipeline execution.
PipelineExperimentConfig (dict) --
Specifies the names of the experiment and trial created by a pipeline.
ExperimentName (string) --
The name of the experiment.
TrialName (string) --
The name of the trial.
FailureReason (string) --
If the execution failed, a message describing why.
CreationTime (datetime) --
The creation time of the pipeline execution.
LastModifiedTime (datetime) --
The time that the pipeline execution was last modified.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, or trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
PipelineParameters (list) --
Contains a list of pipeline parameters. This list can be empty.
(dict) --
Assigns a value to a named Pipeline parameter.
Name (string) --
The name of the parameter to assign a value to. This parameter name must match a named parameter in the pipeline definition.
Value (string) --
The literal value for the parameter.
FeatureGroup (dict) --
Amazon SageMaker Feature Store stores features in a collection called Feature Group. A Feature Group can be visualized as a table which has rows, with a unique identifier for each row where each column in the table is a feature. In principle, a Feature Group is composed of features and values per features.
FeatureGroupArn (string) --
The Amazon Resource Name (ARN) of a FeatureGroup .
FeatureGroupName (string) --
The name of the FeatureGroup .
RecordIdentifierFeatureName (string) --
The name of the Feature whose value uniquely identifies a Record defined in the FeatureGroup FeatureDefinitions .
EventTimeFeatureName (string) --
The name of the feature that stores the EventTime of a Record in a FeatureGroup .
A EventTime is point in time when a new event occurs that corresponds to the creation or update of a Record in FeatureGroup . All Records in the FeatureGroup must have a corresponding EventTime .
FeatureDefinitions (list) --
A list of Feature s. Each Feature must include a FeatureName and a FeatureType .
Valid FeatureType s are Integral , Fractional and String .
FeatureName s cannot be any of the following: is_deleted , write_time , api_invocation_time .
You can create up to 2,500 FeatureDefinition s per FeatureGroup .
(dict) --
A list of features. You must include FeatureName and FeatureType . Valid feature FeatureType s are Integral , Fractional and String .
FeatureName (string) --
The name of a feature. The type must be a string. FeatureName cannot be any of the following: is_deleted , write_time , api_invocation_time .
FeatureType (string) --
The value type of a feature. Valid values are Integral, Fractional, or String.
CreationTime (datetime) --
The time a FeatureGroup was created.
OnlineStoreConfig (dict) --
Use this to specify the Amazon Web Services Key Management Service (KMS) Key ID, or KMSKeyId , for at rest data encryption. You can turn OnlineStore on or off by specifying the EnableOnlineStore flag at General Assembly; the default value is False .
SecurityConfig (dict) --
Use to specify KMS Key ID (KMSKeyId ) for at-rest encryption of your OnlineStore .
KmsKeyId (string) --
The ID of the Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker Feature Store uses to encrypt the Amazon S3 objects at rest using Amazon S3 server-side encryption.
The caller (either IAM user or IAM role) of CreateFeatureGroup must have below permissions to the OnlineStore KmsKeyId :
The caller (either IAM user or IAM role) to all DataPlane operations (PutRecord , GetRecord , DeleteRecord ) must have the following permissions to the KmsKeyId :
EnableOnlineStore (boolean) --
Turn OnlineStore off by specifying False for the EnableOnlineStore flag. Turn OnlineStore on by specifying True for the EnableOnlineStore flag.
The default value is False .
OfflineStoreConfig (dict) --
The configuration of an OfflineStore .
Provide an OfflineStoreConfig in a request to CreateFeatureGroup to create an OfflineStore .
To encrypt an OfflineStore using at rest data encryption, specify Amazon Web Services Key Management Service (KMS) key ID, or KMSKeyId , in S3StorageConfig .
S3StorageConfig (dict) --
The Amazon Simple Storage (Amazon S3) location of OfflineStore .
S3Uri (string) --
The S3 URI, or location in Amazon S3, of OfflineStore .
S3 URIs have a format similar to the following: s3://example-bucket/prefix/ .
KmsKeyId (string) --
The Amazon Web Services Key Management Service (KMS) key ID of the key used to encrypt any objects written into the OfflineStore S3 location.
The IAM roleARN that is passed as a parameter to CreateFeatureGroup must have below permissions to the KmsKeyId :
ResolvedOutputS3Uri (string) --
The S3 path where offline records are written.
DisableGlueTableCreation (boolean) --
Set to True to disable the automatic creation of an Amazon Web Services Glue table when configuring an OfflineStore .
DataCatalogConfig (dict) --
The meta data of the Glue table that is autogenerated when an OfflineStore is created.
TableName (string) --
The name of the Glue table.
Catalog (string) --
The name of the Glue table catalog.
Database (string) --
The name of the Glue table database.
RoleArn (string) --
The Amazon Resource Name (ARN) of the IAM execution role used to create the feature group.
FeatureGroupStatus (string) --
A FeatureGroup status.
OfflineStoreStatus (dict) --
The status of OfflineStore .
Status (string) --
An OfflineStore status.
BlockedReason (string) --
The justification for why the OfflineStoreStatus is Blocked (if applicable).
FailureReason (string) --
The reason that the FeatureGroup failed to be replicated in the OfflineStore . This is failure may be due to a failure to create a FeatureGroup in or delete a FeatureGroup from the OfflineStore .
Description (string) --
A free form description of a FeatureGroup .
Tags (list) --
Tags used to define a FeatureGroup .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for Amazon SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to Amazon SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
The available waiters are:
waiter = client.get_waiter('endpoint_deleted')
Polls SageMaker.Client.describe_endpoint() every 30 seconds until a successful state is reached. An error is returned after 60 failed checks.
See also: AWS API Documentation
Request Syntax
waiter.wait(
EndpointName='string',
WaiterConfig={
'Delay': 123,
'MaxAttempts': 123
}
)
[REQUIRED]
The name of the endpoint.
A dictionary that provides parameters to control waiting behavior.
The amount of time in seconds to wait between attempts. Default: 30
The maximum number of attempts to be made. Default: 60
None
waiter = client.get_waiter('endpoint_in_service')
Polls SageMaker.Client.describe_endpoint() every 30 seconds until a successful state is reached. An error is returned after 120 failed checks.
See also: AWS API Documentation
Request Syntax
waiter.wait(
EndpointName='string',
WaiterConfig={
'Delay': 123,
'MaxAttempts': 123
}
)
[REQUIRED]
The name of the endpoint.
A dictionary that provides parameters to control waiting behavior.
The amount of time in seconds to wait between attempts. Default: 30
The maximum number of attempts to be made. Default: 120
None
waiter = client.get_waiter('notebook_instance_deleted')
Polls SageMaker.Client.describe_notebook_instance() every 30 seconds until a successful state is reached. An error is returned after 60 failed checks.
See also: AWS API Documentation
Request Syntax
waiter.wait(
NotebookInstanceName='string',
WaiterConfig={
'Delay': 123,
'MaxAttempts': 123
}
)
[REQUIRED]
The name of the notebook instance that you want information about.
A dictionary that provides parameters to control waiting behavior.
The amount of time in seconds to wait between attempts. Default: 30
The maximum number of attempts to be made. Default: 60
None
waiter = client.get_waiter('notebook_instance_in_service')
Polls SageMaker.Client.describe_notebook_instance() every 30 seconds until a successful state is reached. An error is returned after 60 failed checks.
See also: AWS API Documentation
Request Syntax
waiter.wait(
NotebookInstanceName='string',
WaiterConfig={
'Delay': 123,
'MaxAttempts': 123
}
)
[REQUIRED]
The name of the notebook instance that you want information about.
A dictionary that provides parameters to control waiting behavior.
The amount of time in seconds to wait between attempts. Default: 30
The maximum number of attempts to be made. Default: 60
None
waiter = client.get_waiter('notebook_instance_stopped')
Polls SageMaker.Client.describe_notebook_instance() every 30 seconds until a successful state is reached. An error is returned after 60 failed checks.
See also: AWS API Documentation
Request Syntax
waiter.wait(
NotebookInstanceName='string',
WaiterConfig={
'Delay': 123,
'MaxAttempts': 123
}
)
[REQUIRED]
The name of the notebook instance that you want information about.
A dictionary that provides parameters to control waiting behavior.
The amount of time in seconds to wait between attempts. Default: 30
The maximum number of attempts to be made. Default: 60
None
waiter = client.get_waiter('processing_job_completed_or_stopped')
Polls SageMaker.Client.describe_processing_job() every 60 seconds until a successful state is reached. An error is returned after 60 failed checks.
See also: AWS API Documentation
Request Syntax
waiter.wait(
ProcessingJobName='string',
WaiterConfig={
'Delay': 123,
'MaxAttempts': 123
}
)
[REQUIRED]
The name of the processing job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
A dictionary that provides parameters to control waiting behavior.
The amount of time in seconds to wait between attempts. Default: 60
The maximum number of attempts to be made. Default: 60
None
waiter = client.get_waiter('training_job_completed_or_stopped')
Polls SageMaker.Client.describe_training_job() every 120 seconds until a successful state is reached. An error is returned after 180 failed checks.
See also: AWS API Documentation
Request Syntax
waiter.wait(
TrainingJobName='string',
WaiterConfig={
'Delay': 123,
'MaxAttempts': 123
}
)
[REQUIRED]
The name of the training job.
A dictionary that provides parameters to control waiting behavior.
The amount of time in seconds to wait between attempts. Default: 120
The maximum number of attempts to be made. Default: 180
None
waiter = client.get_waiter('transform_job_completed_or_stopped')
Polls SageMaker.Client.describe_transform_job() every 60 seconds until a successful state is reached. An error is returned after 60 failed checks.
See also: AWS API Documentation
Request Syntax
waiter.wait(
TransformJobName='string',
WaiterConfig={
'Delay': 123,
'MaxAttempts': 123
}
)
[REQUIRED]
The name of the transform job that you want to view details of.
A dictionary that provides parameters to control waiting behavior.
The amount of time in seconds to wait between attempts. Default: 60
The maximum number of attempts to be made. Default: 60
None