Table of Contents
A low-level client representing Amazon SageMaker Service:
import boto3
client = boto3.client('sagemaker')
These are the available methods:
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, models, 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 AWS 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
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 Tag objects. Each tag is a key-value pair. Only the key parameter is required. If you don't specify a value, Amazon SageMaker sets the value to an empty string.
Describes a tag.
The tag key.
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) --
Describes a tag.
Key (string) --
The tag key.
Value (string) --
The tag value.
Check if an operation can be paginated.
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.
Note
Use this API only for hosting models using Amazon SageMaker hosting services.
The endpoint name must be unique within an AWS Region in your AWS account.
When it receives the request, Amazon SageMaker creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them.
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.
For an example, see Exercise 1: Using the K-Means Algorithm Provided by Amazon SageMaker .
If any of the models hosted at this endpoint get model data from an Amazon S3 location, Amazon SageMaker uses AWS Security Token Service to download model artifacts from the S3 path you provided. AWS STS is activated in your IAM user account by default. If you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more information, see Activating and Deactivating AWS STS i an AWS Region in the AWS Identity and Access Management User Guide .
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 AWS Region in your AWS account.
[REQUIRED]
The name of an endpoint configuration. For more information, see CreateEndpointConfig .
An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide .
Describes a tag.
The tag key.
The tag value.
dict
Response Syntax
{
'EndpointArn': 'string'
}
Response Structure
(dict) --
EndpointArn (string) --
The Amazon Resource Name (ARN) of the endpoint.
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 only if you want to use Amazon SageMaker hosting services to deploy models into production.
In the request, you define one or more ProductionVariant s, each of which identifies a model. 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.
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.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',
'InitialVariantWeight': ...
},
],
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
KmsKeyId='string'
)
[REQUIRED]
The name of the endpoint configuration. You specify this name in a CreateEndpoint request.
[REQUIRED]
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 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.
An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide .
Describes a tag.
The tag key.
The tag value.
dict
Response Syntax
{
'EndpointConfigArn': 'string'
}
Response Structure
(dict) --
EndpointConfigArn (string) --
The Amazon Resource Name (ARN) of the endpoint configuration.
Starts a hyperparameter tuning job.
See also: AWS API Documentation
Request Syntax
response = client.create_hyper_parameter_tuning_job(
HyperParameterTuningJobName='string',
HyperParameterTuningJobConfig={
'Strategy': 'Bayesian',
'HyperParameterTuningJobObjective': {
'Type': 'Maximize'|'Minimize',
'MetricName': 'string'
},
'ResourceLimits': {
'MaxNumberOfTrainingJobs': 123,
'MaxParallelTrainingJobs': 123
},
'ParameterRanges': {
'IntegerParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string'
},
],
'ContinuousParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string'
},
],
'CategoricalParameterRanges': [
{
'Name': 'string',
'Values': [
'string',
]
},
]
}
},
TrainingJobDefinition={
'StaticHyperParameters': {
'string': 'string'
},
'AlgorithmSpecification': {
'TrainingImage': 'string',
'TrainingInputMode': 'Pipe'|'File',
'MetricDefinitions': [
{
'Name': 'string',
'Regex': 'string'
},
]
},
'RoleArn': 'string',
'InputDataConfig': [
{
'ChannelName': 'string',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix',
'S3Uri': 'string',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File'
},
],
'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.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.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123
}
},
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 AWS account and AWS Region. Names are not case sensitive, and must be between 1-32 characters.
[REQUIRED]
The HyperParameterTuningJobConfig object that describes the tuning job, including the search strategy, metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the tuning job.
Specifies the search strategy for hyperparameters. Currently, the only valid value is Bayesian .
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 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 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.
[REQUIRED]
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.
Specifies the values of hyperparameters that do not change for the tuning job.
The HyperParameterAlgorithmSpecification object that specifies the 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 .
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 .
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 with 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.
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.
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 which 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.
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.
Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
The AWS Key Management Service (AWS 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 the 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 Amazon Simple Storage Service Developer Guide.
Note
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob request. Using Key Policies in AWS KMS in the AWS 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.
The AWS Key Management Service (AWS 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. The VolumeKmsKeyId can be any of the following formats:
Sets a maximum duration for the training jobs that the tuning job launches. Use this parameter to limit model training costs.
To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal. This delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts.
When Amazon SageMaker terminates a job because the stopping condition has been met, training algorithms provided by Amazon SageMaker save the intermediate results of the job.
The maximum length of time, in seconds, that the training job can run. If model training does not complete during this time, Amazon SageMaker ends the job. If value is not specified, default value is 1 day. Maximum value is 5 days.
An array of key-value pairs. You can use tags to categorize your AWS resources in different ways, for example, by purpose, owner, or environment. For more information, see AWS Tagging Strategies .
Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.
Describes a tag.
The tag key.
The tag value.
dict
Response Syntax
{
'HyperParameterTuningJobArn': 'string'
}
Response Structure
(dict) --
HyperParameterTuningJobArn (string) --
The Amazon Resource Name (ARN) of the tuning job.
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 containing inference code, artifacts (from prior training), and 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.
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 AWS 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',
'ModelDataUrl': 'string',
'Environment': {
'string': 'string'
}
},
ExecutionRoleArn='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
VpcConfig={
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
)
[REQUIRED]
The name of the new model.
[REQUIRED]
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.
The DNS host name for the container after Amazon SageMaker deploys it.
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
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).
If you provide a value for this parameter, Amazon SageMaker uses AWS Security Token Service to download model artifacts from the S3 path you provide. AWS STS is activated in your IAM user account by default. If you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more information, see Activating and Deactivating AWS STS i an AWS Region in the AWS Identity and Access Management User Guide .
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.
[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. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide .
Describes a tag.
The tag key.
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.
dict
Response Syntax
{
'ModelArn': 'string'
}
Response Structure
(dict) --
ModelArn (string) --
The ARN of the model created in Amazon SageMaker.
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).
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.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'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
)
[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 AWS 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.
A list of tags to associate with the notebook instance. You can add tags later by using the CreateTags API.
Describes a tag.
The tag key.
The tag value.
Sets whether Amazon SageMaker provides internet access to the notebook instance. If you set this to Disabled this notebook instance will be able to access resources only in your VPC, and will not be able to connect to Amazon SageMaker training and endpoint services unless your 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.
dict
Response Syntax
{
'NotebookInstanceArn': 'string'
}
Response Structure
(dict) --
NotebookInstanceArn (string) --
The Amazon Resource Name (ARN) of the notebook instance.
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.
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.
You can restrict access to this API and to the URL that it returns to a list of IP addresses that you specify. To restrict access, attach an IAM policy that denies access to this API unless the call comes from an IP address in the specified list to every AWS Identity and Access Management user, group, or role used to access the notebook instance. 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 .
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.
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 deep learning service other than Amazon SageMaker, provided that you know how to use them for inferences.
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',
'TrainingInputMode': 'Pipe'|'File'
},
RoleArn='string',
InputDataConfig=[
{
'ChannelName': 'string',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix',
'S3Uri': 'string',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File'
},
],
OutputDataConfig={
'KmsKeyId': 'string',
'S3OutputPath': 'string'
},
ResourceConfig={
'InstanceType': '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.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',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
},
VpcConfig={
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
StoppingCondition={
'MaxRuntimeInSeconds': 123
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
[REQUIRED]
The name of the training job. The name must be unique within an AWS Region in an AWS 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 .
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.
[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 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.
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 with 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.
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.
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 which 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.
[REQUIRED]
Specifies the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.
The AWS Key Management Service (AWS 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 the 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 Amazon Simple Storage Service Developer Guide.
Note
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob request. Using Key Policies in AWS KMS in the AWS 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.
The AWS Key Management Service (AWS 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. The VolumeKmsKeyId can be 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.
[REQUIRED]
Sets a duration for training. Use this parameter 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 might use this 120-second window to save the model artifacts.
When Amazon SageMaker terminates a job because the stopping condition has been met, training algorithms provided by Amazon SageMaker save the intermediate results of the job. This intermediate data is a valid model artifact. You can use it to create a model using the CreateModel API.
The maximum length of time, in seconds, that the training job can run. If model training does not complete during this time, Amazon SageMaker ends the job. If value is not specified, default value is 1 day. Maximum value is 5 days.
An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide .
Describes a tag.
The tag key.
The tag value.
dict
Response Syntax
{
'TrainingJobArn': 'string'
}
Response Structure
(dict) --
TrainingJobArn (string) --
The Amazon Resource Name (ARN) of the training job.
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 Amazon SageMaker, see How It Works .
See also: AWS API Documentation
Request Syntax
response = client.create_transform_job(
TransformJobName='string',
ModelName='string',
MaxConcurrentTransforms=123,
MaxPayloadInMB=123,
BatchStrategy='MultiRecord'|'SingleRecord',
Environment={
'string': 'string'
},
TransformInput={
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix',
'S3Uri': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'SplitType': 'None'|'Line'|'RecordIO'
},
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',
'InstanceCount': 123,
'VolumeKmsKeyId': 'string'
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
[REQUIRED]
The name of the transform job. The name must be unique within an AWS Region in an AWS 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 AWS Region in an AWS account.
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.
Batch transform will automatically split your input data into whatever payload size is specified if you set SplitType to Line and BatchStrategy to MultiRecord . There's no need to split the dataset into smaller files or to use larger payload sizes unless the records in your dataset are very large.
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, meaning 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.
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.
Compressing data helps save on storage space. 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 into smaller batches. The default value is None . If you don't want to split the data, specify None . If you want to split records on a newline character boundary, specify Line . To split records according to the RecordIO format, specify RecordIO .
Amazon SageMaker will send maximum number of records per batch in each request up to the MaxPayloadInMB limit. For more information, see RecordIO data format .
Note
For information about the RecordIO format, see Data Format .
[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, the transformed data is stored in a corresponding subfolder in the location under the output prefix. For example, the input data s3://bucket-name/input-name-prefix/dataset01/data.csv will have the transformed data stored at s3://bucket-name/key-name-prefix/dataset01/ , based on the original name, as a series of .part files (.part0001, part0002, etc).
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. You should select 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 AWS Key Management Service (AWS 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 CreateTramsformJob request. For more information, see Using Key Policies in AWS KMS in the AWS 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. For using built-in algorithms to transform moderately sized datasets, ml.m4.xlarge or ml.m5.large should suffice. There is no default value for InstanceType .
The number of ML compute instances to use in the transform job. For distributed transform, provide a value greater than 1. The default value is 1 .
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the batch transform job. The VolumeKmsKeyId can be any of the following formats:
An array of key-value pairs. Adding tags is optional. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide .
Describes a tag.
The tag key.
The tag value.
dict
Response Syntax
{
'TransformJobArn': 'string'
}
Response Structure
(dict) --
TransformJobArn (string) --
The Amazon Resource Name (ARN) of the transform job.
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.
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 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 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 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.
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
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
},
],
'EndpointStatus': 'OutOfService'|'Creating'|'Updating'|'SystemUpdating'|'RollingBack'|'InService'|'Deleting'|'Failed',
'FailureReason': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1)
}
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.
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.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',
'InitialVariantWeight': ...
},
],
'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 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.
AWS 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.
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 to describe.
{
'HyperParameterTuningJobName': 'string',
'HyperParameterTuningJobArn': 'string',
'HyperParameterTuningJobConfig': {
'Strategy': 'Bayesian',
'HyperParameterTuningJobObjective': {
'Type': 'Maximize'|'Minimize',
'MetricName': 'string'
},
'ResourceLimits': {
'MaxNumberOfTrainingJobs': 123,
'MaxParallelTrainingJobs': 123
},
'ParameterRanges': {
'IntegerParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string'
},
],
'ContinuousParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string'
},
],
'CategoricalParameterRanges': [
{
'Name': 'string',
'Values': [
'string',
]
},
]
}
},
'TrainingJobDefinition': {
'StaticHyperParameters': {
'string': 'string'
},
'AlgorithmSpecification': {
'TrainingImage': 'string',
'TrainingInputMode': 'Pipe'|'File',
'MetricDefinitions': [
{
'Name': 'string',
'Regex': 'string'
},
]
},
'RoleArn': 'string',
'InputDataConfig': [
{
'ChannelName': 'string',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix',
'S3Uri': 'string',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File'
},
],
'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.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.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 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': {
'TrainingJobName': 'string',
'TrainingJobArn': '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'
},
'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 the search strategy for hyperparameters. Currently, the only valid value is Bayesian .
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 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 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.
The HyperParameterTrainingJobDefinition object that specifies the definition of the training jobs that this tuning job launches.
Specifies the values of hyperparameters that do not change for the tuning job.
The HyperParameterAlgorithmSpecification object that specifies the 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 .
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 .
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 with 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.
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.
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 which 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.
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.
Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
The AWS Key Management Service (AWS 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 the 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 Amazon Simple Storage Service Developer Guide.
Note
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob request. Using Key Policies in AWS KMS in the AWS 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.
The AWS Key Management Service (AWS 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. The VolumeKmsKeyId can be any of the following formats:
Sets a maximum duration for the training jobs that the tuning job launches. Use this parameter to limit model training costs.
To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal. This delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts.
When Amazon SageMaker terminates a job because the stopping condition has been met, training algorithms provided by Amazon SageMaker save the intermediate results of the job.
The maximum length of time, in seconds, that the training job can run. If model training does not complete during this time, Amazon SageMaker ends the job. If value is not specified, default value is 1 day. Maximum value is 5 days.
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 a 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 name of the training job.
The Amazon Resource Name (ARN) of the training job.
The date and time that the training job was created.
The date and time that the training job started.
The date and time that the training job ended.
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 tuning job failed, the reason it failed.
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',
'ModelDataUrl': 'string',
'Environment': {
'string': 'string'
}
},
'ExecutionRoleArn': 'string',
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
'CreationTime': datetime(2015, 1, 1),
'ModelArn': 'string'
}
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.
The DNS host name for the container after Amazon SageMaker deploys it.
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
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).
If you provide a value for this parameter, Amazon SageMaker uses AWS Security Token Service to download model artifacts from the S3 path you provide. AWS STS is activated in your IAM user account by default. If you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more information, see Activating and Deactivating AWS STS i an AWS Region in the AWS Identity and Access Management User Guide .
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 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.
A timestamp that shows when the model was created.
The Amazon Resource Name (ARN) of the model.
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.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'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
}
Response Structure
The Amazon Resource Name (ARN) of the notebook instance.
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.
Amazon Resource Name (ARN) of the IAM role associated with the instance.
AWS KMS key ID Amazon SageMaker uses to encrypt data when storing it on the ML storage volume attached to the instance.
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, he 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.
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.
Returns information about a training job.
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',
'ModelArtifacts': {
'S3ModelArtifacts': 'string'
},
'TrainingJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'SecondaryStatus': 'Starting'|'LaunchingMLInstances'|'PreparingTrainingStack'|'Downloading'|'DownloadingTrainingImage'|'Training'|'Uploading'|'Stopping'|'Stopped'|'MaxRuntimeExceeded'|'Completed'|'Failed',
'FailureReason': 'string',
'HyperParameters': {
'string': 'string'
},
'AlgorithmSpecification': {
'TrainingImage': 'string',
'TrainingInputMode': 'Pipe'|'File'
},
'RoleArn': 'string',
'InputDataConfig': [
{
'ChannelName': 'string',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix',
'S3Uri': 'string',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File'
},
],
'OutputDataConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string'
},
'ResourceConfig': {
'InstanceType': '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.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',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
},
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 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',
'StartTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1),
'StatusMessage': '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.
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 .
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.
The AWS 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 with 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.
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.
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 which 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.
The S3 path where model artifacts that you configured when creating the job are stored. Amazon SageMaker creates subfolders for model artifacts.
The AWS Key Management Service (AWS 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 the 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 Amazon Simple Storage Service Developer Guide.
Note
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob request. Using Key Policies in AWS KMS in the AWS 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.
The AWS Key Management Service (AWS 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. The VolumeKmsKeyId can be 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.
The condition under which to stop the training job.
The maximum length of time, in seconds, that the training job can run. If model training does not complete during this time, Amazon SageMaker ends the job. If value is not specified, default value is 1 day. Maximum value is 5 days.
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 DescribeTrainingJobResponse , and StatusMessage together. For example, at the start of a training job, you might see the following:
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,
'MaxPayloadInMB': 123,
'BatchStrategy': 'MultiRecord'|'SingleRecord',
'Environment': {
'string': 'string'
},
'TransformInput': {
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix',
'S3Uri': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'SplitType': 'None'|'Line'|'RecordIO'
},
'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',
'InstanceCount': 123,
'VolumeKmsKeyId': 'string'
},
'CreationTime': datetime(2015, 1, 1),
'TransformStartTime': datetime(2015, 1, 1),
'TransformEndTime': datetime(2015, 1, 1)
}
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, the reason that it failed.
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 maximum payload size , in MB used in the transform job.
SingleRecord means only one record was used per a batch. MultiRecord means batches contained as many records that could possibly fit within the MaxPayloadInMB limit.
Describes the dataset to be transformed and the Amazon S3 location where it is stored.
Describes the location of the channel data, meaning 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.
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.
Compressing data helps save on storage space. 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 into smaller batches. The default value is None . If you don't want to split the data, specify None . If you want to split records on a newline character boundary, specify Line . To split records according to the RecordIO format, specify RecordIO .
Amazon SageMaker will send maximum number of records per batch in each request up to the MaxPayloadInMB limit. For more information, see RecordIO data format .
Note
For information about the RecordIO format, see Data Format .
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, the transformed data is stored in a corresponding subfolder in the location under the output prefix. For example, the input data s3://bucket-name/input-name-prefix/dataset01/data.csv will have the transformed data stored at s3://bucket-name/key-name-prefix/dataset01/ , based on the original name, as a series of .part files (.part0001, part0002, etc).
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. You should select 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 AWS Key Management Service (AWS 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 CreateTramsformJob request. For more information, see Using Key Policies in AWS KMS in the AWS 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. For using built-in algorithms to transform moderately sized datasets, ml.m4.xlarge or ml.m5.large should suffice. There is no default value for InstanceType .
The number of ML compute instances to use in the transform job. For distributed transform, provide a value greater than 1. The default value is 1 .
The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the batch transform job. 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 is Completed , Stopped , or Failed . You are billed for the time interval between this time and the value of TransformStartTime .
Generate a presigned url given a client, its method, and arguments
The presigned url
Create a paginator for an operation.
Returns an object that can wait for some condition.
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.
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',
'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 a 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 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.
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 AWS 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'
)
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.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'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'
},
]
}
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 .
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) --
Describes a tag.
Key (string) --
The tag key.
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.
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': [
{
'TrainingJobName': 'string',
'TrainingJobArn': '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.
TrainingJobName (string) --
The name of the training job.
TrainingJobArn (string) --
The Amazon Resource Name (ARN) of 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) --
The date and time that the training job ended.
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.
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 calling ListTransformJobs .
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.
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.
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.
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.
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 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.
Training algorithms provided by Amazon SageMaker save the intermediate results of a model training job. This intermediate data is a valid model artifact. You can use the model artifacts that are saved when Amazon SageMaker stops a training job to create a model.
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.
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.
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 cannot update an endpoint with the current EndpointConfig . To update an endpoint, you must create a new EndpointConfig .
See also: AWS API Documentation
Request Syntax
response = client.update_endpoint(
EndpointName='string',
EndpointConfigName='string'
)
[REQUIRED]
The name of the endpoint whose configuration you want to update.
[REQUIRED]
The name of the new endpoint configuration.
dict
Response Syntax
{
'EndpointArn': 'string'
}
Response Structure
(dict) --
EndpointArn (string) --
The Amazon Resource Name (ARN) of the endpoint.
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.
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. You can also update the VPC security groups.
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.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'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
)
[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.
dict
Response Syntax
{}
Response Structure
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
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.
dict
Response Syntax
{}
Response Structure
The available paginators are:
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_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_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',
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.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'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'
},
]
}
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 .
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) --
Describes a tag.
Key (string) --
The tag key.
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.
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('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