SageMaker

Table of Contents

Client

class SageMaker.Client

A low-level client representing Amazon SageMaker Service

Provides APIs for creating and managing SageMaker resources.

Other Resources:

import boto3

client = boto3.client('sagemaker')

These are the available methods:

add_association(**kwargs)

Creates an association between the source and the destination. A source can be associated with multiple destinations, and a destination can be associated with multiple sources. An association is a lineage tracking entity. For more information, see Amazon SageMaker ML Lineage Tracking.

See also: AWS API Documentation

Request Syntax

response = client.add_association(
    SourceArn='string',
    DestinationArn='string',
    AssociationType='ContributedTo'|'AssociatedWith'|'DerivedFrom'|'Produced'
)
Parameters
  • SourceArn (string) --

    [REQUIRED]

    The ARN of the source.

  • DestinationArn (string) --

    [REQUIRED]

    The Amazon Resource Name (ARN) of the destination.

  • AssociationType (string) --

    The type of association. The following are suggested uses for each type. Amazon SageMaker places no restrictions on their use.

    • ContributedTo - The source contributed to the destination or had a part in enabling the destination. For example, the training data contributed to the training job.
    • AssociatedWith - The source is connected to the destination. For example, an approval workflow is associated with a model deployment.
    • DerivedFrom - The destination is a modification of the source. For example, a digest output of a channel input for a processing job is derived from the original inputs.
    • Produced - The source generated the destination. For example, a training job produced a model artifact.
Return type

dict

Returns

Response Syntax

{
    'SourceArn': 'string',
    'DestinationArn': 'string'
}

Response Structure

  • (dict) --

    • SourceArn (string) --

      The ARN of the source.

    • DestinationArn (string) --

      The Amazon Resource Name (ARN) of the destination.

Exceptions

  • SageMaker.Client.exceptions.ResourceNotFound
  • SageMaker.Client.exceptions.ResourceLimitExceeded
add_tags(**kwargs)

Adds or overwrites one or more tags for the specified SageMaker resource. You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints.

Each tag consists of a key and an optional value. Tag keys must be unique per resource. For more information about tags, see For more information, see Amazon Web Services Tagging Strategies.

Note

Tags that you add to a hyperparameter tuning job by calling this API are also added to any training jobs that the hyperparameter tuning job launches after you call this API, but not to training jobs that the hyperparameter tuning job launched before you called this API. To make sure that the tags associated with a hyperparameter tuning job are also added to all training jobs that the hyperparameter tuning job launches, add the tags when you first create the tuning job by specifying them in the Tags parameter of CreateHyperParameterTuningJob

Note

Tags that you add to a SageMaker Studio Domain or User Profile by calling this API are also added to any Apps that the Domain or User Profile launches after you call this API, but not to Apps that the Domain or User Profile launched before you called this API. To make sure that the tags associated with a Domain or User Profile are also added to all Apps that the Domain or User Profile launches, add the tags when you first create the Domain or User Profile by specifying them in the Tags parameter of CreateDomain or CreateUserProfile.

See also: AWS API Documentation

Request Syntax

response = client.add_tags(
    ResourceArn='string',
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ]
)
Parameters
  • ResourceArn (string) --

    [REQUIRED]

    The Amazon Resource Name (ARN) of the resource that you want to tag.

  • Tags (list) --

    [REQUIRED]

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

    • (dict) --

      A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

      You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.

      For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.

      • Key (string) -- [REQUIRED]

        The tag key. Tag keys must be unique per resource.

      • Value (string) -- [REQUIRED]

        The tag value.

Return type

dict

Returns

Response Syntax

{
    'Tags': [
        {
            'Key': 'string',
            'Value': 'string'
        },
    ]
}

Response Structure

  • (dict) --

    • Tags (list) --

      A list of tags associated with the SageMaker resource.

      • (dict) --

        A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

        You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.

        For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.

        • Key (string) --

          The tag key. Tag keys must be unique per resource.

        • Value (string) --

          The tag value.

associate_trial_component(**kwargs)

Associates a trial component with a trial. A trial component can be associated with multiple trials. To disassociate a trial component from a trial, call the DisassociateTrialComponent API.

See also: AWS API Documentation

Request Syntax

response = client.associate_trial_component(
    TrialComponentName='string',
    TrialName='string'
)
Parameters
  • TrialComponentName (string) --

    [REQUIRED]

    The name of the component to associated with the trial.

  • TrialName (string) --

    [REQUIRED]

    The name of the trial to associate with.

Return type

dict

Returns

Response Syntax

{
    'TrialComponentArn': 'string',
    'TrialArn': 'string'
}

Response Structure

  • (dict) --

    • TrialComponentArn (string) --

      The Amazon Resource Name (ARN) of the trial component.

    • TrialArn (string) --

      The Amazon Resource Name (ARN) of the trial.

Exceptions

  • SageMaker.Client.exceptions.ResourceNotFound
  • SageMaker.Client.exceptions.ResourceLimitExceeded
batch_describe_model_package(**kwargs)

This action batch describes a list of versioned model packages

See also: AWS API Documentation

Request Syntax

response = client.batch_describe_model_package(
    ModelPackageArnList=[
        'string',
    ]
)
Parameters
ModelPackageArnList (list) --

[REQUIRED]

The list of Amazon Resource Name (ARN) of the model package groups.

  • (string) --
Return type
dict
Returns
Response Syntax
{
    'ModelPackageSummaries': {
        'string': {
            'ModelPackageGroupName': 'string',
            'ModelPackageVersion': 123,
            'ModelPackageArn': 'string',
            'ModelPackageDescription': 'string',
            'CreationTime': datetime(2015, 1, 1),
            'InferenceSpecification': {
                'Containers': [
                    {
                        'ContainerHostname': 'string',
                        'Image': 'string',
                        'ImageDigest': 'string',
                        'ModelDataUrl': 'string',
                        'ProductId': 'string',
                        'Environment': {
                            'string': 'string'
                        },
                        'ModelInput': {
                            'DataInputConfig': 'string'
                        },
                        'Framework': 'string',
                        'FrameworkVersion': 'string',
                        'NearestModelName': 'string'
                    },
                ],
                'SupportedTransformInstanceTypes': [
                    'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
                ],
                'SupportedRealtimeInferenceInstanceTypes': [
                    'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge',
                ],
                'SupportedContentTypes': [
                    'string',
                ],
                'SupportedResponseMIMETypes': [
                    'string',
                ]
            },
            'ModelPackageStatus': 'Pending'|'InProgress'|'Completed'|'Failed'|'Deleting',
            'ModelApprovalStatus': 'Approved'|'Rejected'|'PendingManualApproval'
        }
    },
    'BatchDescribeModelPackageErrorMap': {
        'string': {
            'ErrorCode': 'string',
            'ErrorResponse': 'string'
        }
    }
}

Response Structure

  • (dict) --
    • ModelPackageSummaries (dict) --

      The summaries for the model package versions

      • (string) --
        • (dict) --

          Provides summary information about the model package.

          • ModelPackageGroupName (string) --

            The group name for the model package

          • ModelPackageVersion (integer) --

            The version number of a versioned model.

          • ModelPackageArn (string) --

            The Amazon Resource Name (ARN) of the model package.

          • ModelPackageDescription (string) --

            The description of the model package.

          • CreationTime (datetime) --

            The creation time of the mortgage package summary.

          • InferenceSpecification (dict) --

            Defines how to perform inference generation after a training job is run.

            • Containers (list) --

              The Amazon ECR registry path of the Docker image that contains the inference code.

              • (dict) --

                Describes the Docker container for the model package.

                • ContainerHostname (string) --

                  The DNS host name for the Docker container.

                • Image (string) --

                  The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.

                  If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.

                • ImageDigest (string) --

                  An MD5 hash of the training algorithm that identifies the Docker image used for training.

                • ModelDataUrl (string) --

                  The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).

                  Note

                  The model artifacts must be in an S3 bucket that is in the same region as the model package.

                • ProductId (string) --

                  The Amazon Web Services Marketplace product ID of the model package.

                • Environment (dict) --

                  The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.

                  • (string) --
                    • (string) --
                • ModelInput (dict) --

                  A structure with Model Input details.

                  • DataInputConfig (string) --

                    The input configuration object for the model.

                • Framework (string) --

                  The machine learning framework of the model package container image.

                • FrameworkVersion (string) --

                  The framework version of the Model Package Container Image.

                • NearestModelName (string) --

                  The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata .

            • SupportedTransformInstanceTypes (list) --

              A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.

              This parameter is required for unversioned models, and optional for versioned models.

              • (string) --
            • SupportedRealtimeInferenceInstanceTypes (list) --

              A list of the instance types that are used to generate inferences in real-time.

              This parameter is required for unversioned models, and optional for versioned models.

              • (string) --
            • SupportedContentTypes (list) --

              The supported MIME types for the input data.

              • (string) --
            • SupportedResponseMIMETypes (list) --

              The supported MIME types for the output data.

              • (string) --
          • ModelPackageStatus (string) --

            The status of the mortgage package.

          • ModelApprovalStatus (string) --

            The approval status of the model.

    • BatchDescribeModelPackageErrorMap (dict) --

      A map of the resource and BatchDescribeModelPackageError objects reporting the error associated with describing the model package.

      • (string) --
        • (dict) --

          The error code and error description associated with the resource.

          • ErrorCode (string) --
          • ErrorResponse (string) --
can_paginate(operation_name)

Check if an operation can be paginated.

Parameters
operation_name (string) -- The operation name. This is the same name as the method name on the client. For example, if the method name is create_foo, and you'd normally invoke the operation as client.create_foo(**kwargs), if the create_foo operation can be paginated, you can use the call client.get_paginator("create_foo").
Returns
True if the operation can be paginated, False otherwise.
close()

Closes underlying endpoint connections.

create_action(**kwargs)

Creates an action . An action is a lineage tracking entity that represents an action or activity. For example, a model deployment or an HPO job. Generally, an action involves at least one input or output artifact. For more information, see Amazon SageMaker ML Lineage Tracking.

See also: AWS API Documentation

Request Syntax

response = client.create_action(
    ActionName='string',
    Source={
        'SourceUri': 'string',
        'SourceType': 'string',
        'SourceId': 'string'
    },
    ActionType='string',
    Description='string',
    Status='Unknown'|'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
    Properties={
        'string': 'string'
    },
    MetadataProperties={
        'CommitId': 'string',
        'Repository': 'string',
        'GeneratedBy': 'string',
        'ProjectId': 'string'
    },
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ]
)
Parameters
  • ActionName (string) --

    [REQUIRED]

    The name of the action. Must be unique to your account in an Amazon Web Services Region.

  • Source (dict) --

    [REQUIRED]

    The source type, ID, and URI.

    • SourceUri (string) -- [REQUIRED]

      The URI of the source.

    • SourceType (string) --

      The type of the source.

    • SourceId (string) --

      The ID of the source.

  • ActionType (string) --

    [REQUIRED]

    The action type.

  • Description (string) -- The description of the action.
  • Status (string) -- The status of the action.
  • Properties (dict) --

    A list of properties to add to the action.

    • (string) --
      • (string) --
  • MetadataProperties (dict) --

    Metadata properties of the tracking entity, trial, or trial component.

    • CommitId (string) --

      The commit ID.

    • Repository (string) --

      The repository.

    • GeneratedBy (string) --

      The entity this entity was generated by.

    • ProjectId (string) --

      The project ID.

  • Tags (list) --

    A list of tags to apply to the action.

    • (dict) --

      A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

      You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.

      For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.

      • Key (string) -- [REQUIRED]

        The tag key. Tag keys must be unique per resource.

      • Value (string) -- [REQUIRED]

        The tag value.

Return type

dict

Returns

Response Syntax

{
    'ActionArn': 'string'
}

Response Structure

  • (dict) --

    • ActionArn (string) --

      The Amazon Resource Name (ARN) of the action.

Exceptions

  • SageMaker.Client.exceptions.ResourceLimitExceeded
create_algorithm(**kwargs)

Create a machine learning algorithm that you can use in SageMaker and list in the Amazon Web Services Marketplace.

See also: AWS API Documentation

Request Syntax

response = client.create_algorithm(
    AlgorithmName='string',
    AlgorithmDescription='string',
    TrainingSpecification={
        'TrainingImage': 'string',
        'TrainingImageDigest': 'string',
        'SupportedHyperParameters': [
            {
                'Name': 'string',
                'Description': 'string',
                'Type': 'Integer'|'Continuous'|'Categorical'|'FreeText',
                'Range': {
                    'IntegerParameterRangeSpecification': {
                        'MinValue': 'string',
                        'MaxValue': 'string'
                    },
                    'ContinuousParameterRangeSpecification': {
                        'MinValue': 'string',
                        'MaxValue': 'string'
                    },
                    'CategoricalParameterRangeSpecification': {
                        'Values': [
                            'string',
                        ]
                    }
                },
                'IsTunable': True|False,
                'IsRequired': True|False,
                'DefaultValue': 'string'
            },
        ],
        'SupportedTrainingInstanceTypes': [
            'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge',
        ],
        'SupportsDistributedTraining': True|False,
        'MetricDefinitions': [
            {
                'Name': 'string',
                'Regex': 'string'
            },
        ],
        'TrainingChannels': [
            {
                'Name': 'string',
                'Description': 'string',
                'IsRequired': True|False,
                'SupportedContentTypes': [
                    'string',
                ],
                'SupportedCompressionTypes': [
                    'None'|'Gzip',
                ],
                'SupportedInputModes': [
                    'Pipe'|'File'|'FastFile',
                ]
            },
        ],
        'SupportedTuningJobObjectiveMetrics': [
            {
                'Type': 'Maximize'|'Minimize',
                'MetricName': 'string'
            },
        ]
    },
    InferenceSpecification={
        'Containers': [
            {
                'ContainerHostname': 'string',
                'Image': 'string',
                'ImageDigest': 'string',
                'ModelDataUrl': 'string',
                'ProductId': 'string',
                'Environment': {
                    'string': 'string'
                },
                'ModelInput': {
                    'DataInputConfig': 'string'
                },
                'Framework': 'string',
                'FrameworkVersion': 'string',
                'NearestModelName': 'string'
            },
        ],
        'SupportedTransformInstanceTypes': [
            'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
        ],
        'SupportedRealtimeInferenceInstanceTypes': [
            'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge',
        ],
        'SupportedContentTypes': [
            'string',
        ],
        'SupportedResponseMIMETypes': [
            'string',
        ]
    },
    ValidationSpecification={
        'ValidationRole': 'string',
        'ValidationProfiles': [
            {
                'ProfileName': 'string',
                'TrainingJobDefinition': {
                    'TrainingInputMode': 'Pipe'|'File'|'FastFile',
                    'HyperParameters': {
                        'string': 'string'
                    },
                    'InputDataConfig': [
                        {
                            'ChannelName': 'string',
                            'DataSource': {
                                'S3DataSource': {
                                    'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
                                    'S3Uri': 'string',
                                    'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
                                    'AttributeNames': [
                                        'string',
                                    ],
                                    'InstanceGroupNames': [
                                        'string',
                                    ]
                                },
                                'FileSystemDataSource': {
                                    'FileSystemId': 'string',
                                    'FileSystemAccessMode': 'rw'|'ro',
                                    'FileSystemType': 'EFS'|'FSxLustre',
                                    'DirectoryPath': 'string'
                                }
                            },
                            'ContentType': 'string',
                            'CompressionType': 'None'|'Gzip',
                            'RecordWrapperType': 'None'|'RecordIO',
                            'InputMode': 'Pipe'|'File'|'FastFile',
                            'ShuffleConfig': {
                                'Seed': 123
                            }
                        },
                    ],
                    'OutputDataConfig': {
                        'KmsKeyId': 'string',
                        'S3OutputPath': 'string'
                    },
                    'ResourceConfig': {
                        'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge',
                        'InstanceCount': 123,
                        'VolumeSizeInGB': 123,
                        'VolumeKmsKeyId': 'string',
                        'InstanceGroups': [
                            {
                                'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge',
                                'InstanceCount': 123,
                                'InstanceGroupName': 'string'
                            },
                        ],
                        'KeepAlivePeriodInSeconds': 123
                    },
                    'StoppingCondition': {
                        'MaxRuntimeInSeconds': 123,
                        'MaxWaitTimeInSeconds': 123
                    }
                },
                'TransformJobDefinition': {
                    'MaxConcurrentTransforms': 123,
                    'MaxPayloadInMB': 123,
                    'BatchStrategy': 'MultiRecord'|'SingleRecord',
                    'Environment': {
                        'string': 'string'
                    },
                    'TransformInput': {
                        'DataSource': {
                            'S3DataSource': {
                                'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
                                'S3Uri': 'string'
                            }
                        },
                        'ContentType': 'string',
                        'CompressionType': 'None'|'Gzip',
                        'SplitType': 'None'|'Line'|'RecordIO'|'TFRecord'
                    },
                    'TransformOutput': {
                        'S3OutputPath': 'string',
                        'Accept': 'string',
                        'AssembleWith': 'None'|'Line',
                        'KmsKeyId': 'string'
                    },
                    'TransformResources': {
                        'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
                        'InstanceCount': 123,
                        'VolumeKmsKeyId': 'string'
                    }
                }
            },
        ]
    },
    CertifyForMarketplace=True|False,
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ]
)
Parameters
  • AlgorithmName (string) --

    [REQUIRED]

    The name of the algorithm.

  • AlgorithmDescription (string) -- A description of the algorithm.
  • TrainingSpecification (dict) --

    [REQUIRED]

    Specifies details about training jobs run by this algorithm, including the following:

    • The Amazon ECR path of the container and the version digest of the algorithm.
    • The hyperparameters that the algorithm supports.
    • The instance types that the algorithm supports for training.
    • Whether the algorithm supports distributed training.
    • The metrics that the algorithm emits to Amazon CloudWatch.
    • Which metrics that the algorithm emits can be used as the objective metric for hyperparameter tuning jobs.
    • The input channels that the algorithm supports for training data. For example, an algorithm might support train , validation , and test channels.
    • TrainingImage (string) -- [REQUIRED]

      The Amazon ECR registry path of the Docker image that contains the training algorithm.

    • TrainingImageDigest (string) --

      An MD5 hash of the training algorithm that identifies the Docker image used for training.

    • SupportedHyperParameters (list) --

      A list of the HyperParameterSpecification objects, that define the supported hyperparameters. This is required if the algorithm supports automatic model tuning.>

      • (dict) --

        Defines a hyperparameter to be used by an algorithm.

        • Name (string) -- [REQUIRED]

          The name of this hyperparameter. The name must be unique.

        • Description (string) --

          A brief description of the hyperparameter.

        • Type (string) -- [REQUIRED]

          The type of this hyperparameter. The valid types are Integer , Continuous , Categorical , and FreeText .

        • Range (dict) --

          The allowed range for this hyperparameter.

          • IntegerParameterRangeSpecification (dict) --

            A IntegerParameterRangeSpecification object that defines the possible values for an integer hyperparameter.

            • MinValue (string) -- [REQUIRED]

              The minimum integer value allowed.

            • MaxValue (string) -- [REQUIRED]

              The maximum integer value allowed.

          • ContinuousParameterRangeSpecification (dict) --

            A ContinuousParameterRangeSpecification object that defines the possible values for a continuous hyperparameter.

            • MinValue (string) -- [REQUIRED]

              The minimum floating-point value allowed.

            • MaxValue (string) -- [REQUIRED]

              The maximum floating-point value allowed.

          • CategoricalParameterRangeSpecification (dict) --

            A CategoricalParameterRangeSpecification object that defines the possible values for a categorical hyperparameter.

            • Values (list) -- [REQUIRED]

              The allowed categories for the hyperparameter.

              • (string) --
        • IsTunable (boolean) --

          Indicates whether this hyperparameter is tunable in a hyperparameter tuning job.

        • IsRequired (boolean) --

          Indicates whether this hyperparameter is required.

        • DefaultValue (string) --

          The default value for this hyperparameter. If a default value is specified, a hyperparameter cannot be required.

    • SupportedTrainingInstanceTypes (list) -- [REQUIRED]

      A list of the instance types that this algorithm can use for training.

      • (string) --
    • SupportsDistributedTraining (boolean) --

      Indicates whether the algorithm supports distributed training. If set to false, buyers can't request more than one instance during training.

    • MetricDefinitions (list) --

      A list of MetricDefinition objects, which are used for parsing metrics generated by the algorithm.

      • (dict) --

        Specifies a metric that the training algorithm writes to stderr or stdout . SageMakerhyperparameter tuning captures all defined metrics. You specify one metric that a hyperparameter tuning job uses as its objective metric to choose the best training job.

        • Name (string) -- [REQUIRED]

          The name of the metric.

        • Regex (string) -- [REQUIRED]

          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.

    • TrainingChannels (list) -- [REQUIRED]

      A list of ChannelSpecification objects, which specify the input sources to be used by the algorithm.

      • (dict) --

        Defines a named input source, called a channel, to be used by an algorithm.

        • Name (string) -- [REQUIRED]

          The name of the channel.

        • Description (string) --

          A brief description of the channel.

        • IsRequired (boolean) --

          Indicates whether the channel is required by the algorithm.

        • SupportedContentTypes (list) -- [REQUIRED]

          The supported MIME types for the data.

          • (string) --
        • SupportedCompressionTypes (list) --

          The allowed compression types, if data compression is used.

          • (string) --
        • SupportedInputModes (list) -- [REQUIRED]

          The allowed input mode, either FILE or PIPE.

          In FILE mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode.

          In PIPE mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume.

          • (string) --

            The training input mode that the algorithm supports. For more information about input modes, see Algorithms.

            Pipe mode

            If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.

            File mode

            If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.

            You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.

            For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. 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 when one host in a training cluster is overloaded, thus becoming a bottleneck in training.

            FastFile mode

            If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.

            FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.
    • SupportedTuningJobObjectiveMetrics (list) --

      A list of the metrics that the algorithm emits that can be used as the objective metric in a hyperparameter tuning job.

      • (dict) --

        Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the Type parameter.

        • Type (string) -- [REQUIRED]

          Whether to minimize or maximize the objective metric.

        • MetricName (string) -- [REQUIRED]

          The name of the metric to use for the objective metric.

  • InferenceSpecification (dict) --

    Specifies details about inference jobs that the algorithm runs, including the following:

    • The Amazon ECR paths of containers that contain the inference code and model artifacts.
    • The instance types that the algorithm supports for transform jobs and real-time endpoints used for inference.
    • The input and output content formats that the algorithm supports for inference.
    • Containers (list) -- [REQUIRED]

      The Amazon ECR registry path of the Docker image that contains the inference code.

      • (dict) --

        Describes the Docker container for the model package.

        • ContainerHostname (string) --

          The DNS host name for the Docker container.

        • Image (string) -- [REQUIRED]

          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 SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.

        • ImageDigest (string) --

          An MD5 hash of the training algorithm that identifies the Docker image used for training.

        • ModelDataUrl (string) --

          The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).

          Note

          The model artifacts must be in an S3 bucket that is in the same region as the model package.

        • ProductId (string) --

          The Amazon Web Services Marketplace product ID of the model package.

        • Environment (dict) --

          The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.

          • (string) --
            • (string) --
        • ModelInput (dict) --

          A structure with Model Input details.

          • DataInputConfig (string) -- [REQUIRED]

            The input configuration object for the model.

        • Framework (string) --

          The machine learning framework of the model package container image.

        • FrameworkVersion (string) --

          The framework version of the Model Package Container Image.

        • NearestModelName (string) --

          The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata .

    • SupportedTransformInstanceTypes (list) --

      A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.

      This parameter is required for unversioned models, and optional for versioned models.

      • (string) --
    • SupportedRealtimeInferenceInstanceTypes (list) --

      A list of the instance types that are used to generate inferences in real-time.

      This parameter is required for unversioned models, and optional for versioned models.

      • (string) --
    • SupportedContentTypes (list) -- [REQUIRED]

      The supported MIME types for the input data.

      • (string) --
    • SupportedResponseMIMETypes (list) -- [REQUIRED]

      The supported MIME types for the output data.

      • (string) --
  • ValidationSpecification (dict) --

    Specifies configurations for one or more training jobs and that SageMaker runs to test the algorithm's training code and, optionally, one or more batch transform jobs that SageMaker runs to test the algorithm's inference code.

    • ValidationRole (string) -- [REQUIRED]

      The IAM roles that SageMaker uses to run the training jobs.

    • ValidationProfiles (list) -- [REQUIRED]

      An array of AlgorithmValidationProfile objects, each of which specifies a training job and batch transform job that SageMaker runs to validate your algorithm.

      • (dict) --

        Defines a training job and a batch transform job that SageMaker runs to validate your algorithm.

        The data provided in the validation profile is made available to your buyers on Amazon Web Services Marketplace.

        • ProfileName (string) -- [REQUIRED]

          The name of the profile for the algorithm. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).

        • TrainingJobDefinition (dict) -- [REQUIRED]

          The TrainingJobDefinition object that describes the training job that SageMaker runs to validate your algorithm.

          • TrainingInputMode (string) -- [REQUIRED]

            The training input mode that the algorithm supports. For more information about input modes, see Algorithms.

            Pipe mode

            If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.

            File mode

            If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.

            You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.

            For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. 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 when one host in a training cluster is overloaded, thus becoming a bottleneck in training.

            FastFile mode

            If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.

            FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.
          • HyperParameters (dict) --

            The hyperparameters used for the training job.

            • (string) --
              • (string) --
          • InputDataConfig (list) -- [REQUIRED]

            An array of Channel objects, each of which specifies an input source.

            • (dict) --

              A channel is a named input source that training algorithms can consume.

              • ChannelName (string) -- [REQUIRED]

                The name of the channel.

              • DataSource (dict) -- [REQUIRED]

                The location of the channel data.

                • S3DataSource (dict) --

                  The S3 location of the data source that is associated with a channel.

                  • S3DataType (string) -- [REQUIRED]

                    If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.

                    If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.

                    If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .

                  • S3Uri (string) -- [REQUIRED]

                    Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:

                    • A key name prefix might look like this: s3://bucketname/exampleprefix
                    • A manifest might look like this: s3://bucketname/example.manifest A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri . Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets. The following code example shows a valid manifest format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] This JSON is equivalent to the following S3Uri list: s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
                  • S3DataDistributionType (string) --

                    If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .

                    If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.

                    Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.

                    In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.

                  • AttributeNames (list) --

                    A list of one or more attribute names to use that are found in a specified augmented manifest file.

                    • (string) --
                  • InstanceGroupNames (list) --

                    A list of names of instance groups that get data from the S3 data source.

                    • (string) --
                • FileSystemDataSource (dict) --

                  The file system that is associated with a channel.

                  • FileSystemId (string) -- [REQUIRED]

                    The file system id.

                  • FileSystemAccessMode (string) -- [REQUIRED]

                    The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.

                  • FileSystemType (string) -- [REQUIRED]

                    The file system type.

                  • DirectoryPath (string) -- [REQUIRED]

                    The full path to the directory to associate with the channel.

              • ContentType (string) --

                The MIME type of the data.

              • CompressionType (string) --

                If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.

              • RecordWrapperType (string) --

                Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO.

                In File mode, leave this field unset or set it to None.

              • InputMode (string) --

                (Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.

                To use a model for incremental training, choose File input model.

              • ShuffleConfig (dict) --

                A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.

                For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.

                • Seed (integer) -- [REQUIRED]

                  Determines the shuffling order in ShuffleConfig value.

          • OutputDataConfig (dict) -- [REQUIRED]

            the path to the S3 bucket where you want to store model artifacts. SageMaker creates subfolders for the artifacts.

            • KmsKeyId (string) --

              The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:

              • // KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
              • // Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
              • // KMS Key Alias "alias/ExampleAlias"
              • // Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"

              If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig . If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.

              The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .

            • S3OutputPath (string) -- [REQUIRED]

              Identifies the S3 path where you want SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .

          • ResourceConfig (dict) -- [REQUIRED]

            The resources, including the ML compute instances and ML storage volumes, to use for model training.

            • InstanceType (string) --

              The ML compute instance type.

            • InstanceCount (integer) --

              The number of ML compute instances to use. For distributed training, provide a value greater than 1.

            • VolumeSizeInGB (integer) -- [REQUIRED]

              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.

              When using an ML instance with NVMe SSD volumes, SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include ml.p4d , ml.g4dn , and ml.g5 .

              When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through VolumeSizeInGB in the ResourceConfig API. For example, ML instance families that use EBS volumes include ml.c5 and ml.p2 .

              To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types.

              To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs.

            • VolumeKmsKeyId (string) --

              The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.

              Note

              Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.

              For a list of instance types that support local instance storage, see Instance Store Volumes.

              For more information about local instance storage encryption, see SSD Instance Store Volumes.

              The VolumeKmsKeyId can be in any of the following formats:

              • // KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
              • // Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
            • InstanceGroups (list) --

              The configuration of a heterogeneous cluster in JSON format.

              • (dict) --

                Defines an instance group for heterogeneous cluster training. When requesting a training job using the CreateTrainingJob API, you can configure multiple instance groups .

                • InstanceType (string) -- [REQUIRED]

                  Specifies the instance type of the instance group.

                • InstanceCount (integer) -- [REQUIRED]

                  Specifies the number of instances of the instance group.

                • InstanceGroupName (string) -- [REQUIRED]

                  Specifies the name of the instance group.

            • KeepAlivePeriodInSeconds (integer) --

              The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.

          • StoppingCondition (dict) -- [REQUIRED]

            Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.

            To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts.

            • MaxRuntimeInSeconds (integer) --

              The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.

              For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.

              For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.

              The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.

            • MaxWaitTimeInSeconds (integer) --

              The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, SageMaker ends the job.

              When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.

        • TransformJobDefinition (dict) --

          The TransformJobDefinition object that describes the transform job that SageMaker runs to validate your algorithm.

          • MaxConcurrentTransforms (integer) --

            The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1.

          • MaxPayloadInMB (integer) --

            The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata).

          • BatchStrategy (string) --

            A string that determines the number of records included in a single mini-batch.

            SingleRecord means only one record is used per mini-batch. MultiRecord means a mini-batch is set to contain as many records that can fit within the MaxPayloadInMB limit.
          • Environment (dict) --

            The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.

            • (string) --
              • (string) --
          • TransformInput (dict) -- [REQUIRED]

            A description of the input source and the way the transform job consumes it.

            • DataSource (dict) -- [REQUIRED]

              Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.

              • S3DataSource (dict) -- [REQUIRED]

                The S3 location of the data source that is associated with a channel.

                • S3DataType (string) -- [REQUIRED]

                  If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.

                  If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.

                  The following values are compatible: ManifestFile , S3Prefix

                  The following value is not compatible: AugmentedManifestFile

                • S3Uri (string) -- [REQUIRED]

                  Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:

                  • A key name prefix might look like this: s3://bucketname/exampleprefix .
                  • A manifest might look like this: s3://bucketname/example.manifest The manifest is an S3 object which is a JSON file with the following format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] The preceding JSON matches the following S3Uris : s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uris in this manifest constitutes the input data for the channel for this datasource. The object that each S3Uris points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.
            • ContentType (string) --

              The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.

            • CompressionType (string) --

              If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None .

            • SplitType (string) --

              The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None , which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:

              • RecordIO
              • TFRecord

              When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord , Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord , Amazon SageMaker sends individual records in each request.

              Note

              Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord . Padding is not removed if the value of BatchStrategy is set to MultiRecord .

              For more information about RecordIO , see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord , see Consuming TFRecord data in the TensorFlow documentation.

          • TransformOutput (dict) -- [REQUIRED]

            Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.

            • S3OutputPath (string) -- [REQUIRED]

              The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, s3://bucket-name/key-name-prefix .

              For every S3 object used as input for the transform job, batch transform stores the transformed data with an .``out`` suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at s3://bucket-name/input-name-prefix/dataset01/data.csv , batch transform stores the transformed data at s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out . Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .``out`` file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.

            • Accept (string) --

              The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.

            • AssembleWith (string) --

              Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify None . To add a newline character at the end of every transformed record, specify Line .

            • KmsKeyId (string) --

              The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:

              • Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
              • Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
              • Alias name: alias/ExampleAlias
              • Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias

              If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.

              The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .

          • TransformResources (dict) -- [REQUIRED]

            Identifies the ML compute instances for the transform job.

            • InstanceType (string) -- [REQUIRED]

              The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or ml.m5.large instance types.

            • InstanceCount (integer) -- [REQUIRED]

              The number of ML compute instances to use in the transform job. The default value is 1 , and the maximum is 100 . For distributed transform jobs, specify a value greater than 1 .

            • VolumeKmsKeyId (string) --

              The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.

              Note

              Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.

              For a list of instance types that support local instance storage, see Instance Store Volumes.

              For more information about local instance storage encryption, see SSD Instance Store Volumes.

              The VolumeKmsKeyId can be any of the following formats:

              • Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
              • Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
              • Alias name: alias/ExampleAlias
              • Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
  • CertifyForMarketplace (boolean) -- Whether to certify the algorithm so that it can be listed in Amazon Web Services Marketplace.
  • Tags (list) --

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

    • (dict) --

      A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

      You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.

      For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.

      • Key (string) -- [REQUIRED]

        The tag key. Tag keys must be unique per resource.

      • Value (string) -- [REQUIRED]

        The tag value.

Return type

dict

Returns

Response Syntax

{
    'AlgorithmArn': 'string'
}

Response Structure

  • (dict) --

    • AlgorithmArn (string) --

      The Amazon Resource Name (ARN) of the new algorithm.

create_app(**kwargs)

Creates a running app for the specified UserProfile. This operation is automatically invoked by Amazon SageMaker Studio upon access to the associated Domain, and when new kernel configurations are selected by the user. A user may have multiple Apps active simultaneously.

See also: AWS API Documentation

Request Syntax

response = client.create_app(
    DomainId='string',
    UserProfileName='string',
    AppType='JupyterServer'|'KernelGateway'|'TensorBoard'|'RStudioServerPro'|'RSessionGateway',
    AppName='string',
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ],
    ResourceSpec={
        'SageMakerImageArn': 'string',
        'SageMakerImageVersionArn': 'string',
        'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge',
        'LifecycleConfigArn': 'string'
    },
    SpaceName='string'
)
Parameters
  • DomainId (string) --

    [REQUIRED]

    The domain ID.

  • UserProfileName (string) -- The user profile name.
  • AppType (string) --

    [REQUIRED]

    The type of app.

  • AppName (string) --

    [REQUIRED]

    The name of the app.

  • Tags (list) --

    Each tag consists of a key and an optional value. Tag keys must be unique per resource.

    • (dict) --

      A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

      You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.

      For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.

      • Key (string) -- [REQUIRED]

        The tag key. Tag keys must be unique per resource.

      • Value (string) -- [REQUIRED]

        The tag value.

  • ResourceSpec (dict) --

    The instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.

    Note

    The value of InstanceType passed as part of the ResourceSpec in the CreateApp call overrides the value passed as part of the ResourceSpec configured for the user profile or the domain. If InstanceType is not specified in any of those three ResourceSpec values for a KernelGateway app, the CreateApp call fails with a request validation error.

    • SageMakerImageArn (string) --

      The ARN of the SageMaker image that the image version belongs to.

    • SageMakerImageVersionArn (string) --

      The ARN of the image version created on the instance.

    • InstanceType (string) --

      The instance type that the image version runs on.

      Note

      JupyterServer apps only support the system value.

      For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

    • LifecycleConfigArn (string) --

      The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

  • SpaceName (string) -- The name of the space.
Return type

dict

Returns

Response Syntax

{
    'AppArn': 'string'
}

Response Structure

  • (dict) --

    • AppArn (string) --

      The Amazon Resource Name (ARN) of the app.

Exceptions

  • SageMaker.Client.exceptions.ResourceLimitExceeded
  • SageMaker.Client.exceptions.ResourceInUse
create_app_image_config(**kwargs)

Creates a configuration for running a SageMaker image as a KernelGateway app. The configuration specifies the Amazon Elastic File System (EFS) storage volume on the image, and a list of the kernels in the image.

See also: AWS API Documentation

Request Syntax

response = client.create_app_image_config(
    AppImageConfigName='string',
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ],
    KernelGatewayImageConfig={
        'KernelSpecs': [
            {
                'Name': 'string',
                'DisplayName': 'string'
            },
        ],
        'FileSystemConfig': {
            'MountPath': 'string',
            'DefaultUid': 123,
            'DefaultGid': 123
        }
    }
)
Parameters
  • AppImageConfigName (string) --

    [REQUIRED]

    The name of the AppImageConfig. Must be unique to your account.

  • Tags (list) --

    A list of tags to apply to the AppImageConfig.

    • (dict) --

      A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

      You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.

      For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.

      • Key (string) -- [REQUIRED]

        The tag key. Tag keys must be unique per resource.

      • Value (string) -- [REQUIRED]

        The tag value.

  • KernelGatewayImageConfig (dict) --

    The KernelGatewayImageConfig. You can only specify one image kernel in the AppImageConfig API. This kernel will be shown to users before the image starts. Once the image runs, all kernels are visible in JupyterLab.

    • KernelSpecs (list) -- [REQUIRED]

      The specification of the Jupyter kernels in the image.

      • (dict) --

        The specification of a Jupyter kernel.

        • Name (string) -- [REQUIRED]

          The name of the Jupyter kernel in the image. This value is case sensitive.

        • DisplayName (string) --

          The display name of the kernel.

    • FileSystemConfig (dict) --

      The Amazon Elastic File System (EFS) storage configuration for a SageMaker image.

      • MountPath (string) --

        The path within the image to mount the user's EFS home directory. The directory should be empty. If not specified, defaults to /home/sagemaker-user .

      • DefaultUid (integer) --

        The default POSIX user ID (UID). If not specified, defaults to 1000 .

      • DefaultGid (integer) --

        The default POSIX group ID (GID). If not specified, defaults to 100 .

Return type

dict

Returns

Response Syntax

{
    'AppImageConfigArn': 'string'
}

Response Structure

  • (dict) --

    • AppImageConfigArn (string) --

      The Amazon Resource Name (ARN) of the AppImageConfig.

Exceptions

  • SageMaker.Client.exceptions.ResourceInUse
create_artifact(**kwargs)

Creates an artifact . An artifact is a lineage tracking entity that represents a URI addressable object or data. Some examples are the S3 URI of a dataset and the ECR registry path of an image. For more information, see Amazon SageMaker ML Lineage Tracking.

See also: AWS API Documentation

Request Syntax

response = client.create_artifact(
    ArtifactName='string',
    Source={
        'SourceUri': 'string',
        'SourceTypes': [
            {
                'SourceIdType': 'MD5Hash'|'S3ETag'|'S3Version'|'Custom',
                'Value': 'string'
            },
        ]
    },
    ArtifactType='string',
    Properties={
        'string': 'string'
    },
    MetadataProperties={
        'CommitId': 'string',
        'Repository': 'string',
        'GeneratedBy': 'string',
        'ProjectId': 'string'
    },
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ]
)
Parameters
  • ArtifactName (string) -- The name of the artifact. Must be unique to your account in an Amazon Web Services Region.
  • Source (dict) --

    [REQUIRED]

    The ID, ID type, and URI of the source.

    • SourceUri (string) -- [REQUIRED]

      The URI of the source.

    • SourceTypes (list) --

      A list of source types.

      • (dict) --

        The ID and ID type of an artifact source.

        • SourceIdType (string) -- [REQUIRED]

          The type of ID.

        • Value (string) -- [REQUIRED]

          The ID.

  • ArtifactType (string) --

    [REQUIRED]

    The artifact type.

  • Properties (dict) --

    A list of properties to add to the artifact.

    • (string) --
      • (string) --
  • MetadataProperties (dict) --

    Metadata properties of the tracking entity, trial, or trial component.

    • CommitId (string) --

      The commit ID.

    • Repository (string) --

      The repository.

    • GeneratedBy (string) --

      The entity this entity was generated by.

    • ProjectId (string) --

      The project ID.

  • Tags (list) --

    A list of tags to apply to the artifact.

    • (dict) --

      A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

      You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.

      For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.

      • Key (string) -- [REQUIRED]

        The tag key. Tag keys must be unique per resource.

      • Value (string) -- [REQUIRED]

        The tag value.

Return type

dict

Returns

Response Syntax

{
    'ArtifactArn': 'string'
}

Response Structure

  • (dict) --

    • ArtifactArn (string) --

      The Amazon Resource Name (ARN) of the artifact.

Exceptions

  • SageMaker.Client.exceptions.ResourceLimitExceeded
create_auto_ml_job(**kwargs)

Creates an Autopilot job.

Find the best-performing model after you run an Autopilot job by calling .

For information about how to use Autopilot, see Automate Model Development with Amazon SageMaker Autopilot.

See also: AWS API Documentation

Request Syntax

response = client.create_auto_ml_job(
    AutoMLJobName='string',
    InputDataConfig=[
        {
            'DataSource': {
                'S3DataSource': {
                    'S3DataType': 'ManifestFile'|'S3Prefix',
                    'S3Uri': 'string'
                }
            },
            'CompressionType': 'None'|'Gzip',
            'TargetAttributeName': 'string',
            'ContentType': 'string',
            'ChannelType': 'training'|'validation'
        },
    ],
    OutputDataConfig={
        'KmsKeyId': 'string',
        'S3OutputPath': 'string'
    },
    ProblemType='BinaryClassification'|'MulticlassClassification'|'Regression',
    AutoMLJobObjective={
        'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'
    },
    AutoMLJobConfig={
        'CompletionCriteria': {
            'MaxCandidates': 123,
            'MaxRuntimePerTrainingJobInSeconds': 123,
            'MaxAutoMLJobRuntimeInSeconds': 123
        },
        'SecurityConfig': {
            'VolumeKmsKeyId': 'string',
            'EnableInterContainerTrafficEncryption': True|False,
            'VpcConfig': {
                'SecurityGroupIds': [
                    'string',
                ],
                'Subnets': [
                    'string',
                ]
            }
        },
        'DataSplitConfig': {
            'ValidationFraction': ...
        },
        'CandidateGenerationConfig': {
            'FeatureSpecificationS3Uri': 'string'
        },
        'Mode': 'AUTO'|'ENSEMBLING'|'HYPERPARAMETER_TUNING'
    },
    RoleArn='string',
    GenerateCandidateDefinitionsOnly=True|False,
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ],
    ModelDeployConfig={
        'AutoGenerateEndpointName': True|False,
        'EndpointName': 'string'
    }
)
Parameters
  • AutoMLJobName (string) --

    [REQUIRED]

    Identifies an Autopilot job. The name must be unique to your account and is case-insensitive.

  • InputDataConfig (list) --

    [REQUIRED]

    An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to InputDataConfig supported by . Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.

    • (dict) --

      A channel is a named input source that training algorithms can consume. The validation dataset size is limited to less than 2 GB. The training dataset size must be less than 100 GB. For more information, see .

      Note

      A validation dataset must contain the same headers as the training dataset.

      • DataSource (dict) -- [REQUIRED]

        The data source for an AutoML channel.

        • S3DataSource (dict) -- [REQUIRED]

          The Amazon S3 location of the input data.

          • S3DataType (string) -- [REQUIRED]

            The data type.

            A ManifestFile should have the format shown below:

            [ {"prefix": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/DOC-EXAMPLE-PREFIX/"},

            "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-1",

            "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-2",

            ... "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-N" ]

            An S3Prefix should have the following format:

            s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER-OR-FILE
          • S3Uri (string) -- [REQUIRED]

            The URL to the Amazon S3 data source.

      • CompressionType (string) --

        You can use Gzip or None . The default value is None .

      • TargetAttributeName (string) -- [REQUIRED]

        The name of the target variable in supervised learning, usually represented by 'y'.

      • ContentType (string) --

        The content type of the data from the input source. You can use text/csv;header=present or x-application/vnd.amazon+parquet . The default value is text/csv;header=present .

      • ChannelType (string) --

        The channel type (optional) is an enum string. The default value is training . Channels for training and validation must share the same ContentType and TargetAttributeName . For information on specifying training and validation channel types, see ` How to specify training and validation datasets https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-datasets-problem-types.html#autopilot-data-sources-training-or-validation`__.

  • OutputDataConfig (dict) --

    [REQUIRED]

    Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.

    • KmsKeyId (string) --

      The Key Management Service (KMS) encryption key ID.

    • S3OutputPath (string) -- [REQUIRED]

      The Amazon S3 output path. Must be 128 characters or less.

  • ProblemType (string) -- Defines the type of supervised learning available for the candidates. For more information, see Amazon SageMaker Autopilot problem types and algorithm support.
  • AutoMLJobObjective (dict) --

    Defines the objective metric used to measure the predictive quality of an AutoML job. You provide an AutoMLJobObjective$MetricName and Autopilot infers whether to minimize or maximize it.

    • MetricName (string) -- [REQUIRED]

      The name of the objective metric used to measure the predictive quality of a machine learning system. This metric is optimized during training to provide the best estimate for model parameter values from data.

      Here are the options:

      • MSE : The mean squared error (MSE) is the average of the squared differences between the predicted and actual values. It is used for regression. MSE values are always positive: the better a model is at predicting the actual values, the smaller the MSE value is. When the data contains outliers, they tend to dominate the MSE, which might cause subpar prediction performance.
      • Accuracy : The ratio of the number of correctly classified items to the total number of (correctly and incorrectly) classified items. It is used for binary and multiclass classification. It measures how close the predicted class values are to the actual values. Accuracy values vary between zero and one: one indicates perfect accuracy and zero indicates perfect inaccuracy.
      • F1 : The F1 score is the harmonic mean of the precision and recall. It is used for binary classification into classes traditionally referred to as positive and negative. Predictions are said to be true when they match their actual (correct) class and false when they do not. Precision is the ratio of the true positive predictions to all positive predictions (including the false positives) in a data set and measures the quality of the prediction when it predicts the positive class. Recall (or sensitivity) is the ratio of the true positive predictions to all actual positive instances and measures how completely a model predicts the actual class members in a data set. The standard F1 score weighs precision and recall equally. But which metric is paramount typically depends on specific aspects of a problem. F1 scores vary between zero and one: one indicates the best possible performance and zero the worst.
      • AUC : The area under the curve (AUC) metric is used to compare and evaluate binary classification by algorithms such as logistic regression that return probabilities. A threshold is needed to map the probabilities into classifications. The relevant curve is the receiver operating characteristic curve that plots the true positive rate (TPR) of predictions (or recall) against the false positive rate (FPR) as a function of the threshold value, above which a prediction is considered positive. Increasing the threshold results in fewer false positives but more false negatives. AUC is the area under this receiver operating characteristic curve and so provides an aggregated measure of the model performance across all possible classification thresholds. The AUC score can also be interpreted as the probability that a randomly selected positive data point is more likely to be predicted positive than a randomly selected negative example. AUC scores vary between zero and one: a score of one indicates perfect accuracy and a score of one half indicates that the prediction is not better than a random classifier. Values under one half predict less accurately than a random predictor. But such consistently bad predictors can simply be inverted to obtain better than random predictors.
      • F1macro : The F1macro score applies F1 scoring to multiclass classification. In this context, you have multiple classes to predict. You just calculate the precision and recall for each class as you did for the positive class in binary classification. Then, use these values to calculate the F1 score for each class and average them to obtain the F1macro score. F1macro scores vary between zero and one: one indicates the best possible performance and zero the worst.

      If you do not specify a metric explicitly, the default behavior is to automatically use:

      • MSE : for regression.
      • F1 : for binary classification
      • Accuracy : for multiclass classification.
  • AutoMLJobConfig (dict) --

    A collection of settings used to configure an AutoML job.

    • CompletionCriteria (dict) --

      How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.

      • MaxCandidates (integer) --

        The maximum number of times a training job is allowed to run.

      • MaxRuntimePerTrainingJobInSeconds (integer) --

        The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the used by the action.

      • MaxAutoMLJobRuntimeInSeconds (integer) --

        The maximum runtime, in seconds, an AutoML job has to complete.

        If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, will not be completed.

    • SecurityConfig (dict) --

      The security configuration for traffic encryption or Amazon VPC settings.

      • VolumeKmsKeyId (string) --

        The key used to encrypt stored data.

      • EnableInterContainerTrafficEncryption (boolean) --

        Whether to use traffic encryption between the container layers.

      • VpcConfig (dict) --

        The VPC configuration.

        • SecurityGroupIds (list) -- [REQUIRED]

          The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.

          • (string) --
        • Subnets (list) -- [REQUIRED]

          The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.

          • (string) --
    • DataSplitConfig (dict) --

      The configuration for splitting the input training dataset.

      Type: AutoMLDataSplitConfig

      • ValidationFraction (float) --

        The validation fraction (optional) is a float that specifies the portion of the training dataset to be used for validation. The default value is 0.2, and values must be greater than 0 and less than 1. We recommend setting this value to be less than 0.5.

    • CandidateGenerationConfig (dict) --

      The configuration for generating a candidate for an AutoML job (optional).

      • FeatureSpecificationS3Uri (string) --

        A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job. You can input FeatureAttributeNames (optional) in JSON format as shown below:

        { "FeatureAttributeNames":["col1", "col2", ...] } .

        You can also specify the data type of the feature (optional) in the format shown below:

        { "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }

        Note

        These column keys may not include the target column.

        In ensembling mode, Autopilot will only support the following data types: numeric , categorical , text and datetime . In HPO mode, Autopilot can support numeric , categorical , text , datetime and sequence .

        If only FeatureDataTypes is provided, the column keys (col1 , col2 ,..) should be a subset of the column names in the input data.

        If both FeatureDataTypes and FeatureAttributeNames are provided, then the column keys should be a subset of the column names provided in FeatureAttributeNames .

        The key name FeatureAttributeNames is fixed. The values listed in ["col1", "col2", ...] is case sensitive and should be a list of strings containing unique values that are a subset of the column names in the input data. The list of columns provided must not include the target column.

    • Mode (string) --

      The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO . In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.

      The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.

      The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO will automatically select an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.

  • RoleArn (string) --

    [REQUIRED]

    The ARN of the role that is used to access the data.

  • GenerateCandidateDefinitionsOnly (boolean) -- Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.
  • Tags (list) --

    Each tag consists of a key and an optional value. Tag keys must be unique per resource.

    • (dict) --

      A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

      You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.

      For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.

      • Key (string) -- [REQUIRED]

        The tag key. Tag keys must be unique per resource.

      • Value (string) -- [REQUIRED]

        The tag value.

  • ModelDeployConfig (dict) --

    Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.

    • AutoGenerateEndpointName (boolean) --

      Set to True to automatically generate an endpoint name for a one-click Autopilot model deployment; set to False otherwise. The default value is False .

      Note

      If you set AutoGenerateEndpointName to True , do not specify the EndpointName ; otherwise a 400 error is thrown.

    • EndpointName (string) --

      Specifies the endpoint name to use for a one-click Autopilot model deployment if the endpoint name is not generated automatically.

      Note

      Specify the EndpointName if and only if you set AutoGenerateEndpointName to False ; otherwise a 400 error is thrown.

Return type

dict

Returns

Response Syntax

{
    'AutoMLJobArn': 'string'
}

Response Structure

  • (dict) --

    • AutoMLJobArn (string) --

      The unique ARN assigned to the AutoML job when it is created.

Exceptions

  • SageMaker.Client.exceptions.ResourceInUse
  • SageMaker.Client.exceptions.ResourceLimitExceeded
create_code_repository(**kwargs)

Creates a Git repository as a resource in your SageMaker account. You can associate the repository with notebook instances so that you can use Git source control for the notebooks you create. The Git repository is a resource in your SageMaker account, so it can be associated with more than one notebook instance, and it persists independently from the lifecycle of any notebook instances it is associated with.

The repository can be hosted either in Amazon Web Services CodeCommit or in any other Git repository.

See also: AWS API Documentation

Request Syntax

response = client.create_code_repository(
    CodeRepositoryName='string',
    GitConfig={
        'RepositoryUrl': 'string',
        'Branch': 'string',
        'SecretArn': 'string'
    },
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ]
)
Parameters
  • CodeRepositoryName (string) --

    [REQUIRED]

    The name of the Git repository. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).

  • GitConfig (dict) --

    [REQUIRED]

    Specifies details about the repository, including the URL where the repository is located, the default branch, and credentials to use to access the repository.

    • RepositoryUrl (string) -- [REQUIRED]

      The URL where the Git repository is located.

    • Branch (string) --

      The default branch for the Git repository.

    • SecretArn (string) --

      The Amazon Resource Name (ARN) of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the git repository. The secret must have a staging label of AWSCURRENT and must be in the following format:

      {"username": *UserName* , "password": *Password* }
  • Tags (list) --

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

    • (dict) --

      A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

      You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.

      For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.

      • Key (string) -- [REQUIRED]

        The tag key. Tag keys must be unique per resource.

      • Value (string) -- [REQUIRED]

        The tag value.

Return type

dict

Returns

Response Syntax

{
    'CodeRepositoryArn': 'string'
}

Response Structure

  • (dict) --

    • CodeRepositoryArn (string) --

      The Amazon Resource Name (ARN) of the new repository.

create_compilation_job(**kwargs)

Starts a model compilation job. After the model has been compiled, Amazon SageMaker saves the resulting model artifacts to an Amazon Simple Storage Service (Amazon S3) bucket that you specify.

If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts with Amazon Web Services IoT Greengrass. In that case, deploy them as an ML resource.

In the request body, you provide the following:

  • A name for the compilation job
  • Information about the input model artifacts
  • The output location for the compiled model and the device (target) that the model runs on
  • The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker assumes to perform the model compilation job.

You can also provide a Tag to track the model compilation job's resource use and costs. The response body contains the CompilationJobArn for the compiled job.

To stop a model compilation job, use StopCompilationJob. To get information about a particular model compilation job, use DescribeCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.

See also: AWS API Documentation

Request Syntax

response = client.create_compilation_job(
    CompilationJobName='string',
    RoleArn='string',
    ModelPackageVersionArn='string',
    InputConfig={
        'S3Uri': 'string',
        'DataInputConfig': 'string',
        'Framework': 'TENSORFLOW'|'KERAS'|'MXNET'|'ONNX'|'PYTORCH'|'XGBOOST'|'TFLITE'|'DARKNET'|'SKLEARN',
        'FrameworkVersion': 'string'
    },
    OutputConfig={
        'S3OutputLocation': 'string',
        'TargetDevice': 'lambda'|'ml_m4'|'ml_m5'|'ml_c4'|'ml_c5'|'ml_p2'|'ml_p3'|'ml_g4dn'|'ml_inf1'|'ml_eia2'|'jetson_tx1'|'jetson_tx2'|'jetson_nano'|'jetson_xavier'|'rasp3b'|'imx8qm'|'deeplens'|'rk3399'|'rk3288'|'aisage'|'sbe_c'|'qcs605'|'qcs603'|'sitara_am57x'|'amba_cv2'|'amba_cv22'|'amba_cv25'|'x86_win32'|'x86_win64'|'coreml'|'jacinto_tda4vm'|'imx8mplus',
        'TargetPlatform': {
            'Os': 'ANDROID'|'LINUX',
            'Arch': 'X86_64'|'X86'|'ARM64'|'ARM_EABI'|'ARM_EABIHF',
            'Accelerator': 'INTEL_GRAPHICS'|'MALI'|'NVIDIA'|'NNA'
        },
        'CompilerOptions': 'string',
        'KmsKeyId': 'string'
    },
    VpcConfig={
        'SecurityGroupIds': [
            'string',
        ],
        'Subnets': [
            'string',
        ]
    },
    StoppingCondition={
        'MaxRuntimeInSeconds': 123,
        'MaxWaitTimeInSeconds': 123
    },
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ]
)
Parameters
  • CompilationJobName (string) --

    [REQUIRED]

    A name for the model compilation job. The name must be unique within the Amazon Web Services Region and within your Amazon Web Services account.

  • RoleArn (string) --

    [REQUIRED]

    The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.

    During model compilation, Amazon SageMaker needs your permission to:

    • Read input data from an S3 bucket
    • Write model artifacts to an S3 bucket
    • Write logs to Amazon CloudWatch Logs
    • Publish metrics to Amazon CloudWatch

    You grant permissions for all of these tasks to an IAM role. To pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission. For more information, see Amazon SageMaker Roles.

  • ModelPackageVersionArn (string) -- The Amazon Resource Name (ARN) of a versioned model package. Provide either a ModelPackageVersionArn or an InputConfig object in the request syntax. The presence of both objects in the CreateCompilationJob request will return an exception.
  • InputConfig (dict) --

    Provides information about the location of input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.

    • S3Uri (string) -- [REQUIRED]

      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).

    • DataInputConfig (string) -- [REQUIRED]

      Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. The data inputs are InputConfig$Framework specific.

      • TensorFlow : You must specify the name and shape (NHWC format) of the expected data inputs using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.
        • Examples for one input:
          • If using the console, {"input":[1,1024,1024,3]}
          • If using the CLI, {\"input\":[1,1024,1024,3]}
        • Examples for two inputs:
          • If using the console, {"data1": [1,28,28,1], "data2":[1,28,28,1]}
          • If using the CLI, {\"data1\": [1,28,28,1], \"data2\":[1,28,28,1]}
      • KERAS : You must specify the name and shape (NCHW format) of expected data inputs using a dictionary format for your trained model. Note that while Keras model artifacts should be uploaded in NHWC (channel-last) format, DataInputConfig should be specified in NCHW (channel-first) format. The dictionary formats required for the console and CLI are different.
        • Examples for one input:
          • If using the console, {"input_1":[1,3,224,224]}
          • If using the CLI, {\"input_1\":[1,3,224,224]}
        • Examples for two inputs:
          • If using the console, {"input_1": [1,3,224,224], "input_2":[1,3,224,224]}
          • If using the CLI, {\"input_1\": [1,3,224,224], \"input_2\":[1,3,224,224]}
      • MXNET/ONNX/DARKNET : You must specify the name and shape (NCHW format) of the expected data inputs in order using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.
        • Examples for one input:
          • If using the console, {"data":[1,3,1024,1024]}
          • If using the CLI, {\"data\":[1,3,1024,1024]}
        • Examples for two inputs:
          • If using the console, {"var1": [1,1,28,28], "var2":[1,1,28,28]}
          • If using the CLI, {\"var1\": [1,1,28,28], \"var2\":[1,1,28,28]}
      • PyTorch : You can either specify the name and shape (NCHW format) of expected data inputs in order using a dictionary format for your trained model or you can specify the shape only using a list format. The dictionary formats required for the console and CLI are different. The list formats for the console and CLI are the same.
        • Examples for one input in dictionary format:
          • If using the console, {"input0":[1,3,224,224]}
          • If using the CLI, {\"input0\":[1,3,224,224]}
        • Example for one input in list format: [[1,3,224,224]]
        • Examples for two inputs in dictionary format:
          • If using the console, {"input0":[1,3,224,224], "input1":[1,3,224,224]}
          • If using the CLI, {\"input0\":[1,3,224,224], \"input1\":[1,3,224,224]}
        • Example for two inputs in list format: [[1,3,224,224], [1,3,224,224]]
      • XGBOOST : input data name and shape are not needed.
      DataInputConfig supports the following parameters for CoreML OutputConfig$TargetDevice (ML Model format):
      • shape : Input shape, for example {"input_1": {"shape": [1,224,224,3]}} . In addition to static input shapes, CoreML converter supports Flexible input shapes:
        • Range Dimension. You can use the Range Dimension feature if you know the input shape will be within some specific interval in that dimension, for example: {"input_1": {"shape": ["1..10", 224, 224, 3]}}
        • Enumerated shapes. Sometimes, the models are trained to work only on a select set of inputs. You can enumerate all supported input shapes, for example: {"input_1": {"shape": [[1, 224, 224, 3], [1, 160, 160, 3]]}}
      • default_shape : Default input shape. You can set a default shape during conversion for both Range Dimension and Enumerated Shapes. For example {"input_1": {"shape": ["1..10", 224, 224, 3], "default_shape": [1, 224, 224, 3]}}
      • type : Input type. Allowed values: Image and Tensor . By default, the converter generates an ML Model with inputs of type Tensor (MultiArray). User can set input type to be Image. Image input type requires additional input parameters such as bias and scale .
      • bias : If the input type is an Image, you need to provide the bias vector.
      • scale : If the input type is an Image, you need to provide a scale factor.

      CoreML ClassifierConfig parameters can be specified using OutputConfig$CompilerOptions. CoreML converter supports Tensorflow and PyTorch models. CoreML conversion examples:

      • Tensor type input:
        • "DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3]}}
      • Tensor type input without input name (PyTorch):
        • "DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224]}]
      • Image type input:
        • "DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}}
        • "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}
      • Image type input without input name (PyTorch):
        • "DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}]
        • "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}

      Depending on the model format, DataInputConfig requires the following parameters for ml_eia2 OutputConfig:TargetDevice.

    • Framework (string) -- [REQUIRED]

      Identifies the framework in which the model was trained. For example: TENSORFLOW.

    • FrameworkVersion (string) --

      Specifies the framework version to use. This API field is only supported for the MXNet, PyTorch, TensorFlow and TensorFlow Lite frameworks.

      For information about framework versions supported for cloud targets and edge devices, see Cloud Supported Instance Types and Frameworks and Edge Supported Frameworks.

  • OutputConfig (dict) --

    [REQUIRED]

    Provides information about the output location for the compiled model and the target device the model runs on.

    • S3OutputLocation (string) -- [REQUIRED]

      Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .

    • TargetDevice (string) --

      Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed. Alternatively, you can specify OS, architecture, and accelerator using TargetPlatform fields. It can be used instead of TargetPlatform .

    • TargetPlatform (dict) --

      Contains information about a target platform that you want your model to run on, such as OS, architecture, and accelerators. It is an alternative of TargetDevice .

      The following examples show how to configure the TargetPlatform and CompilerOptions JSON strings for popular target platforms:

      • Raspberry Pi 3 Model B+ "TargetPlatform": {"Os": "LINUX", "Arch": "ARM_EABIHF"}, "CompilerOptions": {'mattr': ['+neon']}
      • Jetson TX2 "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "NVIDIA"}, "CompilerOptions": {'gpu-code': 'sm_62', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'}
      • EC2 m5.2xlarge instance OS "TargetPlatform": {"Os": "LINUX", "Arch": "X86_64", "Accelerator": "NVIDIA"}, "CompilerOptions": {'mcpu': 'skylake-avx512'}
      • RK3399 "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "MALI"}
      • ARMv7 phone (CPU) "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM_EABI"}, "CompilerOptions": {'ANDROID_PLATFORM': 25, 'mattr': ['+neon']}
      • ARMv8 phone (CPU) "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM64"}, "CompilerOptions": {'ANDROID_PLATFORM': 29}
      • Os (string) -- [REQUIRED]

        Specifies a target platform OS.

        • LINUX : Linux-based operating systems.
        • ANDROID : Android operating systems. Android API level can be specified using the ANDROID_PLATFORM compiler option. For example, "CompilerOptions": {'ANDROID_PLATFORM': 28}
      • Arch (string) -- [REQUIRED]

        Specifies a target platform architecture.

        • X86_64 : 64-bit version of the x86 instruction set.
        • X86 : 32-bit version of the x86 instruction set.
        • ARM64 : ARMv8 64-bit CPU.
        • ARM_EABIHF : ARMv7 32-bit, Hard Float.
        • ARM_EABI : ARMv7 32-bit, Soft Float. Used by Android 32-bit ARM platform.
      • Accelerator (string) --

        Specifies a target platform accelerator (optional).

        • NVIDIA : Nvidia graphics processing unit. It also requires gpu-code , trt-ver , cuda-ver compiler options
        • MALI : ARM Mali graphics processor
        • INTEL_GRAPHICS : Integrated Intel graphics
    • CompilerOptions (string) --

      Specifies additional parameters for compiler options in JSON format. The compiler options are TargetPlatform specific. It is required for NVIDIA accelerators and highly recommended for CPU compilations. For any other cases, it is optional to specify CompilerOptions.

      • DTYPE : Specifies the data type for the input. When compiling for ml_* (except for ml_inf ) instances using PyTorch framework, provide the data type (dtype) of the model's input. "float32" is used if "DTYPE" is not specified. Options for data type are:
        • float32: Use either "float" or "float32" .
        • int64: Use either "int64" or "long" .

      For example, {"dtype" : "float32"} .

      • CPU : Compilation for CPU supports the following compiler options.
        • mcpu : CPU micro-architecture. For example, {'mcpu': 'skylake-avx512'}
        • mattr : CPU flags. For example, {'mattr': ['+neon', '+vfpv4']}
      • ARM : Details of ARM CPU compilations.
        • NEON : NEON is an implementation of the Advanced SIMD extension used in ARMv7 processors. For example, add {'mattr': ['+neon']} to the compiler options if compiling for ARM 32-bit platform with the NEON support.
      • NVIDIA : Compilation for NVIDIA GPU supports the following compiler options.
        • gpu_code : Specifies the targeted architecture.
        • trt-ver : Specifies the TensorRT versions in x.y.z. format.
        • cuda-ver : Specifies the CUDA version in x.y format.

      For example, {'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver': '10.1'}

      • ANDROID : Compilation for the Android OS supports the following compiler options:
        • ANDROID_PLATFORM : Specifies the Android API levels. Available levels range from 21 to 29. For example, {'ANDROID_PLATFORM': 28} .
        • mattr : Add {'mattr': ['+neon']} to compiler options if compiling for ARM 32-bit platform with NEON support.
      • INFERENTIA : Compilation for target ml_inf1 uses compiler options passed in as a JSON string. For example, "CompilerOptions": "\"--verbose 1 --num-neuroncores 2 -O2\"" . For information about supported compiler options, see Neuron Compiler CLI.
      • CoreML : Compilation for the CoreML OutputConfig$TargetDevice supports the following compiler options:
        • class_labels : Specifies the classification labels file name inside input tar.gz file. For example, {"class_labels": "imagenet_labels_1000.txt"} . Labels inside the txt file should be separated by newlines.
      • EIA : Compilation for the Elastic Inference Accelerator supports the following compiler options:
        • precision_mode : Specifies the precision of compiled artifacts. Supported values are "FP16" and "FP32" . Default is "FP32" .
        • signature_def_key : Specifies the signature to use for models in SavedModel format. Defaults is TensorFlow's default signature def key.
        • output_names : Specifies a list of output tensor names for models in FrozenGraph format. Set at most one API field, either: signature_def_key or output_names .

      For example: {"precision_mode": "FP32", "output_names": ["output:0"]}

    • KmsKeyId (string) --

      The Amazon Web Services Key Management Service key (Amazon Web Services KMS) that Amazon SageMaker uses to encrypt your output models with Amazon S3 server-side encryption after compilation job. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.

      The KmsKeyId can be any of the following formats:

      • Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
      • Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
      • Alias name: alias/ExampleAlias
      • Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
  • VpcConfig (dict) --

    A VpcConfig object that specifies the VPC that you want your compilation job to connect to. Control access to your models by configuring the VPC. For more information, see Protect Compilation Jobs by Using an Amazon Virtual Private Cloud.

    • SecurityGroupIds (list) -- [REQUIRED]

      The VPC security group IDs. IDs have the form of sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.

      • (string) --
    • Subnets (list) -- [REQUIRED]

      The ID of the subnets in the VPC that you want to connect the compilation job to for accessing the model in Amazon S3.

      • (string) --
  • StoppingCondition (dict) --

    [REQUIRED]

    Specifies a limit to how long a model compilation job can run. When the job reaches the time limit, Amazon SageMaker ends the compilation job. Use this API to cap model training costs.

    • MaxRuntimeInSeconds (integer) --

      The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.

      For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.

      For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.

      The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.

    • MaxWaitTimeInSeconds (integer) --

      The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, SageMaker ends the job.

      When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.

  • Tags (list) --

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

    • (dict) --

      A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

      You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.

      For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.

      • Key (string) -- [REQUIRED]

        The tag key. Tag keys must be unique per resource.

      • Value (string) -- [REQUIRED]

        The tag value.

Return type

dict

Returns

Response Syntax

{
    'CompilationJobArn': 'string'
}

Response Structure

  • (dict) --

    • CompilationJobArn (string) --

      If the action is successful, the service sends back an HTTP 200 response. Amazon SageMaker returns the following data in JSON format:

      • CompilationJobArn : The Amazon Resource Name (ARN) of the compiled job.

Exceptions

  • SageMaker.Client.exceptions.ResourceInUse
  • SageMaker.Client.exceptions.ResourceLimitExceeded
create_context(**kwargs)

Creates a context . A context is a lineage tracking entity that represents a logical grouping of other tracking or experiment entities. Some examples are an endpoint and a model package. For more information, see Amazon SageMaker ML Lineage Tracking.

See also: AWS API Documentation

Request Syntax

response = client.create_context(
    ContextName='string',
    Source={
        'SourceUri': 'string',
        'SourceType': 'string',
        'SourceId': 'string'
    },
    ContextType='string',
    Description='string',
    Properties={
        'string': 'string'
    },
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ]
)
Parameters
  • ContextName (string) --

    [REQUIRED]

    The name of the context. Must be unique to your account in an Amazon Web Services Region.

  • Source (dict) --

    [REQUIRED]

    The source type, ID, and URI.

    • SourceUri (string) -- [REQUIRED]

      The URI of the source.

    • SourceType (string) --

      The type of the source.

    • SourceId (string) --

      The ID of the source.

  • ContextType (string) --

    [REQUIRED]

    The context type.

  • Description (string) -- The description of the context.
  • Properties (dict) --

    A list of properties to add to the context.

    • (string) --
      • (string) --
  • Tags (list) --

    A list of tags to apply to the context.

    • (dict) --

      A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

      You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.

      For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.

      • Key (string) -- [REQUIRED]

        The tag key. Tag keys must be unique per resource.

      • Value (string) -- [REQUIRED]

        The tag value.

Return type

dict

Returns

Response Syntax

{
    'ContextArn': 'string'
}

Response Structure

  • (dict) --

    • ContextArn (string) --

      The Amazon Resource Name (ARN) of the context.

Exceptions

  • SageMaker.Client.exceptions.ResourceLimitExceeded
create_data_quality_job_definition(**kwargs)

Creates a definition for a job that monitors data quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor.

See also: AWS API Documentation

Request Syntax

response = client.create_data_quality_job_definition(
    JobDefinitionName='string',
    DataQualityBaselineConfig={
        'BaseliningJobName': 'string',
        'ConstraintsResource': {
            'S3Uri': 'string'
        },
        'StatisticsResource': {
            'S3Uri': 'string'
        }
    },
    DataQualityAppSpecification={
        'ImageUri': 'string',
        'ContainerEntrypoint': [
            'string',
        ],
        'ContainerArguments': [
            'string',
        ],
        'RecordPreprocessorSourceUri': 'string',
        'PostAnalyticsProcessorSourceUri': 'string',
        'Environment': {
            'string': 'string'
        }
    },
    DataQualityJobInput={
        'EndpointInput': {
            'EndpointName': 'string',
            'LocalPath': 'string',
            'S3InputMode': 'Pipe'|'File',
            'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
            'FeaturesAttribute': 'string',
            'InferenceAttribute': 'string',
            'ProbabilityAttribute': 'string',
            'ProbabilityThresholdAttribute': 123.0,
            'StartTimeOffset': 'string',
            'EndTimeOffset': 'string'
        },
        'BatchTransformInput': {
            'DataCapturedDestinationS3Uri': 'string',
            'DatasetFormat': {
                'Csv': {
                    'Header': True|False
                },
                'Json': {
                    'Line': True|False
                },
                'Parquet': {}

            },
            'LocalPath': 'string',
            'S3InputMode': 'Pipe'|'File',
            'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
            'FeaturesAttribute': 'string',
            'InferenceAttribute': 'string',
            'ProbabilityAttribute': 'string',
            'ProbabilityThresholdAttribute': 123.0,
            'StartTimeOffset': 'string',
            'EndTimeOffset': 'string'
        }
    },
    DataQualityJobOutputConfig={
        'MonitoringOutputs': [
            {
                'S3Output': {
                    'S3Uri': 'string',
                    'LocalPath': 'string',
                    'S3UploadMode': 'Continuous'|'EndOfJob'
                }
            },
        ],
        'KmsKeyId': 'string'
    },
    JobResources={
        'ClusterConfig': {
            'InstanceCount': 123,
            'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
            'VolumeSizeInGB': 123,
            'VolumeKmsKeyId': 'string'
        }
    },
    NetworkConfig={
        'EnableInterContainerTrafficEncryption': True|False,
        'EnableNetworkIsolation': True|False,
        'VpcConfig': {
            'SecurityGroupIds': [
                'string',
            ],
            'Subnets': [
                'string',
            ]
        }
    },
    RoleArn='string',
    StoppingCondition={
        'MaxRuntimeInSeconds': 123
    },
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ]
)
Parameters
  • JobDefinitionName (string) --

    [REQUIRED]

    The name for the monitoring job definition.

  • DataQualityBaselineConfig (dict) --

    Configures the constraints and baselines for the monitoring job.

    • BaseliningJobName (string) --

      The name of the job that performs baselining for the data quality monitoring job.

    • ConstraintsResource (dict) --

      The constraints resource for a monitoring job.

      • S3Uri (string) --

        The Amazon S3 URI for the constraints resource.

    • StatisticsResource (dict) --

      The statistics resource for a monitoring job.

      • S3Uri (string) --

        The Amazon S3 URI for the statistics resource.

  • DataQualityAppSpecification (dict) --

    [REQUIRED]

    Specifies the container that runs the monitoring job.

    • ImageUri (string) -- [REQUIRED]

      The container image that the data quality monitoring job runs.

    • ContainerEntrypoint (list) --

      The entrypoint for a container used to run a monitoring job.

      • (string) --
    • ContainerArguments (list) --

      The arguments to send to the container that the monitoring job runs.

      • (string) --
    • RecordPreprocessorSourceUri (string) --

      An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flatted json so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.

    • PostAnalyticsProcessorSourceUri (string) --

      An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.

    • Environment (dict) --

      Sets the environment variables in the container that the monitoring job runs.

      • (string) --
        • (string) --
  • DataQualityJobInput (dict) --

    [REQUIRED]

    A list of inputs for the monitoring job. Currently endpoints are supported as monitoring inputs.

    • EndpointInput (dict) --

      Input object for the endpoint

      • EndpointName (string) -- [REQUIRED]

        An endpoint in customer's account which has enabled DataCaptureConfig enabled.

      • LocalPath (string) -- [REQUIRED]

        Path to the filesystem where the endpoint data is available to the container.

      • S3InputMode (string) --

        Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .

      • S3DataDistributionType (string) --

        Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated

      • FeaturesAttribute (string) --

        The attributes of the input data that are the input features.

      • InferenceAttribute (string) --

        The attribute of the input data that represents the ground truth label.

      • ProbabilityAttribute (string) --

        In a classification problem, the attribute that represents the class probability.

      • ProbabilityThresholdAttribute (float) --

        The threshold for the class probability to be evaluated as a positive result.

      • StartTimeOffset (string) --

        If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

      • EndTimeOffset (string) --

        If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

    • BatchTransformInput (dict) --

      Input object for the batch transform job.

      • DataCapturedDestinationS3Uri (string) -- [REQUIRED]

        The Amazon S3 location being used to capture the data.

      • DatasetFormat (dict) -- [REQUIRED]

        The dataset format for your batch transform job.

        • Csv (dict) --

          The CSV dataset used in the monitoring job.

          • Header (boolean) --

            Indicates if the CSV data has a header.

        • Json (dict) --

          The JSON dataset used in the monitoring job

          • Line (boolean) --

            Indicates if the file should be read as a json object per line.

        • Parquet (dict) --

          The Parquet dataset used in the monitoring job

      • LocalPath (string) -- [REQUIRED]

        Path to the filesystem where the batch transform data is available to the container.

      • S3InputMode (string) --

        Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .

      • S3DataDistributionType (string) --

        Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated

      • FeaturesAttribute (string) --

        The attributes of the input data that are the input features.

      • InferenceAttribute (string) --

        The attribute of the input data that represents the ground truth label.

      • ProbabilityAttribute (string) --

        In a classification problem, the attribute that represents the class probability.

      • ProbabilityThresholdAttribute (float) --

        The threshold for the class probability to be evaluated as a positive result.

      • StartTimeOffset (string) --

        If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

      • EndTimeOffset (string) --

        If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

  • DataQualityJobOutputConfig (dict) --

    [REQUIRED]

    The output configuration for monitoring jobs.

    • MonitoringOutputs (list) -- [REQUIRED]

      Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.

      • (dict) --

        The output object for a monitoring job.

        • S3Output (dict) -- [REQUIRED]

          The Amazon S3 storage location where the results of a monitoring job are saved.

          • S3Uri (string) -- [REQUIRED]

            A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.

          • LocalPath (string) -- [REQUIRED]

            The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.

          • S3UploadMode (string) --

            Whether to upload the results of the monitoring job continuously or after the job completes.

    • KmsKeyId (string) --

      The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.

  • JobResources (dict) --

    [REQUIRED]

    Identifies the resources to deploy for a monitoring job.

    • ClusterConfig (dict) -- [REQUIRED]

      The configuration for the cluster resources used to run the processing job.

      • InstanceCount (integer) -- [REQUIRED]

        The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.

      • InstanceType (string) -- [REQUIRED]

        The ML compute instance type for the processing job.

      • VolumeSizeInGB (integer) -- [REQUIRED]

        The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.

      • VolumeKmsKeyId (string) --

        The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job.

  • NetworkConfig (dict) --

    Specifies networking configuration for the monitoring job.

    • EnableInterContainerTrafficEncryption (boolean) --

      Whether to encrypt all communications between the instances used for the monitoring jobs. Choose True to encrypt communications. Encryption provides greater security for distributed jobs, but the processing might take longer.

    • EnableNetworkIsolation (boolean) --

      Whether to allow inbound and outbound network calls to and from the containers used for the monitoring job.

    • VpcConfig (dict) --

      Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud.

      • SecurityGroupIds (list) -- [REQUIRED]

        The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.

        • (string) --
      • Subnets (list) -- [REQUIRED]

        The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.

        • (string) --
  • RoleArn (string) --

    [REQUIRED]

    The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.

  • StoppingCondition (dict) --

    A time limit for how long the monitoring job is allowed to run before stopping.

    • MaxRuntimeInSeconds (integer) -- [REQUIRED]

      The maximum runtime allowed in seconds.

      Note

      The MaxRuntimeInSeconds cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.

  • Tags (list) --

    (Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide .

    • (dict) --

      A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

      You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.

      For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.

      • Key (string) -- [REQUIRED]

        The tag key. Tag keys must be unique per resource.

      • Value (string) -- [REQUIRED]

        The tag value.

Return type

dict

Returns

Response Syntax

{
    'JobDefinitionArn': 'string'
}

Response Structure

  • (dict) --

    • JobDefinitionArn (string) --

      The Amazon Resource Name (ARN) of the job definition.

Exceptions

  • SageMaker.Client.exceptions.ResourceLimitExceeded
  • SageMaker.Client.exceptions.ResourceInUse
create_device_fleet(**kwargs)

Creates a device fleet.

See also: AWS API Documentation

Request Syntax

response = client.create_device_fleet(
    DeviceFleetName='string',
    RoleArn='string',
    Description='string',
    OutputConfig={
        'S3OutputLocation': 'string',
        'KmsKeyId': 'string',
        'PresetDeploymentType': 'GreengrassV2Component',
        'PresetDeploymentConfig': 'string'
    },
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ],
    EnableIotRoleAlias=True|False
)
Parameters
  • DeviceFleetName (string) --

    [REQUIRED]

    The name of the fleet that the device belongs to.

  • RoleArn (string) -- The Amazon Resource Name (ARN) that has access to Amazon Web Services Internet of Things (IoT).
  • Description (string) -- A description of the fleet.
  • OutputConfig (dict) --

    [REQUIRED]

    The output configuration for storing sample data collected by the fleet.

    • S3OutputLocation (string) -- [REQUIRED]

      The Amazon Simple Storage (S3) bucker URI.

    • KmsKeyId (string) --

      The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume after compilation job. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account.

    • PresetDeploymentType (string) --

      The deployment type SageMaker Edge Manager will create. Currently only supports Amazon Web Services IoT Greengrass Version 2 components.

    • PresetDeploymentConfig (string) --

      The configuration used to create deployment artifacts. Specify configuration options with a JSON string. The available configuration options for each type are:

      • ComponentName (optional) - Name of the GreenGrass V2 component. If not specified, the default name generated consists of "SagemakerEdgeManager" and the name of your SageMaker Edge Manager packaging job.
      • ComponentDescription (optional) - Description of the component.
      • ComponentVersion (optional) - The version of the component.

      Note

      Amazon Web Services IoT Greengrass uses semantic versions for components. Semantic versions follow a*major.minor.patch* number system. For example, version 1.0.0 represents the first major release for a component. For more information, see the semantic version specification.

      • PlatformOS (optional) - The name of the operating system for the platform. Supported platforms include Windows and Linux.
      • PlatformArchitecture (optional) - The processor architecture for the platform. Supported architectures Windows include: Windows32_x86, Windows64_x64. Supported architectures for Linux include: Linux x86_64, Linux ARMV8.
  • Tags (list) --

    Creates tags for the specified fleet.

    • (dict) --

      A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

      You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.

      For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.

      • Key (string) -- [REQUIRED]

        The tag key. Tag keys must be unique per resource.

      • Value (string) -- [REQUIRED]

        The tag value.

  • EnableIotRoleAlias (boolean) --

    Whether to create an Amazon Web Services IoT Role Alias during device fleet creation. The name of the role alias generated will match this pattern: "SageMakerEdge-{DeviceFleetName}".

    For example, if your device fleet is called "demo-fleet", the name of the role alias will be "SageMakerEdge-demo-fleet".

Returns

None

Exceptions

  • SageMaker.Client.exceptions.ResourceInUse
  • SageMaker.Client.exceptions.ResourceLimitExceeded
create_domain(**kwargs)

Creates a Domain used by Amazon SageMaker Studio. A domain consists of an associated Amazon Elastic File System (EFS) volume, a list of authorized users, and a variety of security, application, policy, and Amazon Virtual Private Cloud (VPC) configurations. An Amazon Web Services account is limited to one domain per region. Users within a domain can share notebook files and other artifacts with each other.

EFS storage

When a domain is created, an EFS volume is created for use by all of the users within the domain. Each user receives a private home directory within the EFS volume for notebooks, Git repositories, and data files.

SageMaker uses the Amazon Web Services Key Management Service (Amazon Web Services KMS) to encrypt the EFS volume attached to the domain with an Amazon Web Services managed key by default. For more control, you can specify a customer managed key. For more information, see Protect Data at Rest Using Encryption.

VPC configuration

All SageMaker Studio traffic between the domain and the EFS volume is through the specified VPC and subnets. For other Studio traffic, you can specify the AppNetworkAccessType parameter. AppNetworkAccessType corresponds to the network access type that you choose when you onboard to Studio. The following options are available:

  • PublicInternetOnly - Non-EFS traffic goes through a VPC managed by Amazon SageMaker, which allows internet access. This is the default value.
  • VpcOnly - All Studio traffic is through the specified VPC and subnets. Internet access is disabled by default. To allow internet access, you must specify a NAT gateway. When internet access is disabled, you won't be able to run a Studio notebook or to train or host models unless your VPC has an interface endpoint to the SageMaker API and runtime or a NAT gateway and your security groups allow outbound connections.

Warning

NFS traffic over TCP on port 2049 needs to be allowed in both inbound and outbound rules in order to launch a SageMaker Studio app successfully.

For more information, see Connect SageMaker Studio Notebooks to Resources in a VPC.

See also: AWS API Documentation

Request Syntax

response = client.create_domain(
    DomainName='string',
    AuthMode='SSO'|'IAM',
    DefaultUserSettings={
        'ExecutionRole': 'string',
        'SecurityGroups': [
            'string',
        ],
        'SharingSettings': {
            'NotebookOutputOption': 'Allowed'|'Disabled',
            'S3OutputPath': 'string',
            'S3KmsKeyId': 'string'
        },
        'JupyterServerAppSettings': {
            'DefaultResourceSpec': {
                'SageMakerImageArn': 'string',
                'SageMakerImageVersionArn': 'string',
                'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge',
                'LifecycleConfigArn': 'string'
            },
            'LifecycleConfigArns': [
                'string',
            ],
            'CodeRepositories': [
                {
                    'RepositoryUrl': 'string'
                },
            ]
        },
        'KernelGatewayAppSettings': {
            'DefaultResourceSpec': {
                'SageMakerImageArn': 'string',
                'SageMakerImageVersionArn': 'string',
                'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge',
                'LifecycleConfigArn': 'string'
            },
            'CustomImages': [
                {
                    'ImageName': 'string',
                    'ImageVersionNumber': 123,
                    'AppImageConfigName': 'string'
                },
            ],
            'LifecycleConfigArns': [
                'string',
            ]
        },
        'TensorBoardAppSettings': {
            'DefaultResourceSpec': {
                'SageMakerImageArn': 'string',
                'SageMakerImageVersionArn': 'string',
                'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge',
                'LifecycleConfigArn': 'string'
            }
        },
        'RStudioServerProAppSettings': {
            'AccessStatus': 'ENABLED'|'DISABLED',
            'UserGroup': 'R_STUDIO_ADMIN'|'R_STUDIO_USER'
        },
        'RSessionAppSettings': {
            'DefaultResourceSpec': {
                'SageMakerImageArn': 'string',
                'SageMakerImageVersionArn': 'string',
                'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge',
                'LifecycleConfigArn': 'string'
            },
            'CustomImages': [
                {
                    'ImageName': 'string',
                    'ImageVersionNumber': 123,
                    'AppImageConfigName': 'string'
                },
            ]
        },
        'CanvasAppSettings': {
            'TimeSeriesForecastingSettings': {
                'Status': 'ENABLED'|'DISABLED',
                'AmazonForecastRoleArn': 'string'
            }
        }
    },
    SubnetIds=[
        'string',
    ],
    VpcId='string',
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ],
    AppNetworkAccessType='PublicInternetOnly'|'VpcOnly',
    HomeEfsFileSystemKmsKeyId='string',
    KmsKeyId='string',
    AppSecurityGroupManagement='Service'|'Customer',
    DomainSettings={
        'SecurityGroupIds': [
            'string',
        ],
        'RStudioServerProDomainSettings': {
            'DomainExecutionRoleArn': 'string',
            'RStudioConnectUrl': 'string',
            'RStudioPackageManagerUrl': 'string',
            'DefaultResourceSpec': {
                'SageMakerImageArn': 'string',
                'SageMakerImageVersionArn': 'string',
                'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge',
                'LifecycleConfigArn': 'string'
            }
        },
        'ExecutionRoleIdentityConfig': 'USER_PROFILE_NAME'|'DISABLED'
    },
    DefaultSpaceSettings={
        'ExecutionRole': 'string',
        'SecurityGroups': [
            'string',
        ],
        'JupyterServerAppSettings': {
            'DefaultResourceSpec': {
                'SageMakerImageArn': 'string',
                'SageMakerImageVersionArn': 'string',
                'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge',
                'LifecycleConfigArn': 'string'
            },
            'LifecycleConfigArns': [
                'string',
            ],
            'CodeRepositories': [
                {
                    'RepositoryUrl': 'string'
                },
            ]
        },
        'KernelGatewayAppSettings': {
            'DefaultResourceSpec': {
                'SageMakerImageArn': 'string',
                'SageMakerImageVersionArn': 'string',
                'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge',
                'LifecycleConfigArn': 'string'
            },
            'CustomImages': [
                {
                    'ImageName': 'string',
                    'ImageVersionNumber': 123,
                    'AppImageConfigName': 'string'
                },
            ],
            'LifecycleConfigArns': [
                'string',
            ]
        }
    }
)
Parameters
  • DomainName (string) --

    [REQUIRED]

    A name for the domain.

  • AuthMode (string) --

    [REQUIRED]

    The mode of authentication that members use to access the domain.

  • DefaultUserSettings (dict) --

    [REQUIRED]

    The default settings to use to create a user profile when UserSettings isn't specified in the call to the CreateUserProfile API.

    SecurityGroups is aggregated when specified in both calls. For all other settings in UserSettings , the values specified in CreateUserProfile take precedence over those specified in CreateDomain .
    • ExecutionRole (string) --

      The execution role for the user.

    • SecurityGroups (list) --

      The security groups for the Amazon Virtual Private Cloud (VPC) that Studio uses for communication.

      Optional when the CreateDomain.AppNetworkAccessType parameter is set to PublicInternetOnly .

      Required when the CreateDomain.AppNetworkAccessType parameter is set to VpcOnly .

      Amazon SageMaker adds a security group to allow NFS traffic from SageMaker Studio. Therefore, the number of security groups that you can specify is one less than the maximum number shown.

      • (string) --
    • SharingSettings (dict) --

      Specifies options for sharing SageMaker Studio notebooks.

      • NotebookOutputOption (string) --

        Whether to include the notebook cell output when sharing the notebook. The default is Disabled .

      • S3OutputPath (string) --

        When NotebookOutputOption is Allowed , the Amazon S3 bucket used to store the shared notebook snapshots.

      • S3KmsKeyId (string) --

        When NotebookOutputOption is Allowed , the Amazon Web Services Key Management Service (KMS) encryption key ID used to encrypt the notebook cell output in the Amazon S3 bucket.

    • JupyterServerAppSettings (dict) --

      The Jupyter server's app settings.

      • DefaultResourceSpec (dict) --

        The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app. If you use the LifecycleConfigArns parameter, then this parameter is also required.

        • SageMakerImageArn (string) --

          The ARN of the SageMaker image that the image version belongs to.

        • SageMakerImageVersionArn (string) --

          The ARN of the image version created on the instance.

        • InstanceType (string) --

          The instance type that the image version runs on.

          Note

          JupyterServer apps only support the system value.

          For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

        • LifecycleConfigArn (string) --

          The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

      • LifecycleConfigArns (list) --

        The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the DefaultResourceSpec parameter is also required.

        Note

        To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.

        • (string) --
      • CodeRepositories (list) --

        A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterServer application.

        • (dict) --

          A Git repository that SageMaker automatically displays to users for cloning in the JupyterServer application.

          • RepositoryUrl (string) -- [REQUIRED]

            The URL of the Git repository.

    • KernelGatewayAppSettings (dict) --

      The kernel gateway app settings.

      • DefaultResourceSpec (dict) --

        The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.

        Note

        The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the Amazon Web Services Command Line Interface or Amazon Web Services CloudFormation and the instance type parameter value is not passed.

        • SageMakerImageArn (string) --

          The ARN of the SageMaker image that the image version belongs to.

        • SageMakerImageVersionArn (string) --

          The ARN of the image version created on the instance.

        • InstanceType (string) --

          The instance type that the image version runs on.

          Note

          JupyterServer apps only support the system value.

          For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

        • LifecycleConfigArn (string) --

          The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

      • CustomImages (list) --

        A list of custom SageMaker images that are configured to run as a KernelGateway app.

        • (dict) --

          A custom SageMaker image. For more information, see Bring your own SageMaker image.

          • ImageName (string) -- [REQUIRED]

            The name of the CustomImage. Must be unique to your account.

          • ImageVersionNumber (integer) --

            The version number of the CustomImage.

          • AppImageConfigName (string) -- [REQUIRED]

            The name of the AppImageConfig.

      • LifecycleConfigArns (list) --

        The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.

        Note

        To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.

        • (string) --
    • TensorBoardAppSettings (dict) --

      The TensorBoard app settings.

      • DefaultResourceSpec (dict) --

        The default instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.

        • SageMakerImageArn (string) --

          The ARN of the SageMaker image that the image version belongs to.

        • SageMakerImageVersionArn (string) --

          The ARN of the image version created on the instance.

        • InstanceType (string) --

          The instance type that the image version runs on.

          Note

          JupyterServer apps only support the system value.

          For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

        • LifecycleConfigArn (string) --

          The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

    • RStudioServerProAppSettings (dict) --

      A collection of settings that configure user interaction with the RStudioServerPro app.

      • AccessStatus (string) --

        Indicates whether the current user has access to the RStudioServerPro app.

      • UserGroup (string) --

        The level of permissions that the user has within the RStudioServerPro app. This value defaults to User. The Admin value allows the user access to the RStudio Administrative Dashboard.

    • RSessionAppSettings (dict) --

      A collection of settings that configure the RSessionGateway app.

      • DefaultResourceSpec (dict) --

        Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.

        • SageMakerImageArn (string) --

          The ARN of the SageMaker image that the image version belongs to.

        • SageMakerImageVersionArn (string) --

          The ARN of the image version created on the instance.

        • InstanceType (string) --

          The instance type that the image version runs on.

          Note

          JupyterServer apps only support the system value.

          For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

        • LifecycleConfigArn (string) --

          The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

      • CustomImages (list) --

        A list of custom SageMaker images that are configured to run as a RSession app.

        • (dict) --

          A custom SageMaker image. For more information, see Bring your own SageMaker image.

          • ImageName (string) -- [REQUIRED]

            The name of the CustomImage. Must be unique to your account.

          • ImageVersionNumber (integer) --

            The version number of the CustomImage.

          • AppImageConfigName (string) -- [REQUIRED]

            The name of the AppImageConfig.

    • CanvasAppSettings (dict) --

      The Canvas app settings.

      • TimeSeriesForecastingSettings (dict) --

        Time series forecast settings for the Canvas app.

        • Status (string) --

          Describes whether time series forecasting is enabled or disabled in the Canvas app.

        • AmazonForecastRoleArn (string) --

          The IAM role that Canvas passes to Amazon Forecast for time series forecasting. By default, Canvas uses the execution role specified in the UserProfile that launches the Canvas app. If an execution role is not specified in the UserProfile , Canvas uses the execution role specified in the Domain that owns the UserProfile . To allow time series forecasting, this IAM role should have the AmazonSageMakerCanvasForecastAccess policy attached and forecast.amazonaws.com added in the trust relationship as a service principal.

  • SubnetIds (list) --

    [REQUIRED]

    The VPC subnets that Studio uses for communication.

    • (string) --
  • VpcId (string) --

    [REQUIRED]

    The ID of the Amazon Virtual Private Cloud (VPC) that Studio uses for communication.

  • Tags (list) --

    Tags to associated with the Domain. Each tag consists of a key and an optional value. Tag keys must be unique per resource. Tags are searchable using the Search API.

    Tags that you specify for the Domain are also added to all Apps that the Domain launches.

    • (dict) --

      A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

      You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.

      For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.

      • Key (string) -- [REQUIRED]

        The tag key. Tag keys must be unique per resource.

      • Value (string) -- [REQUIRED]

        The tag value.

  • AppNetworkAccessType (string) --

    Specifies the VPC used for non-EFS traffic. The default value is PublicInternetOnly .

    • PublicInternetOnly - Non-EFS traffic is through a VPC managed by Amazon SageMaker, which allows direct internet access
    • VpcOnly - All Studio traffic is through the specified VPC and subnets
  • HomeEfsFileSystemKmsKeyId (string) -- Use KmsKeyId .
  • KmsKeyId (string) -- SageMaker uses Amazon Web Services KMS to encrypt the EFS volume attached to the domain with an Amazon Web Services managed key by default. For more control, specify a customer managed key.
  • AppSecurityGroupManagement (string) -- The entity that creates and manages the required security groups for inter-app communication in VPCOnly mode. Required when CreateDomain.AppNetworkAccessType is VPCOnly and DomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn is provided.
  • DomainSettings (dict) --

    A collection of Domain settings.

    • SecurityGroupIds (list) --

      The security groups for the Amazon Virtual Private Cloud that the Domain uses for communication between Domain-level apps and user apps.

      • (string) --
    • RStudioServerProDomainSettings (dict) --

      A collection of settings that configure the RStudioServerPro Domain-level app.

      • DomainExecutionRoleArn (string) -- [REQUIRED]

        The ARN of the execution role for the RStudioServerPro Domain-level app.

      • RStudioConnectUrl (string) --

        A URL pointing to an RStudio Connect server.

      • RStudioPackageManagerUrl (string) --

        A URL pointing to an RStudio Package Manager server.

      • DefaultResourceSpec (dict) --

        Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.

        • SageMakerImageArn (string) --

          The ARN of the SageMaker image that the image version belongs to.

        • SageMakerImageVersionArn (string) --

          The ARN of the image version created on the instance.

        • InstanceType (string) --

          The instance type that the image version runs on.

          Note

          JupyterServer apps only support the system value.

          For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

        • LifecycleConfigArn (string) --

          The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

    • ExecutionRoleIdentityConfig (string) --

      The configuration for attaching a SageMaker user profile name to the execution role as a sts:SourceIdentity key.

  • DefaultSpaceSettings (dict) --

    The default settings used to create a space.

    • ExecutionRole (string) --

      The execution role for the space.

    • SecurityGroups (list) --

      The security groups for the Amazon Virtual Private Cloud that the space uses for communication.

      • (string) --
    • JupyterServerAppSettings (dict) --

      The JupyterServer app settings.

      • DefaultResourceSpec (dict) --

        The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app. If you use the LifecycleConfigArns parameter, then this parameter is also required.

        • SageMakerImageArn (string) --

          The ARN of the SageMaker image that the image version belongs to.

        • SageMakerImageVersionArn (string) --

          The ARN of the image version created on the instance.

        • InstanceType (string) --

          The instance type that the image version runs on.

          Note

          JupyterServer apps only support the system value.

          For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

        • LifecycleConfigArn (string) --

          The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

      • LifecycleConfigArns (list) --

        The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the DefaultResourceSpec parameter is also required.

        Note

        To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.

        • (string) --
      • CodeRepositories (list) --

        A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterServer application.

        • (dict) --

          A Git repository that SageMaker automatically displays to users for cloning in the JupyterServer application.

          • RepositoryUrl (string) -- [REQUIRED]

            The URL of the Git repository.

    • KernelGatewayAppSettings (dict) --

      The KernelGateway app settings.

      • DefaultResourceSpec (dict) --

        The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.

        Note

        The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the Amazon Web Services Command Line Interface or Amazon Web Services CloudFormation and the instance type parameter value is not passed.

        • SageMakerImageArn (string) --

          The ARN of the SageMaker image that the image version belongs to.

        • SageMakerImageVersionArn (string) --

          The ARN of the image version created on the instance.

        • InstanceType (string) --

          The instance type that the image version runs on.

          Note

          JupyterServer apps only support the system value.

          For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.

        • LifecycleConfigArn (string) --

          The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.

      • CustomImages (list) --

        A list of custom SageMaker images that are configured to run as a KernelGateway app.

        • (dict) --

          A custom SageMaker image. For more information, see Bring your own SageMaker image.

          • ImageName (string) -- [REQUIRED]

            The name of the CustomImage. Must be unique to your account.

          • ImageVersionNumber (integer) --

            The version number of the CustomImage.

          • AppImageConfigName (string) -- [REQUIRED]

            The name of the AppImageConfig.

      • LifecycleConfigArns (list) --

        The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.

        Note

        To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.

        • (string) --
Return type

dict

Returns

Response Syntax

{
    'DomainArn': 'string',
    'Url': 'string'
}

Response Structure

  • (dict) --

    • DomainArn (string) --

      The Amazon Resource Name (ARN) of the created domain.

    • Url (string) --

      The URL to the created domain.

Exceptions

  • SageMaker.Client.exceptions.ResourceLimitExceeded
  • SageMaker.Client.exceptions.ResourceInUse
create_edge_deployment_plan(**kwargs)

Creates an edge deployment plan, consisting of multiple stages. Each stage may have a different deployment configuration and devices.

See also: AWS API Documentation

Request Syntax

response = client.create_edge_deployment_plan(
    EdgeDeploymentPlanName='string',
    ModelConfigs=[
        {
            'ModelHandle': 'string',
            'EdgePackagingJobName': 'string'
        },
    ],
    DeviceFleetName='string',
    Stages=[
        {
            'StageName': 'string',
            'DeviceSelectionConfig': {
                'DeviceSubsetType': 'PERCENTAGE'|'SELECTION'|'NAMECONTAINS',
                'Percentage': 123,
                'DeviceNames': [
                    'string',
                ],
                'DeviceNameContains': 'string'
            },
            'DeploymentConfig': {
                'FailureHandlingPolicy': 'ROLLBACK_ON_FAILURE'|'DO_NOTHING'
            }
        },
    ],
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ]
)
Parameters
  • EdgeDeploymentPlanName (string) --

    [REQUIRED]

    The name of the edge deployment plan.

  • ModelConfigs (list) --

    [REQUIRED]

    List of models associated with the edge deployment plan.

    • (dict) --

      Contains information about the configuration of a model in a deployment.

      • ModelHandle (string) -- [REQUIRED]

        The name the device application uses to reference this model.

      • EdgePackagingJobName (string) -- [REQUIRED]

        The edge packaging job associated with this deployment.

  • DeviceFleetName (string) --

    [REQUIRED]

    The device fleet used for this edge deployment plan.

  • Stages (list) --

    List of stages of the edge deployment plan. The number of stages is limited to 10 per deployment.

    • (dict) --

      Contains information about a stage in an edge deployment plan.

      • StageName (string) -- [REQUIRED]

        The name of the stage.

      • DeviceSelectionConfig (dict) -- [REQUIRED]

        Configuration of the devices in the stage.

        • DeviceSubsetType (string) -- [REQUIRED]

          Type of device subsets to deploy to the current stage.

        • Percentage (integer) --

          Percentage of devices in the fleet to deploy to the current stage.

        • DeviceNames (list) --

          List of devices chosen to deploy.

          • (string) --
        • DeviceNameContains (string) --

          A filter to select devices with names containing this name.

      • DeploymentConfig (dict) --

        Configuration of the deployment details.

        • FailureHandlingPolicy (string) -- [REQUIRED]

          Toggle that determines whether to rollback to previous configuration if the current deployment fails. By default this is turned on. You may turn this off if you want to investigate the errors yourself.

  • Tags (list) --

    List of tags with which to tag the edge deployment plan.

    • (dict) --

      A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

      You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.

      For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.

      • Key (string) -- [REQUIRED]

        The tag key. Tag keys must be unique per resource.

      • Value (string) -- [REQUIRED]

        The tag value.

Return type

dict

Returns

Response Syntax

{
    'EdgeDeploymentPlanArn': 'string'
}

Response Structure

  • (dict) --

    • EdgeDeploymentPlanArn (string) --

      The ARN of the edge deployment plan.

Exceptions

  • SageMaker.Client.exceptions.ResourceLimitExceeded
create_edge_deployment_stage(**kwargs)

Creates a new stage in an existing edge deployment plan.

See also: AWS API Documentation

Request Syntax

response = client.create_edge_deployment_stage(
    EdgeDeploymentPlanName='string',
    Stages=[
        {
            'StageName': 'string',
            'DeviceSelectionConfig': {
                'DeviceSubsetType': 'PERCENTAGE'|'SELECTION'|'NAMECONTAINS',
                'Percentage': 123,
                'DeviceNames': [
                    'string',
                ],
                'DeviceNameContains': 'string'
            },
            'DeploymentConfig': {
                'FailureHandlingPolicy': 'ROLLBACK_ON_FAILURE'|'DO_NOTHING'
            }
        },
    ]
)
Parameters
  • EdgeDeploymentPlanName (string) --

    [REQUIRED]

    The name of the edge deployment plan.

  • Stages (list) --

    [REQUIRED]

    List of stages to be added to the edge deployment plan.

    • (dict) --

      Contains information about a stage in an edge deployment plan.

      • StageName (string) -- [REQUIRED]

        The name of the stage.

      • DeviceSelectionConfig (dict) -- [REQUIRED]

        Configuration of the devices in the stage.

        • DeviceSubsetType (string) -- [REQUIRED]

          Type of device subsets to deploy to the current stage.

        • Percentage (integer) --

          Percentage of devices in the fleet to deploy to the current stage.

        • DeviceNames (list) --

          List of devices chosen to deploy.

          • (string) --
        • DeviceNameContains (string) --

          A filter to select devices with names containing this name.

      • DeploymentConfig (dict) --

        Configuration of the deployment details.

        • FailureHandlingPolicy (string) -- [REQUIRED]

          Toggle that determines whether to rollback to previous configuration if the current deployment fails. By default this is turned on. You may turn this off if you want to investigate the errors yourself.

Returns

None

Exceptions

  • SageMaker.Client.exceptions.ResourceLimitExceeded
create_edge_packaging_job(**kwargs)

Starts a SageMaker Edge Manager model packaging job. Edge Manager will use the model artifacts from the Amazon Simple Storage Service bucket that you specify. After the model has been packaged, Amazon SageMaker saves the resulting artifacts to an S3 bucket that you specify.

See also: AWS API Documentation

Request Syntax

response = client.create_edge_packaging_job(
    EdgePackagingJobName='string',
    CompilationJobName='string',
    ModelName='string',
    ModelVersion='string',
    RoleArn='string',
    OutputConfig={
        'S3OutputLocation': 'string',
        'KmsKeyId': 'string',
        'PresetDeploymentType': 'GreengrassV2Component',
        'PresetDeploymentConfig': 'string'
    },
    ResourceKey='string',
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ]
)
Parameters
  • EdgePackagingJobName (string) --

    [REQUIRED]

    The name of the edge packaging job.

  • CompilationJobName (string) --

    [REQUIRED]

    The name of the SageMaker Neo compilation job that will be used to locate model artifacts for packaging.

  • ModelName (string) --

    [REQUIRED]

    The name of the model.

  • ModelVersion (string) --

    [REQUIRED]

    The version of the model.

  • RoleArn (string) --

    [REQUIRED]

    The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to download and upload the model, and to contact SageMaker Neo.

  • OutputConfig (dict) --

    [REQUIRED]

    Provides information about the output location for the packaged model.

    • S3OutputLocation (string) -- [REQUIRED]

      The Amazon Simple Storage (S3) bucker URI.

    • KmsKeyId (string) --

      The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume after compilation job. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account.

    • PresetDeploymentType (string) --

      The deployment type SageMaker Edge Manager will create. Currently only supports Amazon Web Services IoT Greengrass Version 2 components.

    • PresetDeploymentConfig (string) --

      The configuration used to create deployment artifacts. Specify configuration options with a JSON string. The available configuration options for each type are:

      • ComponentName (optional) - Name of the GreenGrass V2 component. If not specified, the default name generated consists of "SagemakerEdgeManager" and the name of your SageMaker Edge Manager packaging job.
      • ComponentDescription (optional) - Description of the component.
      • ComponentVersion (optional) - The version of the component.

      Note

      Amazon Web Services IoT Greengrass uses semantic versions for components. Semantic versions follow a*major.minor.patch* number system. For example, version 1.0.0 represents the first major release for a component. For more information, see the semantic version specification.

      • PlatformOS (optional) - The name of the operating system for the platform. Supported platforms include Windows and Linux.
      • PlatformArchitecture (optional) - The processor architecture for the platform. Supported architectures Windows include: Windows32_x86, Windows64_x64. Supported architectures for Linux include: Linux x86_64, Linux ARMV8.
  • ResourceKey (string) -- The Amazon Web Services KMS key to use when encrypting the EBS volume the edge packaging job runs on.
  • Tags (list) --

    Creates tags for the packaging job.

    • (dict) --

      A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

      You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.

      For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.

      • Key (string) -- [REQUIRED]

        The tag key. Tag keys must be unique per resource.

      • Value (string) -- [REQUIRED]

        The tag value.

Returns

None

Exceptions

  • SageMaker.Client.exceptions.ResourceLimitExceeded
create_endpoint(**kwargs)

Creates an endpoint using the endpoint configuration specified in the request. SageMaker uses the endpoint to provision resources and deploy models. You create the endpoint configuration with the CreateEndpointConfig API.

Use this API to deploy models using SageMaker hosting services.

For an example that calls this method when deploying a model to SageMaker hosting services, see the Create Endpoint example notebook.

Note

You must not delete an EndpointConfig that is in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. To update an endpoint, you must create a new EndpointConfig .

The endpoint name must be unique within an Amazon Web Services Region in your Amazon Web Services account.

When it receives the request, SageMaker creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them.

Note

When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting ` Eventually Consistent Reads https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/HowItWorks.ReadConsistency.html`__, the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.

When SageMaker receives the request, it sets the endpoint status to Creating . After it creates the endpoint, it sets the status to InService . SageMaker can then process incoming requests for inferences. To check the status of an endpoint, use the DescribeEndpoint API.

If any of the models hosted at this endpoint get model data from an Amazon S3 location, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provided. Amazon Web Services STS is activated in your IAM user account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide .

Note

To add the IAM role policies for using this API operation, go to the IAM console, and choose Roles in the left navigation pane. Search the IAM role that you want to grant access to use the CreateEndpoint and CreateEndpointConfig API operations, add the following policies to the role.

  • Option 1: For a full SageMaker access, search and attach the AmazonSageMakerFullAccess policy.
  • Option 2: For granting a limited access to an IAM role, paste the following Action elements manually into the JSON file of the IAM role: "Action": ["sagemaker:CreateEndpoint", "sagemaker:CreateEndpointConfig"] "Resource": [ "arn:aws:sagemaker:region:account-id:endpoint/endpointName" "arn:aws:sagemaker:region:account-id:endpoint-config/endpointConfigName" ] For more information, see SageMaker API Permissions: Actions, Permissions, and Resources Reference.

See also: AWS API Documentation

Request Syntax

response = client.create_endpoint(
    EndpointName='string',
    EndpointConfigName='string',
    DeploymentConfig={
        'BlueGreenUpdatePolicy': {
            'TrafficRoutingConfiguration': {
                'Type': 'ALL_AT_ONCE'|'CANARY'|'LINEAR',
                'WaitIntervalInSeconds': 123,
                'CanarySize': {
                    'Type': 'INSTANCE_COUNT'|'CAPACITY_PERCENT',
                    'Value': 123
                },
                'LinearStepSize': {
                    'Type': 'INSTANCE_COUNT'|'CAPACITY_PERCENT',
                    'Value': 123
                }
            },
            'TerminationWaitInSeconds': 123,
            'MaximumExecutionTimeoutInSeconds': 123
        },
        'AutoRollbackConfiguration': {
            'Alarms': [
                {
                    'AlarmName': 'string'
                },
            ]
        }
    },
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ]
)
Parameters
  • EndpointName (string) --

    [REQUIRED]

    The name of the endpoint.The name must be unique within an Amazon Web Services Region in your Amazon Web Services account. The name is case-insensitive in CreateEndpoint , but the case is preserved and must be matched in .

  • EndpointConfigName (string) --

    [REQUIRED]

    The name of an endpoint configuration. For more information, see CreateEndpointConfig.

  • DeploymentConfig (dict) --

    The deployment configuration for an endpoint, which contains the desired deployment strategy and rollback configurations.

    • BlueGreenUpdatePolicy (dict) -- [REQUIRED]

      Update policy for a blue/green deployment. If this update policy is specified, SageMaker creates a new fleet during the deployment while maintaining the old fleet. SageMaker flips traffic to the new fleet according to the specified traffic routing configuration. Only one update policy should be used in the deployment configuration. If no update policy is specified, SageMaker uses a blue/green deployment strategy with all at once traffic shifting by default.

      • TrafficRoutingConfiguration (dict) -- [REQUIRED]

        Defines the traffic routing strategy to shift traffic from the old fleet to the new fleet during an endpoint deployment.

        • Type (string) -- [REQUIRED]

          Traffic routing strategy type.

          • ALL_AT_ONCE : Endpoint traffic shifts to the new fleet in a single step.
          • CANARY : Endpoint traffic shifts to the new fleet in two steps. The first step is the canary, which is a small portion of the traffic. The second step is the remainder of the traffic.
          • LINEAR : Endpoint traffic shifts to the new fleet in n steps of a configurable size.
        • WaitIntervalInSeconds (integer) -- [REQUIRED]

          The waiting time (in seconds) between incremental steps to turn on traffic on the new endpoint fleet.

        • CanarySize (dict) --

          Batch size for the first step to turn on traffic on the new endpoint fleet. Value must be less than or equal to 50% of the variant's total instance count.

          • Type (string) -- [REQUIRED]

            Specifies the endpoint capacity type.

            • INSTANCE_COUNT : The endpoint activates based on the number of instances.
            • CAPACITY_PERCENT : The endpoint activates based on the specified percentage of capacity.
          • Value (integer) -- [REQUIRED]

            Defines the capacity size, either as a number of instances or a capacity percentage.

        • LinearStepSize (dict) --

          Batch size for each step to turn on traffic on the new endpoint fleet. Value must be 10-50% of the variant's total instance count.

          • Type (string) -- [REQUIRED]

            Specifies the endpoint capacity type.

            • INSTANCE_COUNT : The endpoint activates based on the number of instances.
            • CAPACITY_PERCENT : The endpoint activates based on the specified percentage of capacity.
          • Value (integer) -- [REQUIRED]

            Defines the capacity size, either as a number of instances or a capacity percentage.

      • TerminationWaitInSeconds (integer) --

        Additional waiting time in seconds after the completion of an endpoint deployment before terminating the old endpoint fleet. Default is 0.

      • MaximumExecutionTimeoutInSeconds (integer) --

        Maximum execution timeout for the deployment. Note that the timeout value should be larger than the total waiting time specified in TerminationWaitInSeconds and WaitIntervalInSeconds .

    • AutoRollbackConfiguration (dict) --

      Automatic rollback configuration for handling endpoint deployment failures and recovery.

      • Alarms (list) --

        List of CloudWatch alarms in your account that are configured to monitor metrics on an endpoint. If any alarms are tripped during a deployment, SageMaker rolls back the deployment.

        • (dict) --

          An Amazon CloudWatch alarm configured to monitor metrics on an endpoint.

          • AlarmName (string) --

            The name of a CloudWatch alarm in your account.

  • Tags (list) --

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

    • (dict) --

      A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

      You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.

      For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.

      • Key (string) -- [REQUIRED]

        The tag key. Tag keys must be unique per resource.

      • Value (string) -- [REQUIRED]

        The tag value.

Return type

dict

Returns

Response Syntax

{
    'EndpointArn': 'string'
}

Response Structure

  • (dict) --

    • EndpointArn (string) --

      The Amazon Resource Name (ARN) of the endpoint.

Exceptions

  • SageMaker.Client.exceptions.ResourceLimitExceeded
create_endpoint_config(**kwargs)

Creates an endpoint configuration that 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 SageMaker to provision. Then you call the CreateEndpoint API.

Note

Use this API if you want to use SageMaker hosting services to deploy models into production.

In the request, you define a ProductionVariant , for each model that you want to deploy. Each ProductionVariant parameter also describes the resources that you want 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. SageMaker distributes two-thirds of the traffic to Model A, and one-third to model B.

Note

When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting ` Eventually Consistent Reads https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/HowItWorks.ReadConsistency.html`__, the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.

See also: AWS API Documentation

Request Syntax

response = client.create_endpoint_config(
    EndpointConfigName='string',
    ProductionVariants=[
        {
            'VariantName': 'string',
            'ModelName': 'string',
            'InitialInstanceCount': 123,
            'InstanceType': 'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge',
            'InitialVariantWeight': ...,
            'AcceleratorType': 'ml.eia1.medium'|'ml.eia1.large'|'ml.eia1.xlarge'|'ml.eia2.medium'|'ml.eia2.large'|'ml.eia2.xlarge',
            'CoreDumpConfig': {
                'DestinationS3Uri': 'string',
                'KmsKeyId': 'string'
            },
            'ServerlessConfig': {
                'MemorySizeInMB': 123,
                'MaxConcurrency': 123
            },
            'VolumeSizeInGB': 123,
            'ModelDataDownloadTimeoutInSeconds': 123,
            'ContainerStartupHealthCheckTimeoutInSeconds': 123
        },
    ],
    DataCaptureConfig={
        'EnableCapture': True|False,
        'InitialSamplingPercentage': 123,
        'DestinationS3Uri': 'string',
        'KmsKeyId': 'string',
        'CaptureOptions': [
            {
                'CaptureMode': 'Input'|'Output'
            },
        ],
        'CaptureContentTypeHeader': {
            'CsvContentTypes': [
                'string',
            ],
            'JsonContentTypes': [
                'string',
            ]
        }
    },
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ],
    KmsKeyId='string',
    AsyncInferenceConfig={
        'ClientConfig': {
            'MaxConcurrentInvocationsPerInstance': 123
        },
        'OutputConfig': {
            'KmsKeyId': 'string',
            'S3OutputPath': 'string',
            'NotificationConfig': {
                'SuccessTopic': 'string',
                'ErrorTopic': 'string'
            }
        }
    },
    ExplainerConfig={
        'ClarifyExplainerConfig': {
            'EnableExplanations': 'string',
            'InferenceConfig': {
                'FeaturesAttribute': 'string',
                'ContentTemplate': 'string',
                'MaxRecordCount': 123,
                'MaxPayloadInMB': 123,
                'ProbabilityIndex': 123,
                'LabelIndex': 123,
                'ProbabilityAttribute': 'string',
                'LabelAttribute': 'string',
                'LabelHeaders': [
                    'string',
                ],
                'FeatureHeaders': [
                    'string',
                ],
                'FeatureTypes': [
                    'numerical'|'categorical'|'text',
                ]
            },
            'ShapConfig': {
                'ShapBaselineConfig': {
                    'MimeType': 'string',
                    'ShapBaseline': 'string',
                    'ShapBaselineUri': 'string'
                },
                'NumberOfSamples': 123,
                'UseLogit': True|False,
                'Seed': 123,
                'TextConfig': {
                    'Language': 'af'|'sq'|'ar'|'hy'|'eu'|'bn'|'bg'|'ca'|'zh'|'hr'|'cs'|'da'|'nl'|'en'|'et'|'fi'|'fr'|'de'|'el'|'gu'|'he'|'hi'|'hu'|'is'|'id'|'ga'|'it'|'kn'|'ky'|'lv'|'lt'|'lb'|'mk'|'ml'|'mr'|'ne'|'nb'|'fa'|'pl'|'pt'|'ro'|'ru'|'sa'|'sr'|'tn'|'si'|'sk'|'sl'|'es'|'sv'|'tl'|'ta'|'tt'|'te'|'tr'|'uk'|'ur'|'yo'|'lij'|'xx',
                    'Granularity': 'token'|'sentence'|'paragraph'
                }
            }
        }
    },
    ShadowProductionVariants=[
        {
            'VariantName': 'string',
            'ModelName': 'string',
            'InitialInstanceCount': 123,
            'InstanceType': 'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge',
            'InitialVariantWeight': ...,
            'AcceleratorType': 'ml.eia1.medium'|'ml.eia1.large'|'ml.eia1.xlarge'|'ml.eia2.medium'|'ml.eia2.large'|'ml.eia2.xlarge',
            'CoreDumpConfig': {
                'DestinationS3Uri': 'string',
                'KmsKeyId': 'string'
            },
            'ServerlessConfig': {
                'MemorySizeInMB': 123,
                'MaxConcurrency': 123
            },
            'VolumeSizeInGB': 123,
            'ModelDataDownloadTimeoutInSeconds': 123,
            'ContainerStartupHealthCheckTimeoutInSeconds': 123
        },
    ]
)
Parameters
  • EndpointConfigName (string) --

    [REQUIRED]

    The name of the endpoint configuration. You specify this name in a CreateEndpoint request.

  • ProductionVariants (list) --

    [REQUIRED]

    An list of ProductionVariant objects, one for each model that you want to host at this endpoint.

    • (dict) --

      Identifies a model that you want to host and the resources chosen to deploy for hosting it. If you are deploying multiple models, tell SageMaker how to distribute traffic among the models by specifying variant weights.

      • VariantName (string) -- [REQUIRED]

        The name of the production variant.

      • ModelName (string) -- [REQUIRED]

        The name of the model that you want to host. This is the name that you specified when creating the model.

      • InitialInstanceCount (integer) --

        Number of instances to launch initially.

      • InstanceType (string) --

        The ML compute instance type.

      • InitialVariantWeight (float) --

        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.

      • AcceleratorType (string) --

        The size of the Elastic Inference (EI) instance to use for the production variant. EI instances provide on-demand GPU computing for inference. For more information, see Using Elastic Inference in Amazon SageMaker.

      • CoreDumpConfig (dict) --

        Specifies configuration for a core dump from the model container when the process crashes.

        • DestinationS3Uri (string) -- [REQUIRED]

          The Amazon S3 bucket to send the core dump to.

        • KmsKeyId (string) --

          The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the core dump data at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:

          • // KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
          • // Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
          • // KMS Key Alias "alias/ExampleAlias"
          • // Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"

          If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig . If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.

          The KMS key policy must grant permission to the IAM role that you specify in your CreateEndpoint and UpdateEndpoint requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .

      • ServerlessConfig (dict) --

        The serverless configuration for an endpoint. Specifies a serverless endpoint configuration instead of an instance-based endpoint configuration.

        • MemorySizeInMB (integer) -- [REQUIRED]

          The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.

        • MaxConcurrency (integer) -- [REQUIRED]

          The maximum number of concurrent invocations your serverless endpoint can process.

      • VolumeSizeInGB (integer) --

        The size, in GB, of the ML storage volume attached to individual inference instance associated with the production variant. Currenly only Amazon EBS gp2 storage volumes are supported.

      • ModelDataDownloadTimeoutInSeconds (integer) --

        The timeout value, in seconds, to download and extract the model that you want to host from Amazon S3 to the individual inference instance associated with this production variant.

      • ContainerStartupHealthCheckTimeoutInSeconds (integer) --

        The timeout value, in seconds, for your inference container to pass health check by SageMaker Hosting. For more information about health check, see How Your Container Should Respond to Health Check (Ping) Requests.

  • DataCaptureConfig (dict) --

    Configuration to control how SageMaker captures inference data.

    • EnableCapture (boolean) --

      Whether data capture should be enabled or disabled (defaults to enabled).

    • InitialSamplingPercentage (integer) -- [REQUIRED]

      The percentage of requests SageMaker will capture. A lower value is recommended for Endpoints with high traffic.

    • DestinationS3Uri (string) -- [REQUIRED]

      The Amazon S3 location used to capture the data.

    • KmsKeyId (string) --

      The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint.

      The KmsKeyId can be any of the following formats:

      • Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
      • Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
      • Alias name: alias/ExampleAlias
      • Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
    • CaptureOptions (list) -- [REQUIRED]

      Specifies data Model Monitor will capture. You can configure whether to collect only input, only output, or both

      • (dict) --

        Specifies data Model Monitor will capture.

        • CaptureMode (string) -- [REQUIRED]

          Specify the boundary of data to capture.

    • CaptureContentTypeHeader (dict) --

      Configuration specifying how to treat different headers. If no headers are specified SageMaker will by default base64 encode when capturing the data.

      • CsvContentTypes (list) --

        The list of all content type headers that SageMaker will treat as CSV and capture accordingly.

        • (string) --
      • JsonContentTypes (list) --

        The list of all content type headers that SageMaker will treat as JSON and capture accordingly.

        • (string) --
  • Tags (list) --

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

    • (dict) --

      A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

      You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.

      For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.

      • Key (string) -- [REQUIRED]

        The tag key. Tag keys must be unique per resource.

      • Value (string) -- [REQUIRED]

        The tag value.

  • KmsKeyId (string) --

    The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint.

    The KmsKeyId can be any of the following formats:

    • Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
    • Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
    • Alias name: alias/ExampleAlias
    • Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias

    The KMS key policy must grant permission to the IAM role that you specify in your CreateEndpoint , UpdateEndpoint requests. For more information, refer to the Amazon Web Services Key Management Service section`Using Key Policies in Amazon Web Services KMS <https://docs.aws.amazon.com/kms/latest/developerguide/key-policies.html>`__

    Note

    Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a KmsKeyId when using an instance type with local storage. If any of the models that you specify in the ProductionVariants parameter use nitro-based instances with local storage, do not specify a value for the KmsKeyId parameter. If you specify a value for KmsKeyId when using any nitro-based instances with local storage, the call to CreateEndpointConfig fails.

    For a list of instance types that support local instance storage, see Instance Store Volumes.

    For more information about local instance storage encryption, see SSD Instance Store Volumes.

  • AsyncInferenceConfig (dict) --

    Specifies configuration for how an endpoint performs asynchronous inference. This is a required field in order for your Endpoint to be invoked using InvokeEndpointAsync.

    • ClientConfig (dict) --

      Configures the behavior of the client used by SageMaker to interact with the model container during asynchronous inference.

      • MaxConcurrentInvocationsPerInstance (integer) --

        The maximum number of concurrent requests sent by the SageMaker client to the model container. If no value is provided, SageMaker chooses an optimal value.

    • OutputConfig (dict) -- [REQUIRED]

      Specifies the configuration for asynchronous inference invocation outputs.

      • KmsKeyId (string) --

        The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the asynchronous inference output in Amazon S3.

      • S3OutputPath (string) -- [REQUIRED]

        The Amazon S3 location to upload inference responses to.

      • NotificationConfig (dict) --

        Specifies the configuration for notifications of inference results for asynchronous inference.

        • SuccessTopic (string) --

          Amazon SNS topic to post a notification to when inference completes successfully. If no topic is provided, no notification is sent on success.

        • ErrorTopic (string) --

          Amazon SNS topic to post a notification to when inference fails. If no topic is provided, no notification is sent on failure.

  • ExplainerConfig (dict) --

    A member of CreateEndpointConfig that enables explainers.

    • ClarifyExplainerConfig (dict) --

      A member of ExplainerConfig that contains configuration parameters for the SageMaker Clarify explainer.

      • EnableExplanations (string) --

        A JMESPath boolean expression used to filter which records to explain. Explanations are activated by default. See ` EnableExplanations https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-online-explainability-create-endpoint.html#clarify-online-explainability-create-endpoint-enable`__ for additional information.

      • InferenceConfig (dict) --

        The inference configuration parameter for the model container.

        • FeaturesAttribute (string) --

          Provides the JMESPath expression to extract the features from a model container input in JSON Lines format. For example, if FeaturesAttribute is the JMESPath expression 'myfeatures' , it extracts a list of features [1,2,3] from request data '{"myfeatures":[1,2,3]}' .

        • ContentTemplate (string) --

          A template string used to format a JSON record into an acceptable model container input. For example, a ContentTemplate string '{"myfeatures":$features}' will format a list of features [1,2,3] into the record string '{"myfeatures":[1,2,3]}' . Required only when the model container input is in JSON Lines format.

        • MaxRecordCount (integer) --

          The maximum number of records in a request that the model container can process when querying the model container for the predictions of a synthetic dataset. A record is a unit of input data that inference can be made on, for example, a single line in CSV data. If MaxRecordCount is 1 , the model container expects one record per request. A value of 2 or greater means that the model expects batch requests, which can reduce overhead and speed up the inferencing process. If this parameter is not provided, the explainer will tune the record count per request according to the model container's capacity at runtime.

        • MaxPayloadInMB (integer) --

          The maximum payload size (MB) allowed of a request from the explainer to the model container. Defaults to 6 MB.

        • ProbabilityIndex (integer) --

          A zero-based index used to extract a probability value (score) or list from model container output in CSV format. If this value is not provided, the entire model container output will be treated as a probability value (score) or list.

          Example for a single class model: If the model container output consists of a string-formatted prediction label followed by its probability: '1,0.6' , set ProbabilityIndex to 1 to select the probability value 0.6 .

          Example for a multiclass model: If the model container output consists of a string-formatted prediction label followed by its probability: '"[\'cat\',\'dog\',\'fish\']","[0.1,0.6,0.3]"' , set ProbabilityIndex to 1 to select the probability values [0.1,0.6,0.3] .

        • LabelIndex (integer) --

          A zero-based index used to extract a label header or list of label headers from model container output in CSV format.

          Example for a multiclass model: If the model container output consists of label headers followed by probabilities: '"[\'cat\',\'dog\',\'fish\']","[0.1,0.6,0.3]"' , set LabelIndex to 0 to select the label headers ['cat','dog','fish'] .
        • ProbabilityAttribute (string) --

          A JMESPath expression used to extract the probability (or score) from the model container output if the model container is in JSON Lines format.

          Example : If the model container output of a single request is '{"predicted_label":1,"probability":0.6}' , then set ProbabilityAttribute to 'probability' .
        • LabelAttribute (string) --

          A JMESPath expression used to locate the list of label headers in the model container output.

          Example : If the model container output of a batch request is '{"labels":["cat","dog","fish"],"probability":[0.6,0.3,0.1]}' , then set LabelAttribute to 'labels' to extract the list of label headers ["cat","dog","fish"]
        • LabelHeaders (list) --

          For multiclass classification problems, the label headers are the names of the classes. Otherwise, the label header is the name of the predicted label. These are used to help readability for the output of the InvokeEndpoint API. See the response section under Invoke the endpoint in the Developer Guide for more information. If there are no label headers in the model container output, provide them manually using this parameter.

          • (string) --
        • FeatureHeaders (list) --

          The names of the features. If provided, these are included in the endpoint response payload to help readability of the InvokeEndpoint output. See the Response section under Invoke the endpoint in the Developer Guide for more information.

          • (string) --
        • FeatureTypes (list) --

          A list of data types of the features (optional). Applicable only to NLP explainability. If provided, FeatureTypes must have at least one 'text' string (for example, ['text'] ). If FeatureTypes is not provided, the explainer infers the feature types based on the baseline data. The feature types are included in the endpoint response payload. For additional information see the response section under Invoke the endpoint in the Developer Guide for more information.

          • (string) --
      • ShapConfig (dict) -- [REQUIRED]

        The configuration for SHAP analysis.

        • ShapBaselineConfig (dict) -- [REQUIRED]

          The configuration for the SHAP baseline of the Kernal SHAP algorithm.

          • MimeType (string) --

            The MIME type of the baseline data. Choose from 'text/csv' or 'application/jsonlines' . Defaults to 'text/csv' .

          • ShapBaseline (string) --

            The inline SHAP baseline data in string format. ShapBaseline can have one or multiple records to be used as the baseline dataset. The format of the SHAP baseline file should be the same format as the training dataset. For example, if the training dataset is in CSV format and each record contains four features, and all features are numerical, then the format of the baseline data should also share these characteristics. For natural language processing (NLP) of text columns, the baseline value should be the value used to replace the unit of text specified by the Granularity of the TextConfig parameter. The size limit for ShapBasline is 4 KB. Use the ShapBaselineUri parameter if you want to provide more than 4 KB of baseline data.

          • ShapBaselineUri (string) --

            The uniform resource identifier (URI) of the S3 bucket where the SHAP baseline file is stored. The format of the SHAP baseline file should be the same format as the format of the training dataset. For example, if the training dataset is in CSV format, and each record in the training dataset has four features, and all features are numerical, then the baseline file should also have this same format. Each record should contain only the features. If you are using a virtual private cloud (VPC), the ShapBaselineUri should be accessible to the VPC. For more information about setting up endpoints with Amazon Virtual Private Cloud, see Give SageMaker access to Resources in your Amazon Virtual Private Cloud.

        • NumberOfSamples (integer) --

          The number of samples to be used for analysis by the Kernal SHAP algorithm.

          Note

          The number of samples determines the size of the synthetic dataset, which has an impact on latency of explainability requests. For more information, see the Synthetic data of Configure and create an endpoint.

        • UseLogit (boolean) --

          A Boolean toggle to indicate if you want to use the logit function (true) or log-odds units (false) for model predictions. Defaults to false.

        • Seed (integer) --

          The starting value used to initialize the random number generator in the explainer. Provide a value for this parameter to obtain a deterministic SHAP result.

        • TextConfig (dict) --

          A parameter that indicates if text features are treated as text and explanations are provided for individual units of text. Required for natural language processing (NLP) explainability only.

          • Language (string) -- [REQUIRED]

            Specifies the language of the text features in `ISO 639-1 < https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes>`__ or ISO 639-3 code of a supported language.

            Note

            For a mix of multiple languages, use code 'xx' .

          • Granularity (string) -- [REQUIRED]

            The unit of granularity for the analysis of text features. For example, if the unit is 'token' , then each token (like a word in English) of the text is treated as a feature. SHAP values are computed for each unit/feature.

  • ShadowProductionVariants (list) --

    Array of ProductionVariant objects. There is one for each model that you want to host at this endpoint in shadow mode with production traffic replicated from the model specified on ProductionVariants .If you use this field, you can only specify one variant for ProductionVariants and one variant for ShadowProductionVariants .

    • (dict) --

      Identifies a model that you want to host and the resources chosen to deploy for hosting it. If you are deploying multiple models, tell SageMaker how to distribute traffic among the models by specifying variant weights.

      • VariantName (string) -- [REQUIRED]

        The name of the production variant.

      • ModelName (string) -- [REQUIRED]

        The name of the model that you want to host. This is the name that you specified when creating the model.

      • InitialInstanceCount (integer) --

        Number of instances to launch initially.

      • InstanceType (string) --

        The ML compute instance type.

      • InitialVariantWeight (float) --

        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.

      • AcceleratorType (string) --

        The size of the Elastic Inference (EI) instance to use for the production variant. EI instances provide on-demand GPU computing for inference. For more information, see Using Elastic Inference in Amazon SageMaker.

      • CoreDumpConfig (dict) --

        Specifies configuration for a core dump from the model container when the process crashes.

        • DestinationS3Uri (string) -- [REQUIRED]

          The Amazon S3 bucket to send the core dump to.

        • KmsKeyId (string) --

          The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the core dump data at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:

          • // KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
          • // Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
          • // KMS Key Alias "alias/ExampleAlias"
          • // Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"

          If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig . If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.

          The KMS key policy must grant permission to the IAM role that you specify in your CreateEndpoint and UpdateEndpoint requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .

      • ServerlessConfig (dict) --

        The serverless configuration for an endpoint. Specifies a serverless endpoint configuration instead of an instance-based endpoint configuration.

        • MemorySizeInMB (integer) -- [REQUIRED]

          The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.

        • MaxConcurrency (integer) -- [REQUIRED]

          The maximum number of concurrent invocations your serverless endpoint can process.

      • VolumeSizeInGB (integer) --

        The size, in GB, of the ML storage volume attached to individual inference instance associated with the production variant. Currenly only Amazon EBS gp2 storage volumes are supported.

      • ModelDataDownloadTimeoutInSeconds (integer) --

        The timeout value, in seconds, to download and extract the model that you want to host from Amazon S3 to the individual inference instance associated with this production variant.

      • ContainerStartupHealthCheckTimeoutInSeconds (integer) --

        The timeout value, in seconds, for your inference container to pass health check by SageMaker Hosting. For more information about health check, see How Your Container Should Respond to Health Check (Ping) Requests.

Return type

dict

Returns

Response Syntax

{
    'EndpointConfigArn': 'string'
}

Response Structure

  • (dict) --

    • EndpointConfigArn (string) --

      The Amazon Resource Name (ARN) of the endpoint configuration.

Exceptions

  • SageMaker.Client.exceptions.ResourceLimitExceeded
create_experiment(**kwargs)

Creates an SageMaker experiment . An experiment is a collection of trials that are observed, compared and evaluated as a group. A trial is a set of steps, called trial components , that produce a machine learning model.

The goal of an experiment is to determine the components that produce the best model. Multiple trials are performed, each one isolating and measuring the impact of a change to one or more inputs, while keeping the remaining inputs constant.

When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK.

You can add tags to experiments, trials, trial components and then use the Search API to search for the tags.

To add a description to an experiment, specify the optional Description parameter. To add a description later, or to change the description, call the UpdateExperiment API.

To get a list of all your experiments, call the ListExperiments API. To view an experiment's properties, call the DescribeExperiment API. To get a list of all the trials associated with an experiment, call the ListTrials API. To create a trial call the CreateTrial API.

See also: AWS API Documentation

Request Syntax

response = client.create_experiment(
    ExperimentName='string',
    DisplayName='string',
    Description='string',
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ]
)
Parameters
  • ExperimentName (string) --

    [REQUIRED]

    The name of the experiment. The name must be unique in your Amazon Web Services account and is not case-sensitive.

  • DisplayName (string) -- The name of the experiment as displayed. The name doesn't need to be unique. If you don't specify DisplayName , the value in ExperimentName is displayed.
  • Description (string) -- The description of the experiment.
  • Tags (list) --

    A list of tags to associate with the experiment. You can use Search API to search on the tags.

    • (dict) --

      A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

      You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.

      For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.

      • Key (string) -- [REQUIRED]

        The tag key. Tag keys must be unique per resource.

      • Value (string) -- [REQUIRED]

        The tag value.

Return type

dict

Returns

Response Syntax

{
    'ExperimentArn': 'string'
}

Response Structure

  • (dict) --

    • ExperimentArn (string) --

      The Amazon Resource Name (ARN) of the experiment.

Exceptions

  • SageMaker.Client.exceptions.ResourceLimitExceeded
create_feature_group(**kwargs)

Create a new FeatureGroup . A FeatureGroup is a group of Features defined in the FeatureStore to describe a Record .

The FeatureGroup defines the schema and features contained in the FeatureGroup. A FeatureGroup definition is composed of a list of Features , a RecordIdentifierFeatureName , an EventTimeFeatureName and configurations for its OnlineStore and OfflineStore . Check Amazon Web Services service quotas to see the FeatureGroup s quota for your Amazon Web Services account.

Warning

You must include at least one of OnlineStoreConfig and OfflineStoreConfig to create a FeatureGroup .

See also: AWS API Documentation

Request Syntax

response = client.create_feature_group(
    FeatureGroupName='string',
    RecordIdentifierFeatureName='string',
    EventTimeFeatureName='string',
    FeatureDefinitions=[
        {
            'FeatureName': 'string',
            'FeatureType': 'Integral'|'Fractional'|'String'
        },
    ],
    OnlineStoreConfig={
        'SecurityConfig': {
            'KmsKeyId': 'string'
        },
        'EnableOnlineStore': True|False
    },
    OfflineStoreConfig={
        'S3StorageConfig': {
            'S3Uri': 'string',
            'KmsKeyId': 'string',
            'ResolvedOutputS3Uri': 'string'
        },
        'DisableGlueTableCreation': True|False,
        'DataCatalogConfig': {
            'TableName': 'string',
            'Catalog': 'string',
            'Database': 'string'
        },
        'TableFormat': 'Glue'|'Iceberg'
    },
    RoleArn='string',
    Description='string',
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ]
)
Parameters
  • FeatureGroupName (string) --

    [REQUIRED]

    The name of the FeatureGroup . The name must be unique within an Amazon Web Services Region in an Amazon Web Services account. The name:

    • Must start and end with an alphanumeric character.
    • Can only contain alphanumeric character and hyphens. Spaces are not allowed.
  • RecordIdentifierFeatureName (string) --

    [REQUIRED]

    The name of the Feature whose value uniquely identifies a Record defined in the FeatureStore . Only the latest record per identifier value will be stored in the OnlineStore . RecordIdentifierFeatureName must be one of feature definitions' names.

    You use the RecordIdentifierFeatureName to access data in a FeatureStore .

    This name:

    • Must start and end with an alphanumeric character.
    • Can only contains alphanumeric characters, hyphens, underscores. Spaces are not allowed.
  • EventTimeFeatureName (string) --

    [REQUIRED]

    The name of the feature that stores the EventTime of a Record in a FeatureGroup .

    An EventTime is a point in time when a new event occurs that corresponds to the creation or update of a Record in a FeatureGroup . All Records in the FeatureGroup must have a corresponding EventTime .

    An EventTime can be a String or Fractional .

    • Fractional : EventTime feature values must be a Unix timestamp in seconds.
    • String : EventTime feature values must be an ISO-8601 string in the format. The following formats are supported yyyy-MM-dd'T'HH:mm:ssZ and yyyy-MM-dd'T'HH:mm:ss.SSSZ where yyyy , MM , and dd represent the year, month, and day respectively and HH , mm , ss , and if applicable, SSS represent the hour, month, second and milliseconds respsectively. 'T' and Z are constants.
  • FeatureDefinitions (list) --

    [REQUIRED]

    A list of Feature names and types. Name and Type is compulsory per Feature .

    Valid feature FeatureType s are Integral , Fractional and String .

    FeatureName s cannot be any of the following: is_deleted , write_time , api_invocation_time

    You can create up to 2,500 FeatureDefinition s per FeatureGroup .

    • (dict) --

      A list of features. You must include FeatureName and FeatureType . Valid feature FeatureType s are Integral , Fractional and String .

      • FeatureName (string) --

        The name of a feature. The type must be a string. FeatureName cannot be any of the following: is_deleted , write_time , api_invocation_time .

      • FeatureType (string) --

        The value type of a feature. Valid values are Integral, Fractional, or String.

  • OnlineStoreConfig (dict) --

    You can turn the OnlineStore on or off by specifying True for the EnableOnlineStore flag in OnlineStoreConfig ; the default value is False .

    You can also include an Amazon Web Services KMS key ID (KMSKeyId ) for at-rest encryption of the OnlineStore .

    • SecurityConfig (dict) --

      Use to specify KMS Key ID (KMSKeyId ) for at-rest encryption of your OnlineStore .

      • KmsKeyId (string) --

        The ID of the Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker Feature Store uses to encrypt the Amazon S3 objects at rest using Amazon S3 server-side encryption.

        The caller (either IAM user or IAM role) of CreateFeatureGroup must have below permissions to the OnlineStore KmsKeyId :

        • "kms:Encrypt"
        • "kms:Decrypt"
        • "kms:DescribeKey"
        • "kms:CreateGrant"
        • "kms:RetireGrant"
        • "kms:ReEncryptFrom"
        • "kms:ReEncryptTo"
        • "kms:GenerateDataKey"
        • "kms:ListAliases"
        • "kms:ListGrants"
        • "kms:RevokeGrant"

        The caller (either IAM user or IAM role) to all DataPlane operations (PutRecord , GetRecord , DeleteRecord ) must have the following permissions to the KmsKeyId :

        • "kms:Decrypt"
    • EnableOnlineStore (boolean) --

      Turn OnlineStore off by specifying False for the EnableOnlineStore flag. Turn OnlineStore on by specifying True for the EnableOnlineStore flag.

      The default value is False .

  • OfflineStoreConfig (dict) --

    Use this to configure an OfflineFeatureStore . This parameter allows you to specify:

    • The Amazon Simple Storage Service (Amazon S3) location of an OfflineStore .
    • A configuration for an Amazon Web Services Glue or Amazon Web Services Hive data catalog.
    • An KMS encryption key to encrypt the Amazon S3 location used for OfflineStore . If KMS encryption key is not specified, by default we encrypt all data at rest using Amazon Web Services KMS key. By defining your bucket-level key for SSE, you can reduce Amazon Web Services KMS requests costs by up to 99 percent.

    To learn more about this parameter, see OfflineStoreConfig.

    • S3StorageConfig (dict) -- [REQUIRED]

      The Amazon Simple Storage (Amazon S3) location of OfflineStore .

      • S3Uri (string) -- [REQUIRED]

        The S3 URI, or location in Amazon S3, of OfflineStore .

        S3 URIs have a format similar to the following: s3://example-bucket/prefix/ .

      • KmsKeyId (string) --

        The Amazon Web Services Key Management Service (KMS) key ID of the key used to encrypt any objects written into the OfflineStore S3 location.

        The IAM roleARN that is passed as a parameter to CreateFeatureGroup must have below permissions to the KmsKeyId :

        • "kms:GenerateDataKey"
      • ResolvedOutputS3Uri (string) --

        The S3 path where offline records are written.

    • DisableGlueTableCreation (boolean) --

      Set to True to disable the automatic creation of an Amazon Web Services Glue table when configuring an OfflineStore .

    • DataCatalogConfig (dict) --

      The meta data of the Glue table that is autogenerated when an OfflineStore is created.

      • TableName (string) -- [REQUIRED]

        The name of the Glue table.

      • Catalog (string) -- [REQUIRED]

        The name of the Glue table catalog.

      • Database (string) -- [REQUIRED]

        The name of the Glue table database.

    • TableFormat (string) --

      Format for the offline store feature group. Iceberg is the optimal format for feature groups shared between offline and online stores.

  • RoleArn (string) -- The Amazon Resource Name (ARN) of the IAM execution role used to persist data into the OfflineStore if an OfflineStoreConfig is provided.
  • Description (string) -- A free-form description of a FeatureGroup .
  • Tags (list) --

    Tags used to identify Features in each FeatureGroup .

    • (dict) --

      A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

      You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.

      For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.

      • Key (string) -- [REQUIRED]

        The tag key. Tag keys must be unique per resource.

      • Value (string) -- [REQUIRED]

        The tag value.

Return type

dict

Returns

Response Syntax

{
    'FeatureGroupArn': 'string'
}

Response Structure

  • (dict) --

    • FeatureGroupArn (string) --

      The Amazon Resource Name (ARN) of the FeatureGroup . This is a unique identifier for the feature group.

Exceptions

  • SageMaker.Client.exceptions.ResourceInUse
  • SageMaker.Client.exceptions.ResourceLimitExceeded
create_flow_definition(**kwargs)

Creates a flow definition.

See also: AWS API Documentation

Request Syntax

response = client.create_flow_definition(
    FlowDefinitionName='string',
    HumanLoopRequestSource={
        'AwsManagedHumanLoopRequestSource': 'AWS/Rekognition/DetectModerationLabels/Image/V3'|'AWS/Textract/AnalyzeDocument/Forms/V1'
    },
    HumanLoopActivationConfig={
        'HumanLoopActivationConditionsConfig': {
            'HumanLoopActivationConditions': 'string'
        }
    },
    HumanLoopConfig={
        'WorkteamArn': 'string',
        'HumanTaskUiArn': 'string',
        'TaskTitle': 'string',
        'TaskDescription': 'string',
        'TaskCount': 123,
        'TaskAvailabilityLifetimeInSeconds': 123,
        'TaskTimeLimitInSeconds': 123,
        'TaskKeywords': [
            'string',
        ],
        'PublicWorkforceTaskPrice': {
            'AmountInUsd': {
                'Dollars': 123,
                'Cents': 123,
                'TenthFractionsOfACent': 123
            }
        }
    },
    OutputConfig={
        'S3OutputPath': 'string',
        'KmsKeyId': 'string'
    },
    RoleArn='string',
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ]
)
Parameters
  • FlowDefinitionName (string) --

    [REQUIRED]

    The name of your flow definition.

  • HumanLoopRequestSource (dict) --

    Container for configuring the source of human task requests. Use to specify if Amazon Rekognition or Amazon Textract is used as an integration source.

    • AwsManagedHumanLoopRequestSource (string) -- [REQUIRED]

      Specifies whether Amazon Rekognition or Amazon Textract are used as the integration source. The default field settings and JSON parsing rules are different based on the integration source. Valid values:

  • HumanLoopActivationConfig (dict) --

    An object containing information about the events that trigger a human workflow.

    • HumanLoopActivationConditionsConfig (dict) -- [REQUIRED]

      Container structure for defining under what conditions SageMaker creates a human loop.

      • HumanLoopActivationConditions (string) -- [REQUIRED]

        JSON expressing use-case specific conditions declaratively. If any condition is matched, atomic tasks are created against the configured work team. The set of conditions is different for Rekognition and Textract. For more information about how to structure the JSON, see JSON Schema for Human Loop Activation Conditions in Amazon Augmented AI in the Amazon SageMaker Developer Guide .

  • HumanLoopConfig (dict) --

    [REQUIRED]

    An object containing information about the tasks the human reviewers will perform.

    • WorkteamArn (string) -- [REQUIRED]

      Amazon Resource Name (ARN) of a team of workers. To learn more about the types of workforces and work teams you can create and use with Amazon A2I, see Create and Manage Workforces.

    • HumanTaskUiArn (string) -- [REQUIRED]

      The Amazon Resource Name (ARN) of the human task user interface.

      You can use standard HTML and Crowd HTML Elements to create a custom worker task template. You use this template to create a human task UI.

      To learn how to create a custom HTML template, see Create Custom Worker Task Template.

      To learn how to create a human task UI, which is a worker task template that can be used in a flow definition, see Create and Delete a Worker Task Templates.

    • TaskTitle (string) -- [REQUIRED]

      A title for the human worker task.

    • TaskDescription (string) -- [REQUIRED]

      A description for the human worker task.

    • TaskCount (integer) -- [REQUIRED]

      The number of distinct workers who will perform the same task on each object. For example, if TaskCount is set to 3 for an image classification labeling job, three workers will classify each input image. Increasing TaskCount can improve label accuracy.

    • TaskAvailabilityLifetimeInSeconds (integer) --

      The length of time that a task remains available for review by human workers.

    • TaskTimeLimitInSeconds (integer) --

      The amount of time that a worker has to complete a task. The default value is 3,600 seconds (1 hour).

    • TaskKeywords (list) --

      Keywords used to describe the task so that workers can discover the task.

      • (string) --
    • PublicWorkforceTaskPrice (dict) --

      Defines the amount of money paid to an Amazon Mechanical Turk worker for each task performed.

      Use one of the following prices for bounding box tasks. Prices are in US dollars and should be based on the complexity of the task; the longer it takes in your initial testing, the more you should offer.

      • 0.036
      • 0.048
      • 0.060
      • 0.072
      • 0.120
      • 0.240
      • 0.360
      • 0.480
      • 0.600
      • 0.720
      • 0.840
      • 0.960
      • 1.080
      • 1.200

      Use one of the following prices for image classification, text classification, and custom tasks. Prices are in US dollars.

      • 0.012
      • 0.024
      • 0.036
      • 0.048
      • 0.060
      • 0.072
      • 0.120
      • 0.240
      • 0.360
      • 0.480
      • 0.600
      • 0.720
      • 0.840
      • 0.960
      • 1.080
      • 1.200

      Use one of the following prices for semantic segmentation tasks. Prices are in US dollars.

      • 0.840
      • 0.960
      • 1.080
      • 1.200

      Use one of the following prices for Textract AnalyzeDocument Important Form Key Amazon Augmented AI review tasks. Prices are in US dollars.

      • 2.400
      • 2.280
      • 2.160
      • 2.040
      • 1.920
      • 1.800
      • 1.680
      • 1.560
      • 1.440
      • 1.320
      • 1.200
      • 1.080
      • 0.960
      • 0.840
      • 0.720
      • 0.600
      • 0.480
      • 0.360
      • 0.240
      • 0.120
      • 0.072
      • 0.060
      • 0.048
      • 0.036
      • 0.024
      • 0.012

      Use one of the following prices for Rekognition DetectModerationLabels Amazon Augmented AI review tasks. Prices are in US dollars.

      • 1.200
      • 1.080
      • 0.960
      • 0.840
      • 0.720
      • 0.600
      • 0.480
      • 0.360
      • 0.240
      • 0.120
      • 0.072
      • 0.060
      • 0.048
      • 0.036
      • 0.024
      • 0.012

      Use one of the following prices for Amazon Augmented AI custom human review tasks. Prices are in US dollars.

      • 1.200
      • 1.080
      • 0.960
      • 0.840
      • 0.720
      • 0.600
      • 0.480
      • 0.360
      • 0.240
      • 0.120
      • 0.072
      • 0.060
      • 0.048
      • 0.036
      • 0.024
      • 0.012
      • AmountInUsd (dict) --

        Defines the amount of money paid to an Amazon Mechanical Turk worker in United States dollars.

        • Dollars (integer) --

          The whole number of dollars in the amount.

        • Cents (integer) --

          The fractional portion, in cents, of the amount.

        • TenthFractionsOfACent (integer) --

          Fractions of a cent, in tenths.

  • OutputConfig (dict) --

    [REQUIRED]

    An object containing information about where the human review results will be uploaded.

    • S3OutputPath (string) -- [REQUIRED]

      The Amazon S3 path where the object containing human output will be made available.

      To learn more about the format of Amazon A2I output data, see Amazon A2I Output Data.

    • KmsKeyId (string) --

      The Amazon Key Management Service (KMS) key ID for server-side encryption.

  • RoleArn (string) --

    [REQUIRED]

    The Amazon Resource Name (ARN) of the role needed to call other services on your behalf. For example, arn:aws:iam::1234567890:role/service-role/AmazonSageMaker-ExecutionRole-20180111T151298 .

  • Tags (list) --

    An array of key-value pairs that contain metadata to help you categorize and organize a flow definition. Each tag consists of a key and a value, both of which you define.

    • (dict) --

      A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

      You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.

      For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.

      • Key (string) -- [REQUIRED]

        The tag key. Tag keys must be unique per resource.

      • Value (string) -- [REQUIRED]

        The tag value.

Return type

dict

Returns

Response Syntax

{
    'FlowDefinitionArn': 'string'
}

Response Structure

  • (dict) --

    • FlowDefinitionArn (string) --

      The Amazon Resource Name (ARN) of the flow definition you create.

Exceptions

  • SageMaker.Client.exceptions.ResourceLimitExceeded
  • SageMaker.Client.exceptions.ResourceInUse
create_hub(**kwargs)

Create a hub.

See also: AWS API Documentation

Request Syntax

response = client.create_hub(
    HubName='string',
    HubDescription='string',
    HubDisplayName='string',
    HubSearchKeywords=[
        'string',
    ],
    S3StorageConfig={
        'S3OutputPath': 'string'
    },
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ]
)
Parameters
  • HubName (string) --

    [REQUIRED]

    The name of the hub to create.

  • HubDescription (string) --

    [REQUIRED]

    A description of the hub.

  • HubDisplayName (string) -- The display name of the hub.
  • HubSearchKeywords (list) --

    The searchable keywords for the hub.

    • (string) --
  • S3StorageConfig (dict) --

    The Amazon S3 storage configuration for the hub.

    • S3OutputPath (string) --

      The Amazon S3 output path for the hub.

  • Tags (list) --

    Any tags to associate with the hub.

    • (dict) --

      A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

      You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.

      For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.

      • Key (string) -- [REQUIRED]

        The tag key. Tag keys must be unique per resource.

      • Value (string) -- [REQUIRED]

        The tag value.

Return type

dict

Returns

Response Syntax

{
    'HubArn': 'string'
}

Response Structure

  • (dict) --

    • HubArn (string) --

      The Amazon Resource Name (ARN) of the hub.

Exceptions

  • SageMaker.Client.exceptions.ResourceInUse
  • SageMaker.Client.exceptions.ResourceLimitExceeded
create_human_task_ui(**kwargs)

Defines the settings you will use for the human review workflow user interface. Reviewers will see a three-panel interface with an instruction area, the item to review, and an input area.

See also: AWS API Documentation

Request Syntax

response = client.create_human_task_ui(
    HumanTaskUiName='string',
    UiTemplate={
        'Content': 'string'
    },
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ]
)
Parameters
  • HumanTaskUiName (string) --

    [REQUIRED]

    The name of the user interface you are creating.

  • UiTemplate (dict) --

    [REQUIRED]

    The Liquid template for the worker user interface.

    • Content (string) -- [REQUIRED]

      The content of the Liquid template for the worker user interface.

  • Tags (list) --

    An array of key-value pairs that contain metadata to help you categorize and organize a human review workflow user interface. Each tag consists of a key and a value, both of which you define.

    • (dict) --

      A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

      You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.

      For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.

      • Key (string) -- [REQUIRED]

        The tag key. Tag keys must be unique per resource.

      • Value (string) -- [REQUIRED]

        The tag value.

Return type

dict

Returns

Response Syntax

{
    'HumanTaskUiArn': 'string'
}

Response Structure

  • (dict) --

    • HumanTaskUiArn (string) --

      The Amazon Resource Name (ARN) of the human review workflow user interface you create.

Exceptions

  • SageMaker.Client.exceptions.ResourceLimitExceeded
  • SageMaker.Client.exceptions.ResourceInUse
create_hyper_parameter_tuning_job(**kwargs)

Starts a hyperparameter tuning job. A hyperparameter tuning job finds the best version of a model by running many training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by an objective metric that you choose.

A hyperparameter tuning job automatically creates Amazon SageMaker experiments, trials, and trial components for each training job that it runs. You can view these entities in Amazon SageMaker Studio. For more information, see View Experiments, Trials, and Trial Components.

Warning

Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error.

See also: AWS API Documentation

Request Syntax

response = client.create_hyper_parameter_tuning_job(
    HyperParameterTuningJobName='string',
    HyperParameterTuningJobConfig={
        'Strategy': 'Bayesian'|'Random'|'Hyperband'|'Grid',
        'StrategyConfig': {
            'HyperbandStrategyConfig': {
                'MinResource': 123,
                'MaxResource': 123
            }
        },
        'HyperParameterTuningJobObjective': {
            'Type': 'Maximize'|'Minimize',
            'MetricName': 'string'
        },
        'ResourceLimits': {
            'MaxNumberOfTrainingJobs': 123,
            'MaxParallelTrainingJobs': 123
        },
        'ParameterRanges': {
            'IntegerParameterRanges': [
                {
                    'Name': 'string',
                    'MinValue': 'string',
                    'MaxValue': 'string',
                    'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
                },
            ],
            'ContinuousParameterRanges': [
                {
                    'Name': 'string',
                    'MinValue': 'string',
                    'MaxValue': 'string',
                    'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
                },
            ],
            'CategoricalParameterRanges': [
                {
                    'Name': 'string',
                    'Values': [
                        'string',
                    ]
                },
            ]
        },
        'TrainingJobEarlyStoppingType': 'Off'|'Auto',
        'TuningJobCompletionCriteria': {
            'TargetObjectiveMetricValue': ...
        }
    },
    TrainingJobDefinition={
        'DefinitionName': 'string',
        'TuningObjective': {
            'Type': 'Maximize'|'Minimize',
            'MetricName': 'string'
        },
        'HyperParameterRanges': {
            'IntegerParameterRanges': [
                {
                    'Name': 'string',
                    'MinValue': 'string',
                    'MaxValue': 'string',
                    'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
                },
            ],
            'ContinuousParameterRanges': [
                {
                    'Name': 'string',
                    'MinValue': 'string',
                    'MaxValue': 'string',
                    'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
                },
            ],
            'CategoricalParameterRanges': [
                {
                    'Name': 'string',
                    'Values': [
                        'string',
                    ]
                },
            ]
        },
        'StaticHyperParameters': {
            'string': 'string'
        },
        'AlgorithmSpecification': {
            'TrainingImage': 'string',
            'TrainingInputMode': 'Pipe'|'File'|'FastFile',
            'AlgorithmName': 'string',
            'MetricDefinitions': [
                {
                    'Name': 'string',
                    'Regex': 'string'
                },
            ]
        },
        'RoleArn': 'string',
        'InputDataConfig': [
            {
                'ChannelName': 'string',
                'DataSource': {
                    'S3DataSource': {
                        'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
                        'S3Uri': 'string',
                        'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
                        'AttributeNames': [
                            'string',
                        ],
                        'InstanceGroupNames': [
                            'string',
                        ]
                    },
                    'FileSystemDataSource': {
                        'FileSystemId': 'string',
                        'FileSystemAccessMode': 'rw'|'ro',
                        'FileSystemType': 'EFS'|'FSxLustre',
                        'DirectoryPath': 'string'
                    }
                },
                'ContentType': 'string',
                'CompressionType': 'None'|'Gzip',
                'RecordWrapperType': 'None'|'RecordIO',
                'InputMode': 'Pipe'|'File'|'FastFile',
                'ShuffleConfig': {
                    'Seed': 123
                }
            },
        ],
        'VpcConfig': {
            'SecurityGroupIds': [
                'string',
            ],
            'Subnets': [
                'string',
            ]
        },
        'OutputDataConfig': {
            'KmsKeyId': 'string',
            'S3OutputPath': 'string'
        },
        'ResourceConfig': {
            'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge',
            'InstanceCount': 123,
            'VolumeSizeInGB': 123,
            'VolumeKmsKeyId': 'string',
            'InstanceGroups': [
                {
                    'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge',
                    'InstanceCount': 123,
                    'InstanceGroupName': 'string'
                },
            ],
            'KeepAlivePeriodInSeconds': 123
        },
        'StoppingCondition': {
            'MaxRuntimeInSeconds': 123,
            'MaxWaitTimeInSeconds': 123
        },
        'EnableNetworkIsolation': True|False,
        'EnableInterContainerTrafficEncryption': True|False,
        'EnableManagedSpotTraining': True|False,
        'CheckpointConfig': {
            'S3Uri': 'string',
            'LocalPath': 'string'
        },
        'RetryStrategy': {
            'MaximumRetryAttempts': 123
        },
        'HyperParameterTuningResourceConfig': {
            'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge',
            'InstanceCount': 123,
            'VolumeSizeInGB': 123,
            'VolumeKmsKeyId': 'string',
            'AllocationStrategy': 'Prioritized',
            'InstanceConfigs': [
                {
                    'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge',
                    'InstanceCount': 123,
                    'VolumeSizeInGB': 123
                },
            ]
        }
    },
    TrainingJobDefinitions=[
        {
            'DefinitionName': 'string',
            'TuningObjective': {
                'Type': 'Maximize'|'Minimize',
                'MetricName': 'string'
            },
            'HyperParameterRanges': {
                'IntegerParameterRanges': [
                    {
                        'Name': 'string',
                        'MinValue': 'string',
                        'MaxValue': 'string',
                        'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
                    },
                ],
                'ContinuousParameterRanges': [
                    {
                        'Name': 'string',
                        'MinValue': 'string',
                        'MaxValue': 'string',
                        'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
                    },
                ],
                'CategoricalParameterRanges': [
                    {
                        'Name': 'string',
                        'Values': [
                            'string',
                        ]
                    },
                ]
            },
            'StaticHyperParameters': {
                'string': 'string'
            },
            'AlgorithmSpecification': {
                'TrainingImage': 'string',
                'TrainingInputMode': 'Pipe'|'File'|'FastFile',
                'AlgorithmName': 'string',
                'MetricDefinitions': [
                    {
                        'Name': 'string',
                        'Regex': 'string'
                    },
                ]
            },
            'RoleArn': 'string',
            'InputDataConfig': [
                {
                    'ChannelName': 'string',
                    'DataSource': {
                        'S3DataSource': {
                            'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
                            'S3Uri': 'string',
                            'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
                            'AttributeNames': [
                                'string',
                            ],
                            'InstanceGroupNames': [
                                'string',
                            ]
                        },
                        'FileSystemDataSource': {
                            'FileSystemId': 'string',
                            'FileSystemAccessMode': 'rw'|'ro',
                            'FileSystemType': 'EFS'|'FSxLustre',
                            'DirectoryPath': 'string'
                        }
                    },
                    'ContentType': 'string',
                    'CompressionType': 'None'|'Gzip',
                    'RecordWrapperType': 'None'|'RecordIO',
                    'InputMode': 'Pipe'|'File'|'FastFile',
                    'ShuffleConfig': {
                        'Seed': 123
                    }
                },
            ],
            'VpcConfig': {
                'SecurityGroupIds': [
                    'string',
                ],
                'Subnets': [
                    'string',
                ]
            },
            'OutputDataConfig': {
                'KmsKeyId': 'string',
                'S3OutputPath': 'string'
            },
            'ResourceConfig': {
                'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge',
                'InstanceCount': 123,
                'VolumeSizeInGB': 123,
                'VolumeKmsKeyId': 'string',
                'InstanceGroups': [
                    {
                        'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge',
                        'InstanceCount': 123,
                        'InstanceGroupName': 'string'
                    },
                ],
                'KeepAlivePeriodInSeconds': 123
            },
            'StoppingCondition': {
                'MaxRuntimeInSeconds': 123,
                'MaxWaitTimeInSeconds': 123
            },
            'EnableNetworkIsolation': True|False,
            'EnableInterContainerTrafficEncryption': True|False,
            'EnableManagedSpotTraining': True|False,
            'CheckpointConfig': {
                'S3Uri': 'string',
                'LocalPath': 'string'
            },
            'RetryStrategy': {
                'MaximumRetryAttempts': 123
            },
            'HyperParameterTuningResourceConfig': {
                'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge',
                'InstanceCount': 123,
                'VolumeSizeInGB': 123,
                'VolumeKmsKeyId': 'string',
                'AllocationStrategy': 'Prioritized',
                'InstanceConfigs': [
                    {
                        'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge',
                        'InstanceCount': 123,
                        'VolumeSizeInGB': 123
                    },
                ]
            }
        },
    ],
    WarmStartConfig={
        'ParentHyperParameterTuningJobs': [
            {
                'HyperParameterTuningJobName': 'string'
            },
        ],
        'WarmStartType': 'IdenticalDataAndAlgorithm'|'TransferLearning'
    },
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ]
)
Parameters
  • HyperParameterTuningJobName (string) --

    [REQUIRED]

    The name of the tuning job. This name is the prefix for the names of all training jobs that this tuning job launches. The name must be unique within the same Amazon Web Services account and Amazon Web Services Region. The name must have 1 to 32 characters. Valid characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case sensitive.

  • HyperParameterTuningJobConfig (dict) --

    [REQUIRED]

    The HyperParameterTuningJobConfig object that describes the tuning job, including the search strategy, the objective metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the tuning job. For more information, see How Hyperparameter Tuning Works.

    • Strategy (string) -- [REQUIRED]

      Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job it launches. For information about search strategies, see How Hyperparameter Tuning Works.

    • StrategyConfig (dict) --

      The configuration for the Hyperband optimization strategy. This parameter should be provided only if Hyperband is selected as the strategy for HyperParameterTuningJobConfig .

      • HyperbandStrategyConfig (dict) --

        The configuration for the object that specifies the Hyperband strategy. This parameter is only supported for the Hyperband selection for Strategy within the HyperParameterTuningJobConfig API.

        • MinResource (integer) --

          The minimum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter tuning job. If the value for MinResource has not been reached, the training job will not be stopped by Hyperband .

        • MaxResource (integer) --

          The maximum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter tuning job. Once a job reaches the MaxResource value, it is stopped. If a value for MaxResource is not provided, and Hyperband is selected as the hyperparameter tuning strategy, HyperbandTrainingJ attempts to infer MaxResource from the following keys (if present) in StaticsHyperParameters:

          • epochs
          • numepochs
          • n-epochs
          • n_epochs
          • num_epochs

          If HyperbandStrategyConfig is unable to infer a value for MaxResource , it generates a validation error. The maximum value is 20,000 epochs. All metrics that correspond to an objective metric are used to derive early stopping decisions. For distributive training jobs, ensure that duplicate metrics are not printed in the logs across the individual nodes in a training job. If multiple nodes are publishing duplicate or incorrect metrics, training jobs may make an incorrect stopping decision and stop the job prematurely.

    • HyperParameterTuningJobObjective (dict) --

      The HyperParameterTuningJobObjective object that specifies the objective metric for this tuning job.

      • Type (string) -- [REQUIRED]

        Whether to minimize or maximize the objective metric.

      • MetricName (string) -- [REQUIRED]

        The name of the metric to use for the objective metric.

    • ResourceLimits (dict) -- [REQUIRED]

      The ResourceLimits object that specifies the maximum number of training jobs and parallel training jobs for this tuning job.

      • MaxNumberOfTrainingJobs (integer) --

        The maximum number of training jobs that a hyperparameter tuning job can launch.

      • MaxParallelTrainingJobs (integer) -- [REQUIRED]

        The maximum number of concurrent training jobs that a hyperparameter tuning job can launch.

    • ParameterRanges (dict) --

      The ParameterRanges object that specifies the ranges of hyperparameters that this tuning job searches.

      • IntegerParameterRanges (list) --

        The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.

        • (dict) --

          For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches.

          • Name (string) -- [REQUIRED]

            The name of the hyperparameter to search.

          • MinValue (string) -- [REQUIRED]

            The minimum value of the hyperparameter to search.

          • MaxValue (string) -- [REQUIRED]

            The maximum value of the hyperparameter to search.

          • ScalingType (string) --

            The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:

            Auto

            SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.

            Linear

            Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.

            Logarithmic

            Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.

            Logarithmic scaling works only for ranges that have only values greater than 0.

      • ContinuousParameterRanges (list) --

        The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.

        • (dict) --

          A list of continuous hyperparameters to tune.

          • Name (string) -- [REQUIRED]

            The name of the continuous hyperparameter to tune.

          • MinValue (string) -- [REQUIRED]

            The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValue for tuning.

          • MaxValue (string) -- [REQUIRED]

            The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.

          • ScalingType (string) --

            The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:

            Auto

            SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.

            Linear

            Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.

            Logarithmic

            Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.

            Logarithmic scaling works only for ranges that have only values greater than 0.

            ReverseLogarithmic

            Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.

            Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.

      • CategoricalParameterRanges (list) --

        The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.

        • (dict) --

          A list of categorical hyperparameters to tune.

          • Name (string) -- [REQUIRED]

            The name of the categorical hyperparameter to tune.

          • Values (list) -- [REQUIRED]

            A list of the categories for the hyperparameter.

            • (string) --
    • TrainingJobEarlyStoppingType (string) --

      Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job. Because the Hyperband strategy has its own advanced internal early stopping mechanism, TrainingJobEarlyStoppingType must be OFF to use Hyperband . This parameter can take on one of the following values (the default value is OFF ):

      OFF

      Training jobs launched by the hyperparameter tuning job do not use early stopping.

      AUTO

      SageMaker stops training jobs launched by the hyperparameter tuning job when they are unlikely to perform better than previously completed training jobs. For more information, see Stop Training Jobs Early.

    • TuningJobCompletionCriteria (dict) --

      The tuning job's completion criteria.

      • TargetObjectiveMetricValue (float) -- [REQUIRED]

        The value of the objective metric.

  • TrainingJobDefinition (dict) --

    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.

    • DefinitionName (string) --

      The job definition name.

    • TuningObjective (dict) --

      Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the Type parameter.

      • Type (string) -- [REQUIRED]

        Whether to minimize or maximize the objective metric.

      • MetricName (string) -- [REQUIRED]

        The name of the metric to use for the objective metric.

    • HyperParameterRanges (dict) --

      Specifies ranges of integer, continuous, and categorical hyperparameters that a hyperparameter tuning job searches. The hyperparameter tuning job launches training jobs with hyperparameter values within these ranges to find the combination of values that result in the training job with the best performance as measured by the objective metric of the hyperparameter tuning job.

      Note

      The maximum number of items specified for Array Members refers to the maximum number of hyperparameters for each range and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of hyperparameters for all the ranges can't exceed the maximum number specified.

      • IntegerParameterRanges (list) --

        The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.

        • (dict) --

          For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches.

          • Name (string) -- [REQUIRED]

            The name of the hyperparameter to search.

          • MinValue (string) -- [REQUIRED]

            The minimum value of the hyperparameter to search.

          • MaxValue (string) -- [REQUIRED]

            The maximum value of the hyperparameter to search.

          • ScalingType (string) --

            The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:

            Auto

            SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.

            Linear

            Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.

            Logarithmic

            Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.

            Logarithmic scaling works only for ranges that have only values greater than 0.

      • ContinuousParameterRanges (list) --

        The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.

        • (dict) --

          A list of continuous hyperparameters to tune.

          • Name (string) -- [REQUIRED]

            The name of the continuous hyperparameter to tune.

          • MinValue (string) -- [REQUIRED]

            The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValue for tuning.

          • MaxValue (string) -- [REQUIRED]

            The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.

          • ScalingType (string) --

            The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:

            Auto

            SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.

            Linear

            Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.

            Logarithmic

            Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.

            Logarithmic scaling works only for ranges that have only values greater than 0.

            ReverseLogarithmic

            Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.

            Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.

      • CategoricalParameterRanges (list) --

        The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.

        • (dict) --

          A list of categorical hyperparameters to tune.

          • Name (string) -- [REQUIRED]

            The name of the categorical hyperparameter to tune.

          • Values (list) -- [REQUIRED]

            A list of the categories for the hyperparameter.

            • (string) --
    • StaticHyperParameters (dict) --

      Specifies the values of hyperparameters that do not change for the tuning job.

      • (string) --
        • (string) --
    • AlgorithmSpecification (dict) -- [REQUIRED]

      The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.

      • TrainingImage (string) --

        The registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.

      • TrainingInputMode (string) -- [REQUIRED]

        The training input mode that the algorithm supports. For more information about input modes, see Algorithms.

        Pipe mode

        If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.

        File mode

        If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.

        You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.

        For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. 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 when one host in a training cluster is overloaded, thus becoming a bottleneck in training.

        FastFile mode

        If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.

        FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.
      • AlgorithmName (string) --

        The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this parameter, do not specify a value for TrainingImage .

      • MetricDefinitions (list) --

        An array of MetricDefinition objects that specify the metrics that the algorithm emits.

        • (dict) --

          Specifies a metric that the training algorithm writes to stderr or stdout . SageMakerhyperparameter tuning captures all defined metrics. You specify one metric that a hyperparameter tuning job uses as its objective metric to choose the best training job.

          • Name (string) -- [REQUIRED]

            The name of the metric.

          • Regex (string) -- [REQUIRED]

            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.

    • RoleArn (string) -- [REQUIRED]

      The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.

    • InputDataConfig (list) --

      An array of Channel objects that specify the input for the training jobs that the tuning job launches.

      • (dict) --

        A channel is a named input source that training algorithms can consume.

        • ChannelName (string) -- [REQUIRED]

          The name of the channel.

        • DataSource (dict) -- [REQUIRED]

          The location of the channel data.

          • S3DataSource (dict) --

            The S3 location of the data source that is associated with a channel.

            • S3DataType (string) -- [REQUIRED]

              If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.

              If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.

              If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .

            • S3Uri (string) -- [REQUIRED]

              Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:

              • A key name prefix might look like this: s3://bucketname/exampleprefix
              • A manifest might look like this: s3://bucketname/example.manifest A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri . Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets. The following code example shows a valid manifest format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] This JSON is equivalent to the following S3Uri list: s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
            • S3DataDistributionType (string) --

              If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .

              If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.

              Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.

              In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.

            • AttributeNames (list) --

              A list of one or more attribute names to use that are found in a specified augmented manifest file.

              • (string) --
            • InstanceGroupNames (list) --

              A list of names of instance groups that get data from the S3 data source.

              • (string) --
          • FileSystemDataSource (dict) --

            The file system that is associated with a channel.

            • FileSystemId (string) -- [REQUIRED]

              The file system id.

            • FileSystemAccessMode (string) -- [REQUIRED]

              The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.

            • FileSystemType (string) -- [REQUIRED]

              The file system type.

            • DirectoryPath (string) -- [REQUIRED]

              The full path to the directory to associate with the channel.

        • ContentType (string) --

          The MIME type of the data.

        • CompressionType (string) --

          If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.

        • RecordWrapperType (string) --

          Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO.

          In File mode, leave this field unset or set it to None.

        • InputMode (string) --

          (Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.

          To use a model for incremental training, choose File input model.

        • ShuffleConfig (dict) --

          A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.

          For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.

          • Seed (integer) -- [REQUIRED]

            Determines the shuffling order in ShuffleConfig value.

    • VpcConfig (dict) --

      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.

      • SecurityGroupIds (list) -- [REQUIRED]

        The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.

        • (string) --
      • Subnets (list) -- [REQUIRED]

        The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.

        • (string) --
    • OutputDataConfig (dict) -- [REQUIRED]

      Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.

      • KmsKeyId (string) --

        The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:

        • // KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
        • // Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
        • // KMS Key Alias "alias/ExampleAlias"
        • // Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"

        If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig . If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.

        The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .

      • S3OutputPath (string) -- [REQUIRED]

        Identifies the S3 path where you want SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .

    • ResourceConfig (dict) --

      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 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.

      Note

      If you want to use hyperparameter optimization with instance type flexibility, use HyperParameterTuningResourceConfig instead.

      • InstanceType (string) --

        The ML compute instance type.

      • InstanceCount (integer) --

        The number of ML compute instances to use. For distributed training, provide a value greater than 1.

      • VolumeSizeInGB (integer) -- [REQUIRED]

        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.

        When using an ML instance with NVMe SSD volumes, SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include ml.p4d , ml.g4dn , and ml.g5 .

        When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through VolumeSizeInGB in the ResourceConfig API. For example, ML instance families that use EBS volumes include ml.c5 and ml.p2 .

        To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types.

        To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs.

      • VolumeKmsKeyId (string) --

        The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.

        Note

        Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.

        For a list of instance types that support local instance storage, see Instance Store Volumes.

        For more information about local instance storage encryption, see SSD Instance Store Volumes.

        The VolumeKmsKeyId can be in any of the following formats:

        • // KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
        • // Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
      • InstanceGroups (list) --

        The configuration of a heterogeneous cluster in JSON format.

        • (dict) --

          Defines an instance group for heterogeneous cluster training. When requesting a training job using the CreateTrainingJob API, you can configure multiple instance groups .

          • InstanceType (string) -- [REQUIRED]

            Specifies the instance type of the instance group.

          • InstanceCount (integer) -- [REQUIRED]

            Specifies the number of instances of the instance group.

          • InstanceGroupName (string) -- [REQUIRED]

            Specifies the name of the instance group.

      • KeepAlivePeriodInSeconds (integer) --

        The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.

    • StoppingCondition (dict) -- [REQUIRED]

      Specifies a limit to how long a model hyperparameter training job can run. It also specifies how long a managed spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.

      • MaxRuntimeInSeconds (integer) --

        The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.

        For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.

        For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.

        The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.

      • MaxWaitTimeInSeconds (integer) --

        The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, SageMaker ends the job.

        When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.

    • EnableNetworkIsolation (boolean) --

      Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.

    • EnableInterContainerTrafficEncryption (boolean) --

      To encrypt all communications between ML compute instances in distributed training, choose True . Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.

    • EnableManagedSpotTraining (boolean) --

      A Boolean indicating whether managed spot training is enabled (True ) or not (False ).

    • CheckpointConfig (dict) --

      Contains information about the output location for managed spot training checkpoint data.

      • S3Uri (string) -- [REQUIRED]

        Identifies the S3 path where you want SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix .

      • LocalPath (string) --

        (Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/ .

    • RetryStrategy (dict) --

      The number of times to retry the job when the job fails due to an InternalServerError .

      • MaximumRetryAttempts (integer) -- [REQUIRED]

        The number of times to retry the job. When the job is retried, it's SecondaryStatus is changed to STARTING .

    • HyperParameterTuningResourceConfig (dict) --

      The configuration for the hyperparameter tuning resources, including the compute instances and storage volumes, used for training jobs launched by the tuning job. By default, storage volumes hold model artifacts and incremental states. Choose File for TrainingInputMode in the AlgorithmSpecification parameter to additionally store training data in the storage volume (optional).

      • InstanceType (string) --

        The instance type used to run hyperparameter optimization tuning jobs. See descriptions of instance types for more information.

      • InstanceCount (integer) --

        The number of compute instances of type InstanceType to use. For distributed training, select a value greater than 1.

      • VolumeSizeInGB (integer) --

        The volume size in GB for the storage volume to be used in processing hyperparameter optimization jobs (optional). These volumes store model artifacts, incremental states and optionally, scratch space for training algorithms. Do not provide a value for this parameter if a value for InstanceConfigs is also specified.

        Some instance types have a fixed total local storage size. If you select one of these instances for training, VolumeSizeInGB cannot be greater than this total size. For a list of instance types with local instance storage and their sizes, see instance store volumes.

        Note

        SageMaker supports only the General Purpose SSD (gp2) storage volume type.

      • VolumeKmsKeyId (string) --

        A key used by Amazon Web Services Key Management Service to encrypt data on the storage volume attached to the compute instances used to run the training job. You can use either of the following formats to specify a key.

        KMS Key ID:

        "1234abcd-12ab-34cd-56ef-1234567890ab"

        Amazon Resource Name (ARN) of a KMS key:

        "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

        Some instances use local storage, which use a hardware module to encrypt storage volumes. If you choose one of these instance types, you cannot request a VolumeKmsKeyId . For a list of instance types that use local storage, see instance store volumes. For more information about Amazon Web Services Key Management Service, see KMS encryption for more information.

      • AllocationStrategy (string) --

        The strategy that determines the order of preference for resources specified in InstanceConfigs used in hyperparameter optimization.

      • InstanceConfigs (list) --

        A list containing the configuration(s) for one or more resources for processing hyperparameter jobs. These resources include compute instances and storage volumes to use in model training jobs launched by hyperparameter tuning jobs. The AllocationStrategy controls the order in which multiple configurations provided in InstanceConfigs are used.

        Note

        If you only want to use a single instance configuration inside the HyperParameterTuningResourceConfig API, do not provide a value for InstanceConfigs . Instead, use InstanceType , VolumeSizeInGB and InstanceCount . If you use InstanceConfigs , do not provide values for InstanceType , VolumeSizeInGB or InstanceCount .

        • (dict) --

          The configuration for hyperparameter tuning resources for use in training jobs launched by the tuning job. These resources include compute instances and storage volumes. Specify one or more compute instance configurations and allocation strategies to select resources (optional).

          • InstanceType (string) -- [REQUIRED]

            The instance type used for processing of hyperparameter optimization jobs. Choose from general purpose (no GPUs) instance types: ml.m5.xlarge, ml.m5.2xlarge, and ml.m5.4xlarge or compute optimized (no GPUs) instance types: ml.c5.xlarge and ml.c5.2xlarge. For more information about instance types, see instance type descriptions.

          • InstanceCount (integer) -- [REQUIRED]

            The number of instances of the type specified by InstanceType . Choose an instance count larger than 1 for distributed training algorithms. See SageMaker distributed training jobs for more information.

          • VolumeSizeInGB (integer) -- [REQUIRED]

            The volume size in GB of the data to be processed for hyperparameter optimization (optional).

  • TrainingJobDefinitions (list) --

    A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.

    • (dict) --

      Defines the training jobs launched by a hyperparameter tuning job.

      • DefinitionName (string) --

        The job definition name.

      • TuningObjective (dict) --

        Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the Type parameter.

        • Type (string) -- [REQUIRED]

          Whether to minimize or maximize the objective metric.

        • MetricName (string) -- [REQUIRED]

          The name of the metric to use for the objective metric.

      • HyperParameterRanges (dict) --

        Specifies ranges of integer, continuous, and categorical hyperparameters that a hyperparameter tuning job searches. The hyperparameter tuning job launches training jobs with hyperparameter values within these ranges to find the combination of values that result in the training job with the best performance as measured by the objective metric of the hyperparameter tuning job.

        Note

        The maximum number of items specified for Array Members refers to the maximum number of hyperparameters for each range and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of hyperparameters for all the ranges can't exceed the maximum number specified.

        • IntegerParameterRanges (list) --

          The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.

          • (dict) --

            For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches.

            • Name (string) -- [REQUIRED]

              The name of the hyperparameter to search.

            • MinValue (string) -- [REQUIRED]

              The minimum value of the hyperparameter to search.

            • MaxValue (string) -- [REQUIRED]

              The maximum value of the hyperparameter to search.

            • ScalingType (string) --

              The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:

              Auto

              SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.

              Linear

              Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.

              Logarithmic

              Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.

              Logarithmic scaling works only for ranges that have only values greater than 0.

        • ContinuousParameterRanges (list) --

          The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.

          • (dict) --

            A list of continuous hyperparameters to tune.

            • Name (string) -- [REQUIRED]

              The name of the continuous hyperparameter to tune.

            • MinValue (string) -- [REQUIRED]

              The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValue for tuning.

            • MaxValue (string) -- [REQUIRED]

              The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.

            • ScalingType (string) --

              The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:

              Auto

              SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.

              Linear

              Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.

              Logarithmic

              Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.

              Logarithmic scaling works only for ranges that have only values greater than 0.

              ReverseLogarithmic

              Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.

              Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.

        • CategoricalParameterRanges (list) --

          The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.

          • (dict) --

            A list of categorical hyperparameters to tune.

            • Name (string) -- [REQUIRED]

              The name of the categorical hyperparameter to tune.

            • Values (list) -- [REQUIRED]

              A list of the categories for the hyperparameter.

              • (string) --
      • StaticHyperParameters (dict) --

        Specifies the values of hyperparameters that do not change for the tuning job.

        • (string) --
          • (string) --
      • AlgorithmSpecification (dict) -- [REQUIRED]

        The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.

        • TrainingImage (string) --

          The registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.

        • TrainingInputMode (string) -- [REQUIRED]

          The training input mode that the algorithm supports. For more information about input modes, see Algorithms.

          Pipe mode

          If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.

          File mode

          If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.

          You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.

          For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. 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 when one host in a training cluster is overloaded, thus becoming a bottleneck in training.

          FastFile mode

          If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.

          FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.
        • AlgorithmName (string) --

          The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this parameter, do not specify a value for TrainingImage .

        • MetricDefinitions (list) --

          An array of MetricDefinition objects that specify the metrics that the algorithm emits.

          • (dict) --

            Specifies a metric that the training algorithm writes to stderr or stdout . SageMakerhyperparameter tuning captures all defined metrics. You specify one metric that a hyperparameter tuning job uses as its objective metric to choose the best training job.

            • Name (string) -- [REQUIRED]

              The name of the metric.

            • Regex (string) -- [REQUIRED]

              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.

      • RoleArn (string) -- [REQUIRED]

        The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.

      • InputDataConfig (list) --

        An array of Channel objects that specify the input for the training jobs that the tuning job launches.

        • (dict) --

          A channel is a named input source that training algorithms can consume.

          • ChannelName (string) -- [REQUIRED]

            The name of the channel.

          • DataSource (dict) -- [REQUIRED]

            The location of the channel data.

            • S3DataSource (dict) --

              The S3 location of the data source that is associated with a channel.

              • S3DataType (string) -- [REQUIRED]

                If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.

                If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.

                If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .

              • S3Uri (string) -- [REQUIRED]

                Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:

                • A key name prefix might look like this: s3://bucketname/exampleprefix
                • A manifest might look like this: s3://bucketname/example.manifest A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri . Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets. The following code example shows a valid manifest format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] This JSON is equivalent to the following S3Uri list: s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
              • S3DataDistributionType (string) --

                If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .

                If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.

                Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.

                In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.

              • AttributeNames (list) --

                A list of one or more attribute names to use that are found in a specified augmented manifest file.

                • (string) --
              • InstanceGroupNames (list) --

                A list of names of instance groups that get data from the S3 data source.

                • (string) --
            • FileSystemDataSource (dict) --

              The file system that is associated with a channel.

              • FileSystemId (string) -- [REQUIRED]

                The file system id.

              • FileSystemAccessMode (string) -- [REQUIRED]

                The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.

              • FileSystemType (string) -- [REQUIRED]

                The file system type.

              • DirectoryPath (string) -- [REQUIRED]

                The full path to the directory to associate with the channel.

          • ContentType (string) --

            The MIME type of the data.

          • CompressionType (string) --

            If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.

          • RecordWrapperType (string) --

            Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO.

            In File mode, leave this field unset or set it to None.

          • InputMode (string) --

            (Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.

            To use a model for incremental training, choose File input model.

          • ShuffleConfig (dict) --

            A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.

            For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.

            • Seed (integer) -- [REQUIRED]

              Determines the shuffling order in ShuffleConfig value.

      • VpcConfig (dict) --

        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.

        • SecurityGroupIds (list) -- [REQUIRED]

          The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.

          • (string) --
        • Subnets (list) -- [REQUIRED]

          The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.

          • (string) --
      • OutputDataConfig (dict) -- [REQUIRED]

        Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.

        • KmsKeyId (string) --

          The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:

          • // KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
          • // Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
          • // KMS Key Alias "alias/ExampleAlias"
          • // Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"

          If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig . If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.

          The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .

        • S3OutputPath (string) -- [REQUIRED]

          Identifies the S3 path where you want SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .

      • ResourceConfig (dict) --

        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 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.

        Note

        If you want to use hyperparameter optimization with instance type flexibility, use HyperParameterTuningResourceConfig instead.

        • InstanceType (string) --

          The ML compute instance type.

        • InstanceCount (integer) --

          The number of ML compute instances to use. For distributed training, provide a value greater than 1.

        • VolumeSizeInGB (integer) -- [REQUIRED]

          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.

          When using an ML instance with NVMe SSD volumes, SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include ml.p4d , ml.g4dn , and ml.g5 .

          When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through VolumeSizeInGB in the ResourceConfig API. For example, ML instance families that use EBS volumes include ml.c5 and ml.p2 .

          To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types.

          To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs.

        • VolumeKmsKeyId (string) --

          The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.

          Note

          Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.

          For a list of instance types that support local instance storage, see Instance Store Volumes.

          For more information about local instance storage encryption, see SSD Instance Store Volumes.

          The VolumeKmsKeyId can be in any of the following formats:

          • // KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
          • // Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
        • InstanceGroups (list) --

          The configuration of a heterogeneous cluster in JSON format.

          • (dict) --

            Defines an instance group for heterogeneous cluster training. When requesting a training job using the CreateTrainingJob API, you can configure multiple instance groups .

            • InstanceType (string) -- [REQUIRED]

              Specifies the instance type of the instance group.

            • InstanceCount (integer) -- [REQUIRED]

              Specifies the number of instances of the instance group.

            • InstanceGroupName (string) -- [REQUIRED]

              Specifies the name of the instance group.

        • KeepAlivePeriodInSeconds (integer) --

          The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.

      • StoppingCondition (dict) -- [REQUIRED]

        Specifies a limit to how long a model hyperparameter training job can run. It also specifies how long a managed spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.

        • MaxRuntimeInSeconds (integer) --

          The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.

          For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.

          For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.

          The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.

        • MaxWaitTimeInSeconds (integer) --

          The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, SageMaker ends the job.

          When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.

      • EnableNetworkIsolation (boolean) --

        Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.

      • EnableInterContainerTrafficEncryption (boolean) --

        To encrypt all communications between ML compute instances in distributed training, choose True . Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.

      • EnableManagedSpotTraining (boolean) --

        A Boolean indicating whether managed spot training is enabled (True ) or not (False ).

      • CheckpointConfig (dict) --

        Contains information about the output location for managed spot training checkpoint data.

        • S3Uri (string) -- [REQUIRED]

          Identifies the S3 path where you want SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix .

        • LocalPath (string) --

          (Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/ .

      • RetryStrategy (dict) --

        The number of times to retry the job when the job fails due to an InternalServerError .

        • MaximumRetryAttempts (integer) -- [REQUIRED]

          The number of times to retry the job. When the job is retried, it's SecondaryStatus is changed to STARTING .

      • HyperParameterTuningResourceConfig (dict) --

        The configuration for the hyperparameter tuning resources, including the compute instances and storage volumes, used for training jobs launched by the tuning job. By default, storage volumes hold model artifacts and incremental states. Choose File for TrainingInputMode in the AlgorithmSpecification parameter to additionally store training data in the storage volume (optional).

        • InstanceType (string) --

          The instance type used to run hyperparameter optimization tuning jobs. See descriptions of instance types for more information.

        • InstanceCount (integer) --

          The number of compute instances of type InstanceType to use. For distributed training, select a value greater than 1.

        • VolumeSizeInGB (integer) --

          The volume size in GB for the storage volume to be used in processing hyperparameter optimization jobs (optional). These volumes store model artifacts, incremental states and optionally, scratch space for training algorithms. Do not provide a value for this parameter if a value for InstanceConfigs is also specified.

          Some instance types have a fixed total local storage size. If you select one of these instances for training, VolumeSizeInGB cannot be greater than this total size. For a list of instance types with local instance storage and their sizes, see instance store volumes.

          Note

          SageMaker supports only the General Purpose SSD (gp2) storage volume type.

        • VolumeKmsKeyId (string) --

          A key used by Amazon Web Services Key Management Service to encrypt data on the storage volume attached to the compute instances used to run the training job. You can use either of the following formats to specify a key.

          KMS Key ID:

          "1234abcd-12ab-34cd-56ef-1234567890ab"

          Amazon Resource Name (ARN) of a KMS key:

          "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

          Some instances use local storage, which use a hardware module to encrypt storage volumes. If you choose one of these instance types, you cannot request a VolumeKmsKeyId . For a list of instance types that use local storage, see instance store volumes. For more information about Amazon Web Services Key Management Service, see KMS encryption for more information.

        • AllocationStrategy (string) --

          The strategy that determines the order of preference for resources specified in InstanceConfigs used in hyperparameter optimization.

        • InstanceConfigs (list) --

          A list containing the configuration(s) for one or more resources for processing hyperparameter jobs. These resources include compute instances and storage volumes to use in model training jobs launched by hyperparameter tuning jobs. The AllocationStrategy controls the order in which multiple configurations provided in InstanceConfigs are used.

          Note

          If you only want to use a single instance configuration inside the HyperParameterTuningResourceConfig API, do not provide a value for InstanceConfigs . Instead, use InstanceType , VolumeSizeInGB and InstanceCount . If you use InstanceConfigs , do not provide values for InstanceType , VolumeSizeInGB or InstanceCount .

          • (dict) --

            The configuration for hyperparameter tuning resources for use in training jobs launched by the tuning job. These resources include compute instances and storage volumes. Specify one or more compute instance configurations and allocation strategies to select resources (optional).

            • InstanceType (string) -- [REQUIRED]

              The instance type used for processing of hyperparameter optimization jobs. Choose from general purpose (no GPUs) instance types: ml.m5.xlarge, ml.m5.2xlarge, and ml.m5.4xlarge or compute optimized (no GPUs) instance types: ml.c5.xlarge and ml.c5.2xlarge. For more information about instance types, see instance type descriptions.

            • InstanceCount (integer) -- [REQUIRED]

              The number of instances of the type specified by InstanceType . Choose an instance count larger than 1 for distributed training algorithms. See SageMaker distributed training jobs for more information.

            • VolumeSizeInGB (integer) -- [REQUIRED]

              The volume size in GB of the data to be processed for hyperparameter optimization (optional).

  • WarmStartConfig (dict) --

    Specifies the configuration for starting the hyperparameter tuning job using one or more previous tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.

    All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric. If you specify IDENTICAL_DATA_AND_ALGORITHM as the WarmStartType value for the warm start configuration, the training job that performs the best in the new tuning job is compared to the best training jobs from the parent tuning jobs. From these, the training job that performs the best as measured by the objective metric is returned as the overall best training job.

    Note

    All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count against the limit of training jobs for the tuning job.

    • ParentHyperParameterTuningJobs (list) -- [REQUIRED]

      An array of hyperparameter tuning jobs that are used as the starting point for the new hyperparameter tuning job. For more information about warm starting a hyperparameter tuning job, see Using a Previous Hyperparameter Tuning Job as a Starting Point.

      Hyperparameter tuning jobs created before October 1, 2018 cannot be used as parent jobs for warm start tuning jobs.

      • (dict) --

        A previously completed or stopped hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job.

        • HyperParameterTuningJobName (string) --

          The name of the hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job.

    • WarmStartType (string) -- [REQUIRED]

      Specifies one of the following:

      IDENTICAL_DATA_AND_ALGORITHM

      The new hyperparameter tuning job uses the same input data and training image as the parent tuning jobs. You can change the hyperparameter ranges to search and the maximum number of training jobs that the hyperparameter tuning job launches. You cannot use a new version of the training algorithm, unless the changes in the new version do not affect the algorithm itself. For example, changes that improve logging or adding support for a different data format are allowed. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.

      TRANSFER_LEARNING

      The new hyperparameter tuning job can include input data, hyperparameter ranges, maximum number of concurrent training jobs, and maximum number of training jobs that are different than those of its parent hyperparameter tuning jobs. The training image can also be a different version from the version used in the parent hyperparameter tuning job. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.

  • Tags (list) --

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

    Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.

    • (dict) --

      A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

      You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.

      For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.

      • Key (string) -- [REQUIRED]

        The tag key. Tag keys must be unique per resource.

      • Value (string) -- [REQUIRED]

        The tag value.

Return type

dict

Returns

Response Syntax

{
    'HyperParameterTuningJobArn': 'string'
}

Response Structure

  • (dict) --

    • HyperParameterTuningJobArn (string) --

      The Amazon Resource Name (ARN) of the tuning job. SageMaker assigns an ARN to a hyperparameter tuning job when you create it.

Exceptions

  • SageMaker.Client.exceptions.ResourceInUse
  • SageMaker.Client.exceptions.ResourceLimitExceeded
create_image(**kwargs)

Creates a custom SageMaker image. A SageMaker image is a set of image versions. Each image version represents a container image stored in Amazon Elastic Container Registry (ECR). For more information, see Bring your own SageMaker image.

See also: AWS API Documentation

Request Syntax

response = client.create_image(
    Description='string',
    DisplayName='string',
    ImageName='string',
    RoleArn='string',
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ]
)
Parameters
  • Description (string) -- The description of the image.
  • DisplayName (string) -- The display name of the image. If not provided, ImageName is displayed.
  • ImageName (string) --

    [REQUIRED]

    The name of the image. Must be unique to your account.

  • RoleArn (string) --

    [REQUIRED]

    The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.

  • Tags (list) --

    A list of tags to apply to the image.

    • (dict) --

      A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

      You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.

      For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.

      • Key (string) -- [REQUIRED]

        The tag key. Tag keys must be unique per resource.

      • Value (string) -- [REQUIRED]

        The tag value.

Return type

dict

Returns

Response Syntax

{
    'ImageArn': 'string'
}

Response Structure

  • (dict) --

    • ImageArn (string) --

      The Amazon Resource Name (ARN) of the image.

Exceptions

  • SageMaker.Client.exceptions.ResourceInUse
  • SageMaker.Client.exceptions.ResourceLimitExceeded
create_image_version(**kwargs)

Creates a version of the SageMaker image specified by ImageName . The version represents the Amazon Elastic Container Registry (ECR) container image specified by BaseImage .

See also: AWS API Documentation

Request Syntax

response = client.create_image_version(
    BaseImage='string',
    ClientToken='string',
    ImageName='string'
)
Parameters
  • BaseImage (string) --

    [REQUIRED]

    The registry path of the container image to use as the starting point for this version. The path is an Amazon Elastic Container Registry (ECR) URI in the following format:

    <acct-id>.dkr.ecr.<region>.amazonaws.com/<repo-name[:tag] or [@digest]>
  • ClientToken (string) --

    [REQUIRED]

    A unique ID. If not specified, the Amazon Web Services CLI and Amazon Web Services SDKs, such as the SDK for Python (Boto3), add a unique value to the call.

    This field is autopopulated if not provided.

  • ImageName (string) --

    [REQUIRED]

    The ImageName of the Image to create a version of.

Return type

dict

Returns

Response Syntax

{
    'ImageVersionArn': 'string'
}

Response Structure

  • (dict) --

    • ImageVersionArn (string) --

      The Amazon Resource Name (ARN) of the image version.

Exceptions

  • SageMaker.Client.exceptions.ResourceInUse
  • SageMaker.Client.exceptions.ResourceLimitExceeded
  • SageMaker.Client.exceptions.ResourceNotFound
create_inference_experiment(**kwargs)

Creates an inference experiment using the configurations specified in the request.

Use this API to schedule an experiment to compare model variants on a Amazon SageMaker inference endpoint. For more information about inference experiments, see Shadow tests.

Amazon SageMaker begins your experiment at the scheduled time and routes traffic to your endpoint's model variants based on your specified configuration.

While the experiment is in progress or after it has concluded, you can view metrics that compare your model variants. For more information, see View, monitor, and edit shadow tests.

See also: AWS API Documentation

Request Syntax

response = client.create_inference_experiment(
    Name='string',
    Type='ShadowMode',
    Schedule={
        'StartTime': datetime(2015, 1, 1),
        'EndTime': datetime(2015, 1, 1)
    },
    Description='string',
    RoleArn='string',
    EndpointName='string',
    ModelVariants=[
        {
            'ModelName': 'string',
            'VariantName': 'string',
            'InfrastructureConfig': {
                'InfrastructureType': 'RealTimeInference',
                'RealTimeInferenceConfig': {
                    'InstanceType': 'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge',
                    'InstanceCount': 123
                }
            }
        },
    ],
    DataStorageConfig={
        'Destination': 'string',
        'KmsKey': 'string',
        'ContentType': {
            'CsvContentTypes': [
                'string',
            ],
            'JsonContentTypes': [
                'string',
            ]
        }
    },
    ShadowModeConfig={
        'SourceModelVariantName': 'string',
        'ShadowModelVariants': [
            {
                'ShadowModelVariantName': 'string',
                'SamplingPercentage': 123
            },
        ]
    },
    KmsKey='string',
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ]
)
Parameters
  • Name (string) --

    [REQUIRED]

    The name for the inference experiment.

  • Type (string) --

    [REQUIRED]

    The type of the inference experiment that you want to run. The following types of experiments are possible:

    • ShadowMode : You can use this type to validate a shadow variant. For more information, see Shadow tests.
  • Schedule (dict) --

    The duration for which you want the inference experiment to run. If you don't specify this field, the experiment automatically concludes after 7 days.

    • StartTime (datetime) --

      The timestamp at which the inference experiment started or will start.

    • EndTime (datetime) --

      The timestamp at which the inference experiment ended or will end.

  • Description (string) -- A description for the inference experiment.
  • RoleArn (string) --

    [REQUIRED]

    The ARN of the IAM role that Amazon SageMaker can assume to access model artifacts and container images.

  • EndpointName (string) --

    [REQUIRED]

    The name of the Amazon SageMaker endpoint on which you want to run the inference experiment.

  • ModelVariants (list) --

    [REQUIRED]

    Array of ModelVariantConfigSummary objects. There is one for each variant in the inference experiment. Each ModelVariantConfigSummary object in the array describes the infrastructure configuration for the corresponding variant.

    • (dict) --

      Contains information about the deployment options of a model.

      • ModelName (string) -- [REQUIRED]

        The name of the model.

      • VariantName (string) -- [REQUIRED]

        The name of the variant.

      • InfrastructureConfig (dict) -- [REQUIRED]

        The configuration for the infrastructure that the model will be deployed to.

        • InfrastructureType (string) -- [REQUIRED]

          The inference option to which to deploy your model. Possible values are the following:

          • RealTime : Deploy to real-time inference.
        • RealTimeInferenceConfig (dict) -- [REQUIRED]

          The infrastructure configuration for deploying the model to real-time inference.

          • InstanceType (string) -- [REQUIRED]

            The number of instances of the type specified by InstanceType .

          • InstanceCount (integer) -- [REQUIRED]

            The instance type the model is deployed to.

  • DataStorageConfig (dict) --

    The storage configuration for the inference experiment. This is an optional parameter that you can use for data capture. For more information, see Capture data.

    • Destination (string) -- [REQUIRED]

      The Amazon S3 bucket where the inference experiment data is stored.

    • KmsKey (string) --

      The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that Amazon SageMaker uses to encrypt captured data when uploading to Amazon S3.

    • ContentType (dict) --

      Configuration specifying how to treat different headers. If no headers are specified SageMaker will by default base64 encode when capturing the data.

      • CsvContentTypes (list) --

        The list of all content type headers that SageMaker will treat as CSV and capture accordingly.

        • (string) --
      • JsonContentTypes (list) --

        The list of all content type headers that SageMaker will treat as JSON and capture accordingly.

        • (string) --
  • ShadowModeConfig (dict) --

    [REQUIRED]

    Shows which variant is the production variant and which variant is the shadow variant. For the shadow variant, also shows the sampling percentage.

    • SourceModelVariantName (string) -- [REQUIRED]

      The name of the production variant.

    • ShadowModelVariants (list) -- [REQUIRED]

      List of shadow variant configurations.

      • (dict) --

        The name and sampling percentage of a shadow variant.

        • ShadowModelVariantName (string) -- [REQUIRED]

          The name of the shadow variant.

        • SamplingPercentage (integer) -- [REQUIRED]

          The percentage of inference requests that are replicated to the shadow variant.

  • KmsKey (string) --

    The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint. The KmsKey can be any of the following formats:

    • KMS key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
    • Amazon Resource Name (ARN) of a KMS key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
    • KMS key Alias "alias/ExampleAlias"
    • Amazon Resource Name (ARN) of a KMS key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"

    If you use a KMS key ID or an alias of your KMS key, the Amazon SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. Amazon SageMaker uses server-side encryption with KMS managed keys for OutputDataConfig . If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.

    The KMS key policy must grant permission to the IAM role that you specify in your CreateEndpoint and UpdateEndpoint requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .

  • Tags (list) --

    Array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging your Amazon Web Services Resources.

    • (dict) --

      A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

      You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.

      For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.

      • Key (string) -- [REQUIRED]

        The tag key. Tag keys must be unique per resource.

      • Value (string) -- [REQUIRED]

        The tag value.

Return type

dict

Returns

Response Syntax

{
    'InferenceExperimentArn': 'string'
}

Response Structure

  • (dict) --

    • InferenceExperimentArn (string) --

      The ARN for your inference experiment.

Exceptions

  • SageMaker.Client.exceptions.ResourceInUse
  • SageMaker.Client.exceptions.ResourceLimitExceeded
create_inference_recommendations_job(**kwargs)

Starts a recommendation job. You can create either an instance recommendation or load test job.

See also: AWS API Documentation

Request Syntax

response = client.create_inference_recommendations_job(
    JobName='string',
    JobType='Default'|'Advanced',
    RoleArn='string',
    InputConfig={
        'ModelPackageVersionArn': 'string',
        'JobDurationInSeconds': 123,
        'TrafficPattern': {
            'TrafficType': 'PHASES',
            'Phases': [
                {
                    'InitialNumberOfUsers': 123,
                    'SpawnRate': 123,
                    'DurationInSeconds': 123
                },
            ]
        },
        'ResourceLimit': {
            'MaxNumberOfTests': 123,
            'MaxParallelOfTests': 123
        },
        'EndpointConfigurations': [
            {
                'InstanceType': 'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge',
                'InferenceSpecificationName': 'string',
                'EnvironmentParameterRanges': {
                    'CategoricalParameterRanges': [
                        {
                            'Name': 'string',
                            'Value': [
                                'string',
                            ]
                        },
                    ]
                }
            },
        ],
        'VolumeKmsKeyId': 'string',
        'ContainerConfig': {
            'Domain': 'string',
            'Task': 'string',
            'Framework': 'string',
            'FrameworkVersion': 'string',
            'PayloadConfig': {
                'SamplePayloadUrl': 'string',
                'SupportedContentTypes': [
                    'string',
                ]
            },
            'NearestModelName': 'string',
            'SupportedInstanceTypes': [
                'string',
            ]
        },
        'Endpoints': [
            {
                'EndpointName': 'string'
            },
        ]
    },
    JobDescription='string',
    StoppingConditions={
        'MaxInvocations': 123,
        'ModelLatencyThresholds': [
            {
                'Percentile': 'string',
                'ValueInMilliseconds': 123
            },
        ]
    },
    OutputConfig={
        'KmsKeyId': 'string',
        'CompiledOutputConfig': {
            'S3OutputUri': 'string'
        }
    },
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ]
)
Parameters
  • JobName (string) --

    [REQUIRED]

    A name for the recommendation job. The name must be unique within the Amazon Web Services Region and within your Amazon Web Services account.

  • JobType (string) --

    [REQUIRED]

    Defines the type of recommendation job. Specify Default to initiate an instance recommendation and Advanced to initiate a load test. If left unspecified, Amazon SageMaker Inference Recommender will run an instance recommendation (DEFAULT ) job.

  • RoleArn (string) --

    [REQUIRED]

    The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.

  • InputConfig (dict) --

    [REQUIRED]

    Provides information about the versioned model package Amazon Resource Name (ARN), the traffic pattern, and endpoint configurations.

    • ModelPackageVersionArn (string) -- [REQUIRED]

      The Amazon Resource Name (ARN) of a versioned model package.

    • JobDurationInSeconds (integer) --

      Specifies the maximum duration of the job, in seconds.>

    • TrafficPattern (dict) --

      Specifies the traffic pattern of the job.

      • TrafficType (string) --

        Defines the traffic patterns.

      • Phases (list) --

        Defines the phases traffic specification.

        • (dict) --

          Defines the traffic pattern.

          • InitialNumberOfUsers (integer) --

            Specifies how many concurrent users to start with.

          • SpawnRate (integer) --

            Specified how many new users to spawn in a minute.

          • DurationInSeconds (integer) --

            Specifies how long traffic phase should be.

    • ResourceLimit (dict) --

      Defines the resource limit of the job.

      • MaxNumberOfTests (integer) --

        Defines the maximum number of load tests.

      • MaxParallelOfTests (integer) --

        Defines the maximum number of parallel load tests.

    • EndpointConfigurations (list) --

      Specifies the endpoint configuration to use for a job.

      • (dict) --

        The endpoint configuration for the load test.

        • InstanceType (string) -- [REQUIRED]

          The instance types to use for the load test.

        • InferenceSpecificationName (string) --

          The inference specification name in the model package version.

        • EnvironmentParameterRanges (dict) --

          The parameter you want to benchmark against.

          • CategoricalParameterRanges (list) --

            Specified a list of parameters for each category.

            • (dict) --

              Environment parameters you want to benchmark your load test against.

              • Name (string) -- [REQUIRED]

                The Name of the environment variable.

              • Value (list) -- [REQUIRED]

                The list of values you can pass.

                • (string) --
    • VolumeKmsKeyId (string) --

      The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint. This key will be passed to SageMaker Hosting for endpoint creation.

      The SageMaker execution role must have kms:CreateGrant permission in order to encrypt data on the storage volume of the endpoints created for inference recommendation. The inference recommendation job will fail asynchronously during endpoint configuration creation if the role passed does not have kms:CreateGrant permission.

      The KmsKeyId can be any of the following formats:

      • // KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
      • // Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:<region>:<account>:key/<key-id-12ab-34cd-56ef-1234567890ab>"
      • // KMS Key Alias "alias/ExampleAlias"
      • // Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:<region>:<account>:alias/<ExampleAlias>"

      For more information about key identifiers, see Key identifiers (KeyID) in the Amazon Web Services Key Management Service (Amazon Web Services KMS) documentation.

    • ContainerConfig (dict) --

      Specifies mandatory fields for running an Inference Recommender job. The fields specified in ContainerConfig override the corresponding fields in the model package.

      • Domain (string) --

        The machine learning domain of the model and its components.

        Valid Values: COMPUTER_VISION | NATURAL_LANGUAGE_PROCESSING | MACHINE_LEARNING

      • Task (string) --

        The machine learning task that the model accomplishes.

        Valid Values: IMAGE_CLASSIFICATION | OBJECT_DETECTION | TEXT_GENERATION | IMAGE_SEGMENTATION | FILL_MASK | CLASSIFICATION | REGRESSION | OTHER

      • Framework (string) --

        The machine learning framework of the container image.

        Valid Values: TENSORFLOW | PYTORCH | XGBOOST | SAGEMAKER-SCIKIT-LEARN

      • FrameworkVersion (string) --

        The framework version of the container image.

      • PayloadConfig (dict) --

        Specifies the SamplePayloadUrl and all other sample payload-related fields.

        • SamplePayloadUrl (string) --

          The Amazon Simple Storage Service (Amazon S3) path where the sample payload is stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).

        • SupportedContentTypes (list) --

          The supported MIME types for the input data.

          • (string) --
      • NearestModelName (string) --

        The name of a pre-trained machine learning model benchmarked by Amazon SageMaker Inference Recommender that matches your model.

        Valid Values: efficientnetb7 | unet | xgboost | faster-rcnn-resnet101 | nasnetlarge | vgg16 | inception-v3 | mask-rcnn | sagemaker-scikit-learn | densenet201-gluon | resnet18v2-gluon | xception | densenet201 | yolov4 | resnet152 | bert-base-cased | xceptionV1-keras | resnet50 | retinanet

      • SupportedInstanceTypes (list) --

        A list of the instance types that are used to generate inferences in real-time.

        • (string) --
    • Endpoints (list) --

      Existing customer endpoints on which to run an Inference Recommender job.

      • (dict) --

        Details about a customer endpoint that was compared in an Inference Recommender job.

        • EndpointName (string) -- [REQUIRED]

          The name of a customer's endpoint.

  • JobDescription (string) -- Description of the recommendation job.
  • StoppingConditions (dict) --

    A set of conditions for stopping a recommendation job. If any of the conditions are met, the job is automatically stopped.

    • MaxInvocations (integer) --

      The maximum number of requests per minute expected for the endpoint.

    • ModelLatencyThresholds (list) --

      The interval of time taken by a model to respond as viewed from SageMaker. The interval includes the local communication time taken to send the request and to fetch the response from the container of a model and the time taken to complete the inference in the container.

      • (dict) --

        The model latency threshold.

        • Percentile (string) --

          The model latency percentile threshold.

        • ValueInMilliseconds (integer) --

          The model latency percentile value in milliseconds.

  • OutputConfig (dict) --

    Provides information about the output artifacts and the KMS key to use for Amazon S3 server-side encryption.

    • KmsKeyId (string) --

      The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt your output artifacts with Amazon S3 server-side encryption. The SageMaker execution role must have kms:GenerateDataKey permission.

      The KmsKeyId can be any of the following formats:

      • // KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
      • // Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:<region>:<account>:key/<key-id-12ab-34cd-56ef-1234567890ab>"
      • // KMS Key Alias "alias/ExampleAlias"
      • // Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:<region>:<account>:alias/<ExampleAlias>"

      For more information about key identifiers, see Key identifiers (KeyID) in the Amazon Web Services Key Management Service (Amazon Web Services KMS) documentation.

    • CompiledOutputConfig (dict) --

      Provides information about the output configuration for the compiled model.

      • S3OutputUri (string) --

        Identifies the Amazon S3 bucket where you want SageMaker to store the compiled model artifacts.

  • Tags (list) --

    The metadata that you apply to Amazon Web Services resources to help you categorize and organize them. Each tag consists of a key and a value, both of which you define. For more information, see Tagging Amazon Web Services Resources in the Amazon Web Services General Reference.

    • (dict) --

      A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

      You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.

      For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.

      • Key (string) -- [REQUIRED]

        The tag key. Tag keys must be unique per resource.

      • Value (string) -- [REQUIRED]

        The tag value.

Return type

dict

Returns

Response Syntax

{
    'JobArn': 'string'
}

Response Structure

  • (dict) --

    • JobArn (string) --

      The Amazon Resource Name (ARN) of the recommendation job.

Exceptions

  • SageMaker.Client.exceptions.ResourceInUse
  • SageMaker.Client.exceptions.ResourceLimitExceeded
create_labeling_job(**kwargs)

Creates a job that uses workers to label the data objects in your input dataset. You can use the labeled data to train machine learning models.

You can select your workforce from one of three providers:

  • A private workforce that you create. It can include employees, contractors, and outside experts. Use a private workforce when want the data to stay within your organization or when a specific set of skills is required.
  • One or more vendors that you select from the Amazon Web Services Marketplace. Vendors provide expertise in specific areas.
  • The Amazon Mechanical Turk workforce. This is the largest workforce, but it should only be used for public data or data that has been stripped of any personally identifiable information.

You can also use automated data labeling to reduce the number of data objects that need to be labeled by a human. Automated data labeling uses active learning to determine if a data object can be labeled by machine or if it needs to be sent to a human worker. For more information, see Using Automated Data Labeling.

The data objects to be labeled are contained in an Amazon S3 bucket. You create a manifest file that describes the location of each object. For more information, see Using Input and Output Data.

The output can be used as the manifest file for another labeling job or as training data for your machine learning models.

You can use this operation to create a static labeling job or a streaming labeling job. A static labeling job stops if all data objects in the input manifest file identified in ManifestS3Uri have been labeled. A streaming labeling job runs perpetually until it is manually stopped, or remains idle for 10 days. You can send new data objects to an active (InProgress ) streaming labeling job in real time. To learn how to create a static labeling job, see Create a Labeling Job (API) in the Amazon SageMaker Developer Guide. To learn how to create a streaming labeling job, see Create a Streaming Labeling Job.

See also: AWS API Documentation

Request Syntax

response = client.create_labeling_job(
    LabelingJobName='string',
    LabelAttributeName='string',
    InputConfig={
        'DataSource': {
            'S3DataSource': {
                'ManifestS3Uri': 'string'
            },
            'SnsDataSource': {
                'SnsTopicArn': 'string'
            }
        },
        'DataAttributes': {
            'ContentClassifiers': [
                'FreeOfPersonallyIdentifiableInformation'|'FreeOfAdultContent',
            ]
        }
    },
    OutputConfig={
        'S3OutputPath': 'string',
        'KmsKeyId': 'string',
        'SnsTopicArn': 'string'
    },
    RoleArn='string',
    LabelCategoryConfigS3Uri='string',
    StoppingConditions={
        'MaxHumanLabeledObjectCount': 123,
        'MaxPercentageOfInputDatasetLabeled': 123
    },
    LabelingJobAlgorithmsConfig={
        'LabelingJobAlgorithmSpecificationArn': 'string',
        'InitialActiveLearningModelArn': 'string',
        'LabelingJobResourceConfig': {
            'VolumeKmsKeyId': 'string',
            'VpcConfig': {
                'SecurityGroupIds': [
                    'string',
                ],
                'Subnets': [
                    'string',
                ]
            }
        }
    },
    HumanTaskConfig={
        'WorkteamArn': 'string',
        'UiConfig': {
            'UiTemplateS3Uri': 'string',
            'HumanTaskUiArn': 'string'
        },
        'PreHumanTaskLambdaArn': 'string',
        'TaskKeywords': [
            'string',
        ],
        'TaskTitle': 'string',
        'TaskDescription': 'string',
        'NumberOfHumanWorkersPerDataObject': 123,
        'TaskTimeLimitInSeconds': 123,
        'TaskAvailabilityLifetimeInSeconds': 123,
        'MaxConcurrentTaskCount': 123,
        'AnnotationConsolidationConfig': {
            'AnnotationConsolidationLambdaArn': 'string'
        },
        'PublicWorkforceTaskPrice': {
            'AmountInUsd': {
                'Dollars': 123,
                'Cents': 123,
                'TenthFractionsOfACent': 123
            }
        }
    },
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ]
)
Parameters
  • LabelingJobName (string) --

    [REQUIRED]

    The name of the labeling job. This name is used to identify the job in a list of labeling jobs. Labeling job names must be unique within an Amazon Web Services account and region. LabelingJobName is not case sensitive. For example, Example-job and example-job are considered the same labeling job name by Ground Truth.

  • LabelAttributeName (string) --

    [REQUIRED]

    The attribute name to use for the label in the output manifest file. This is the key for the key/value pair formed with the label that a worker assigns to the object. The LabelAttributeName must meet the following requirements.

    • The name can't end with "-metadata".
    • If you are using one of the following built-in task types, the attribute name must end with "-ref". If the task type you are using is not listed below, the attribute name must not end with "-ref".
      • Image semantic segmentation (SemanticSegmentation) , and adjustment (AdjustmentSemanticSegmentation ) and verification (VerificationSemanticSegmentation ) labeling jobs for this task type.
      • Video frame object detection (VideoObjectDetection ), and adjustment and verification (AdjustmentVideoObjectDetection ) labeling jobs for this task type.
      • Video frame object tracking (VideoObjectTracking ), and adjustment and verification (AdjustmentVideoObjectTracking ) labeling jobs for this task type.
      • 3D point cloud semantic segmentation (3DPointCloudSemanticSegmentation ), and adjustment and verification (Adjustment3DPointCloudSemanticSegmentation ) labeling jobs for this task type.
      • 3D point cloud object tracking (3DPointCloudObjectTracking ), and adjustment and verification (Adjustment3DPointCloudObjectTracking ) labeling jobs for this task type.

    Warning

    If you are creating an adjustment or verification labeling job, you must use a different LabelAttributeName than the one used in the original labeling job. The original labeling job is the Ground Truth labeling job that produced the labels that you want verified or adjusted. To learn more about adjustment and verification labeling jobs, see Verify and Adjust Labels.

  • InputConfig (dict) --

    [REQUIRED]

    Input data for the labeling job, such as the Amazon S3 location of the data objects and the location of the manifest file that describes the data objects.

    You must specify at least one of the following: S3DataSource or SnsDataSource .

    • Use SnsDataSource to specify an SNS input topic for a streaming labeling job. If you do not specify and SNS input topic ARN, Ground Truth will create a one-time labeling job that stops after all data objects in the input manifest file have been labeled.
    • Use S3DataSource to specify an input manifest file for both streaming and one-time labeling jobs. Adding an S3DataSource is optional if you use SnsDataSource to create a streaming labeling job.

    If you use the Amazon Mechanical Turk workforce, your input data should not include confidential information, personal information or protected health information. Use ContentClassifiers to specify that your data is free of personally identifiable information and adult content.

    • DataSource (dict) -- [REQUIRED]

      The location of the input data.

      • S3DataSource (dict) --

        The Amazon S3 location of the input data objects.

        • ManifestS3Uri (string) -- [REQUIRED]

          The Amazon S3 location of the manifest file that describes the input data objects.

          The input manifest file referenced in ManifestS3Uri must contain one of the following keys: source-ref or source . The value of the keys are interpreted as follows:

          • source-ref : The source of the object is the Amazon S3 object specified in the value. Use this value when the object is a binary object, such as an image.
          • source : The source of the object is the value. Use this value when the object is a text value.

          If you are a new user of Ground Truth, it is recommended you review Use an Input Manifest File in the Amazon SageMaker Developer Guide to learn how to create an input manifest file.

      • SnsDataSource (dict) --

        An Amazon SNS data source used for streaming labeling jobs. To learn more, see Send Data to a Streaming Labeling Job.

        • SnsTopicArn (string) -- [REQUIRED]

          The Amazon SNS input topic Amazon Resource Name (ARN). Specify the ARN of the input topic you will use to send new data objects to a streaming labeling job.

    • DataAttributes (dict) --

      Attributes of the data specified by the customer.

      • ContentClassifiers (list) --

        Declares that your content is free of personally identifiable information or adult content. SageMaker may restrict the Amazon Mechanical Turk workers that can view your task based on this information.

        • (string) --
  • OutputConfig (dict) --

    [REQUIRED]

    The location of the output data and the Amazon Web Services Key Management Service key ID for the key used to encrypt the output data, if any.

    • S3OutputPath (string) -- [REQUIRED]

      The Amazon S3 location to write output data.

    • KmsKeyId (string) --

      The Amazon Web Services Key Management Service ID of the key used to encrypt the output data, if any.

      If you provide your own KMS key ID, you must add the required permissions to your KMS key described in Encrypt Output Data and Storage Volume with Amazon Web Services KMS.

      If you don't provide a KMS key ID, Amazon SageMaker uses the default Amazon Web Services KMS key for Amazon S3 for your role's account to encrypt your output data.

      If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.

    • SnsTopicArn (string) --

      An Amazon Simple Notification Service (Amazon SNS) output topic ARN. Provide a SnsTopicArn if you want to do real time chaining to another streaming job and receive an Amazon SNS notifications each time a data object is submitted by a worker.

      If you provide an SnsTopicArn in OutputConfig , when workers complete labeling tasks, Ground Truth will send labeling task output data to the SNS output topic you specify here.

      To learn more, see Receive Output Data from a Streaming Labeling Job.

  • RoleArn (string) --

    [REQUIRED]

    The Amazon Resource Number (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during data labeling. You must grant this role the necessary permissions so that Amazon SageMaker can successfully complete data labeling.

  • LabelCategoryConfigS3Uri (string) --

    The S3 URI of the file, referred to as a label category configuration file , that defines the categories used to label the data objects.

    For 3D point cloud and video frame task types, you can add label category attributes and frame attributes to your label category configuration file. To learn how, see Create a Labeling Category Configuration File for 3D Point Cloud Labeling Jobs.

    For named entity recognition jobs, in addition to "labels" , you must provide worker instructions in the label category configuration file using the "instructions" parameter: "instructions": {"shortInstruction":"<h1>Add header</h1><p>Add Instructions</p>", "fullInstruction":"<p>Add additional instructions.</p>"} . For details and an example, see Create a Named Entity Recognition Labeling Job (API).

    For all other built-in task types and custom tasks, your label category configuration file must be a JSON file in the following format. Identify the labels you want to use by replacing label_1 , label_2 ,``...`` ,``label_n`` with your label categories.

    {

    "document-version": "2018-11-28",

    "labels": [{"label": "label_1"},{"label": "label_2"},...{"label": "label_n"}]

    }

    Note the following about the label category configuration file:

    • For image classification and text classification (single and multi-label) you must specify at least two label categories. For all other task types, the minimum number of label categories required is one.
    • Each label category must be unique, you cannot specify duplicate label categories.
    • If you create a 3D point cloud or video frame adjustment or verification labeling job, you must include auditLabelAttributeName in the label category configuration. Use this parameter to enter the ` LabelAttributeName https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateLabelingJob.html#sagemaker-CreateLabelingJob-request-LabelAttributeName`__ of the labeling job you want to adjust or verify annotations of.
  • StoppingConditions (dict) --

    A set of conditions for stopping the labeling job. If any of the conditions are met, the job is automatically stopped. You can use these conditions to control the cost of data labeling.

    • MaxHumanLabeledObjectCount (integer) --

      The maximum number of objects that can be labeled by human workers.

    • MaxPercentageOfInputDatasetLabeled (integer) --

      The maximum number of input data objects that should be labeled.

  • LabelingJobAlgorithmsConfig (dict) --

    Configures the information required to perform automated data labeling.

    • LabelingJobAlgorithmSpecificationArn (string) -- [REQUIRED]

      Specifies the Amazon Resource Name (ARN) of the algorithm used for auto-labeling. You must select one of the following ARNs:

      • Image classification arn:aws:sagemaker:*region* :027400017018:labeling-job-algorithm-specification/image-classification
      • Text classification arn:aws:sagemaker:*region* :027400017018:labeling-job-algorithm-specification/text-classification
      • Object detection arn:aws:sagemaker:*region* :027400017018:labeling-job-algorithm-specification/object-detection
      • Semantic Segmentation arn:aws:sagemaker:*region* :027400017018:labeling-job-algorithm-specification/semantic-segmentation
    • InitialActiveLearningModelArn (string) --

      At the end of an auto-label job Ground Truth sends the Amazon Resource Name (ARN) of the final model used for auto-labeling. You can use this model as the starting point for subsequent similar jobs by providing the ARN of the model here.

    • LabelingJobResourceConfig (dict) --

      Provides configuration information for a labeling job.

      • VolumeKmsKeyId (string) --

        The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training and inference jobs used for automated data labeling.

        You can only specify a VolumeKmsKeyId when you create a labeling job with automated data labeling enabled using the API operation CreateLabelingJob . You cannot specify an Amazon Web Services KMS key to encrypt the storage volume used for automated data labeling model training and inference when you create a labeling job using the console. To learn more, see Output Data and Storage Volume Encryption.

        The VolumeKmsKeyId can be any of the following formats:

        • KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
        • Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
      • VpcConfig (dict) --

        Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Training Jobs by Using an Amazon Virtual Private Cloud.

        • SecurityGroupIds (list) -- [REQUIRED]

          The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.

          • (string) --
        • Subnets (list) -- [REQUIRED]

          The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.

          • (string) --
  • HumanTaskConfig (dict) --

    [REQUIRED]

    Configures the labeling task and how it is presented to workers; including, but not limited to price, keywords, and batch size (task count).

    • WorkteamArn (string) -- [REQUIRED]

      The Amazon Resource Name (ARN) of the work team assigned to complete the tasks.

    • UiConfig (dict) -- [REQUIRED]

      Information about the user interface that workers use to complete the labeling task.

      • UiTemplateS3Uri (string) --

        The Amazon S3 bucket location of the UI template, or worker task template. This is the template used to render the worker UI and tools for labeling job tasks. For more information about the contents of a UI template, see Creating Your Custom Labeling Task Template.

      • HumanTaskUiArn (string) --

        The ARN of the worker task template used to render the worker UI and tools for labeling job tasks.

        Use this parameter when you are creating a labeling job for named entity recognition, 3D point cloud and video frame labeling jobs. Use your labeling job task type to select one of the following ARNs and use it with this parameter when you create a labeling job. Replace aws-region with the Amazon Web Services Region you are creating your labeling job in. For example, replace aws-region with us-west-1 if you create a labeling job in US West (N. California).

        Named Entity Recognition

        Use the following HumanTaskUiArn for named entity recognition labeling jobs:

        arn:aws:sagemaker:aws-region:394669845002:human-task-ui/NamedEntityRecognition

        3D Point Cloud HumanTaskUiArns

        Use this HumanTaskUiArn for 3D point cloud object detection and 3D point cloud object detection adjustment labeling jobs.

        • arn:aws:sagemaker:aws-region:394669845002:human-task-ui/PointCloudObjectDetection

        Use this HumanTaskUiArn for 3D point cloud object tracking and 3D point cloud object tracking adjustment labeling jobs.

        • arn:aws:sagemaker:aws-region:394669845002:human-task-ui/PointCloudObjectTracking

        Use this HumanTaskUiArn for 3D point cloud semantic segmentation and 3D point cloud semantic segmentation adjustment labeling jobs.

        • arn:aws:sagemaker:aws-region:394669845002:human-task-ui/PointCloudSemanticSegmentation
        Video Frame HumanTaskUiArns

        Use this HumanTaskUiArn for video frame object detection and video frame object detection adjustment labeling jobs.

        • arn:aws:sagemaker:region:394669845002:human-task-ui/VideoObjectDetection

        Use this HumanTaskUiArn for video frame object tracking and video frame object tracking adjustment labeling jobs.

        • arn:aws:sagemaker:aws-region:394669845002:human-task-ui/VideoObjectTracking
    • PreHumanTaskLambdaArn (string) -- [REQUIRED]

      The Amazon Resource Name (ARN) of a Lambda function that is run before a data object is sent to a human worker. Use this function to provide input to a custom labeling job.

      For built-in task types, use one of the following Amazon SageMaker Ground Truth Lambda function ARNs for PreHumanTaskLambdaArn . For custom labeling workflows, see Pre-annotation Lambda.

      Bounding box - Finds the most similar boxes from different workers based on the Jaccard index of the boxes.
      • arn:aws:lambda:us-east-1:432418664414:function:PRE-BoundingBox
      • arn:aws:lambda:us-east-2:266458841044:function:PRE-BoundingBox
      • arn:aws:lambda:us-west-2:081040173940:function:PRE-BoundingBox
      • arn:aws:lambda:ca-central-1:918755190332:function:PRE-BoundingBox
      • arn:aws:lambda:eu-west-1:568282634449:function:PRE-BoundingBox
      • arn:aws:lambda:eu-west-2:487402164563:function:PRE-BoundingBox
      • arn:aws:lambda:eu-central-1:203001061592:function:PRE-BoundingBox
      • arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-BoundingBox
      • arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-BoundingBox
      • arn:aws:lambda:ap-south-1:565803892007:function:PRE-BoundingBox
      • arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-BoundingBox
      • arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-BoundingBox
      Image classification - Uses a variant of the Expectation Maximization approach to estimate the true class of an image based on annotations from individual workers.
      • arn:aws:lambda:us-east-1:432418664414:function:PRE-ImageMultiClass
      • arn:aws:lambda:us-east-2:266458841044:function:PRE-ImageMultiClass
      • arn:aws:lambda:us-west-2:081040173940:function:PRE-ImageMultiClass
      • arn:aws:lambda:ca-central-1:918755190332:function:PRE-ImageMultiClass
      • arn:aws:lambda:eu-west-1:568282634449:function:PRE-ImageMultiClass
      • arn:aws:lambda:eu-west-2:487402164563:function:PRE-ImageMultiClass
      • arn:aws:lambda:eu-central-1:203001061592:function:PRE-ImageMultiClass
      • arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-ImageMultiClass
      • arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-ImageMultiClass
      • arn:aws:lambda:ap-south-1:565803892007:function:PRE-ImageMultiClass
      • arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-ImageMultiClass
      • arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-ImageMultiClass
      Multi-label image classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of an image based on annotations from individual workers.
      • arn:aws:lambda:us-east-1:432418664414:function:PRE-ImageMultiClassMultiLabel
      • arn:aws:lambda:us-east-2:266458841044:function:PRE-ImageMultiClassMultiLabel
      • arn:aws:lambda:us-west-2:081040173940:function:PRE-ImageMultiClassMultiLabel
      • arn:aws:lambda:ca-central-1:918755190332:function:PRE-ImageMultiClassMultiLabel
      • arn:aws:lambda:eu-west-1:568282634449:function:PRE-ImageMultiClassMultiLabel
      • arn:aws:lambda:eu-west-2:487402164563:function:PRE-ImageMultiClassMultiLabel
      • arn:aws:lambda:eu-central-1:203001061592:function:PRE-ImageMultiClassMultiLabel
      • arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-ImageMultiClassMultiLabel
      • arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-ImageMultiClassMultiLabel
      • arn:aws:lambda:ap-south-1:565803892007:function:PRE-ImageMultiClassMultiLabel
      • arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-ImageMultiClassMultiLabel
      • arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-ImageMultiClassMultiLabel
      Semantic segmentation - Treats each pixel in an image as a multi-class classification and treats pixel annotations from workers as "votes" for the correct label.
      • arn:aws:lambda:us-east-1:432418664414:function:PRE-SemanticSegmentation
      • arn:aws:lambda:us-east-2:266458841044:function:PRE-SemanticSegmentation
      • arn:aws:lambda:us-west-2:081040173940:function:PRE-SemanticSegmentation
      • arn:aws:lambda:ca-central-1:918755190332:function:PRE-SemanticSegmentation
      • arn:aws:lambda:eu-west-1:568282634449:function:PRE-SemanticSegmentation
      • arn:aws:lambda:eu-west-2:487402164563:function:PRE-SemanticSegmentation
      • arn:aws:lambda:eu-central-1:203001061592:function:PRE-SemanticSegmentation
      • arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-SemanticSegmentation
      • arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-SemanticSegmentation
      • arn:aws:lambda:ap-south-1:565803892007:function:PRE-SemanticSegmentation
      • arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-SemanticSegmentation
      • arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-SemanticSegmentation
      Text classification - Uses a variant of the Expectation Maximization approach to estimate the true class of text based on annotations from individual workers.
      • arn:aws:lambda:us-east-1:432418664414:function:PRE-TextMultiClass
      • arn:aws:lambda:us-east-2:266458841044:function:PRE-TextMultiClass
      • arn:aws:lambda:us-west-2:081040173940:function:PRE-TextMultiClass
      • arn:aws:lambda:ca-central-1:918755190332:function:PRE-TextMultiClass
      • arn:aws:lambda:eu-west-1:568282634449:function:PRE-TextMultiClass
      • arn:aws:lambda:eu-west-2:487402164563:function:PRE-TextMultiClass
      • arn:aws:lambda:eu-central-1:203001061592:function:PRE-TextMultiClass
      • arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-TextMultiClass
      • arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-TextMultiClass
      • arn:aws:lambda:ap-south-1:565803892007:function:PRE-TextMultiClass
      • arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-TextMultiClass
      • arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-TextMultiClass
      Multi-label text classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of text based on annotations from individual workers.
      • arn:aws:lambda:us-east-1:432418664414:function:PRE-TextMultiClassMultiLabel
      • arn:aws:lambda:us-east-2:266458841044:function:PRE-TextMultiClassMultiLabel
      • arn:aws:lambda:us-west-2:081040173940:function:PRE-TextMultiClassMultiLabel
      • arn:aws:lambda:ca-central-1:918755190332:function:PRE-TextMultiClassMultiLabel
      • arn:aws:lambda:eu-west-1:568282634449:function:PRE-TextMultiClassMultiLabel
      • arn:aws:lambda:eu-west-2:487402164563:function:PRE-TextMultiClassMultiLabel
      • arn:aws:lambda:eu-central-1:203001061592:function:PRE-TextMultiClassMultiLabel
      • arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-TextMultiClassMultiLabel
      • arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-TextMultiClassMultiLabel
      • arn:aws:lambda:ap-south-1:565803892007:function:PRE-TextMultiClassMultiLabel
      • arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-TextMultiClassMultiLabel
      • arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-TextMultiClassMultiLabel
      Named entity recognition - Groups similar selections and calculates aggregate boundaries, resolving to most-assigned label.
      • arn:aws:lambda:us-east-1:432418664414:function:PRE-NamedEntityRecognition
      • arn:aws:lambda:us-east-2:266458841044:function:PRE-NamedEntityRecognition
      • arn:aws:lambda:us-west-2:081040173940:function:PRE-NamedEntityRecognition
      • arn:aws:lambda:ca-central-1:918755190332:function:PRE-NamedEntityRecognition
      • arn:aws:lambda:eu-west-1:568282634449:function:PRE-NamedEntityRecognition
      • arn:aws:lambda:eu-west-2:487402164563:function:PRE-NamedEntityRecognition
      • arn:aws:lambda:eu-central-1:203001061592:function:PRE-NamedEntityRecognition
      • arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-NamedEntityRecognition
      • arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-NamedEntityRecognition
      • arn:aws:lambda:ap-south-1:565803892007:function:PRE-NamedEntityRecognition
      • arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-NamedEntityRecognition
      • arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-NamedEntityRecognition
      Video Classification - Use this task type when you need workers to classify videos using predefined labels that you specify. Workers are shown videos and are asked to choose one label for each video.
      • arn:aws:lambda:us-east-1:432418664414:function:PRE-VideoMultiClass
      • arn:aws:lambda:us-east-2:266458841044:function:PRE-VideoMultiClass
      • arn:aws:lambda:us-west-2:081040173940:function:PRE-VideoMultiClass
      • arn:aws:lambda:eu-west-1:568282634449:function:PRE-VideoMultiClass
      • arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VideoMultiClass
      • arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VideoMultiClass
      • arn:aws:lambda:ap-south-1:565803892007:function:PRE-VideoMultiClass
      • arn:aws:lambda:eu-central-1:203001061592:function:PRE-VideoMultiClass
      • arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VideoMultiClass
      • arn:aws:lambda:eu-west-2:487402164563:function:PRE-VideoMultiClass
      • arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VideoMultiClass
      • arn:aws:lambda:ca-central-1:918755190332:function:PRE-VideoMultiClass
      Video Frame Object Detection - Use this task type to have workers identify and locate objects in a sequence of video frames (images extracted from a video) using bounding boxes. For example, you can use this task to ask workers to identify and localize various objects in a series of video frames, such as cars, bikes, and pedestrians.
      • arn:aws:lambda:us-east-1:432418664414:function:PRE-VideoObjectDetection
      • arn:aws:lambda:us-east-2:266458841044:function:PRE-VideoObjectDetection
      • arn:aws:lambda:us-west-2:081040173940:function:PRE-VideoObjectDetection
      • arn:aws:lambda:eu-west-1:568282634449:function:PRE-VideoObjectDetection
      • arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VideoObjectDetection
      • arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VideoObjectDetection
      • arn:aws:lambda:ap-south-1:565803892007:function:PRE-VideoObjectDetection
      • arn:aws:lambda:eu-central-1:203001061592:function:PRE-VideoObjectDetection
      • arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VideoObjectDetection
      • arn:aws:lambda:eu-west-2:487402164563:function:PRE-VideoObjectDetection
      • arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VideoObjectDetection
      • arn:aws:lambda:ca-central-1:918755190332:function:PRE-VideoObjectDetection
      Video Frame Object Tracking - Use this task type to have workers track the movement of objects in a sequence of video frames (images extracted from a video) using bounding boxes. For example, you can use this task to ask workers to track the movement of objects, such as cars, bikes, and pedestrians.
      • arn:aws:lambda:us-east-1:432418664414:function:PRE-VideoObjectTracking
      • arn:aws:lambda:us-east-2:266458841044:function:PRE-VideoObjectTracking
      • arn:aws:lambda:us-west-2:081040173940:function:PRE-VideoObjectTracking
      • arn:aws:lambda:eu-west-1:568282634449:function:PRE-VideoObjectTracking
      • arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VideoObjectTracking
      • arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VideoObjectTracking
      • arn:aws:lambda:ap-south-1:565803892007:function:PRE-VideoObjectTracking
      • arn:aws:lambda:eu-central-1:203001061592:function:PRE-VideoObjectTracking
      • arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VideoObjectTracking
      • arn:aws:lambda:eu-west-2:487402164563:function:PRE-VideoObjectTracking
      • arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VideoObjectTracking
      • arn:aws:lambda:ca-central-1:918755190332:function:PRE-VideoObjectTracking
      3D Point Cloud Modalities

      Use the following pre-annotation lambdas for 3D point cloud labeling modality tasks. See 3D Point Cloud Task types to learn more.

      3D Point Cloud Object Detection - Use this task type when you want workers to classify objects in a 3D point cloud by drawing 3D cuboids around objects. For example, you can use this task type to ask workers to identify different types of objects in a point cloud, such as cars, bikes, and pedestrians.
      • arn:aws:lambda:us-east-1:432418664414:function:PRE-3DPointCloudObjectDetection
      • arn:aws:lambda:us-east-2:266458841044:function:PRE-3DPointCloudObjectDetection
      • arn:aws:lambda:us-west-2:081040173940:function:PRE-3DPointCloudObjectDetection
      • arn:aws:lambda:eu-west-1:568282634449:function:PRE-3DPointCloudObjectDetection
      • arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-3DPointCloudObjectDetection
      • arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-3DPointCloudObjectDetection
      • arn:aws:lambda:ap-south-1:565803892007:function:PRE-3DPointCloudObjectDetection
      • arn:aws:lambda:eu-central-1:203001061592:function:PRE-3DPointCloudObjectDetection
      • arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-3DPointCloudObjectDetection
      • arn:aws:lambda:eu-west-2:487402164563:function:PRE-3DPointCloudObjectDetection
      • arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-3DPointCloudObjectDetection
      • arn:aws:lambda:ca-central-1:918755190332:function:PRE-3DPointCloudObjectDetection
      3D Point Cloud Object Tracking - Use this task type when you want workers to draw 3D cuboids around objects that appear in a sequence of 3D point cloud frames. For example, you can use this task type to ask workers to track the movement of vehicles across multiple point cloud frames.
      • arn:aws:lambda:us-east-1:432418664414:function:PRE-3DPointCloudObjectTracking
      • arn:aws:lambda:us-east-2:266458841044:function:PRE-3DPointCloudObjectTracking
      • arn:aws:lambda:us-west-2:081040173940:function:PRE-3DPointCloudObjectTracking
      • arn:aws:lambda:eu-west-1:568282634449:function:PRE-3DPointCloudObjectTracking
      • arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-3DPointCloudObjectTracking
      • arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-3DPointCloudObjectTracking
      • arn:aws:lambda:ap-south-1:565803892007:function:PRE-3DPointCloudObjectTracking
      • arn:aws:lambda:eu-central-1:203001061592:function:PRE-3DPointCloudObjectTracking
      • arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-3DPointCloudObjectTracking
      • arn:aws:lambda:eu-west-2:487402164563:function:PRE-3DPointCloudObjectTracking
      • arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-3DPointCloudObjectTracking
      • arn:aws:lambda:ca-central-1:918755190332:function:PRE-3DPointCloudObjectTracking
      3D Point Cloud Semantic Segmentation - Use this task type when you want workers to create a point-level semantic segmentation masks by painting objects in a 3D point cloud using different colors where each color is assigned to one of the classes you specify.
      • arn:aws:lambda:us-east-1:432418664414:function:PRE-3DPointCloudSemanticSegmentation
      • arn:aws:lambda:us-east-2:266458841044:function:PRE-3DPointCloudSemanticSegmentation
      • arn:aws:lambda:us-west-2:081040173940:function:PRE-3DPointCloudSemanticSegmentation
      • arn:aws:lambda:eu-west-1:568282634449:function:PRE-3DPointCloudSemanticSegmentation
      • arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-3DPointCloudSemanticSegmentation
      • arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-3DPointCloudSemanticSegmentation
      • arn:aws:lambda:ap-south-1:565803892007:function:PRE-3DPointCloudSemanticSegmentation
      • arn:aws:lambda:eu-central-1:203001061592:function:PRE-3DPointCloudSemanticSegmentation
      • arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-3DPointCloudSemanticSegmentation
      • arn:aws:lambda:eu-west-2:487402164563:function:PRE-3DPointCloudSemanticSegmentation
      • arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-3DPointCloudSemanticSegmentation
      • arn:aws:lambda:ca-central-1:918755190332:function:PRE-3DPointCloudSemanticSegmentation
      Use the following ARNs for Label Verification and Adjustment Jobs

      Use label verification and adjustment jobs to review and adjust labels. To learn more, see Verify and Adjust Labels.

      Bounding box verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgement for bounding box labels based on annotations from individual workers.
      • arn:aws:lambda:us-east-1:432418664414:function:PRE-VerificationBoundingBox
      • arn:aws:lambda:us-east-2:266458841044:function:PRE-VerificationBoundingBox
      • arn:aws:lambda:us-west-2:081040173940:function:PRE-VerificationBoundingBox
      • arn:aws:lambda:eu-west-1:568282634449:function:PRE-VerificationBoundingBox
      • arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VerificationBoundingBox
      • arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VerificationBoundingBox
      • arn:aws:lambda:ap-south-1:565803892007:function:PRE-VerificationBoundingBox
      • arn:aws:lambda:eu-central-1:203001061592:function:PRE-VerificationBoundingBox
      • arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VerificationBoundingBox
      • arn:aws:lambda:eu-west-2:487402164563:function:PRE-VerificationBoundingBox
      • arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VerificationBoundingBox
      • arn:aws:lambda:ca-central-1:918755190332:function:PRE-VerificationBoundingBox
      Bounding box adjustment - Finds the most similar boxes from different workers based on the Jaccard index of the adjusted annotations.
      • arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentBoundingBox
      • arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentBoundingBox
      • arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentBoundingBox
      • arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentBoundingBox
      • arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentBoundingBox
      • arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentBoundingBox
      • arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentBoundingBox
      • arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentBoundingBox
      • arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentBoundingBox
      • arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentBoundingBox
      • arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentBoundingBox
      • arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentBoundingBox
      Semantic segmentation verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgment for semantic segmentation labels based on annotations from individual workers.
      • arn:aws:lambda:us-east-1:432418664414:function:PRE-VerificationSemanticSegmentation
      • arn:aws:lambda:us-east-2:266458841044:function:PRE-VerificationSemanticSegmentation
      • arn:aws:lambda:us-west-2:081040173940:function:PRE-VerificationSemanticSegmentation
      • arn:aws:lambda:ca-central-1:918755190332:function:PRE-VerificationSemanticSegmentation
      • arn:aws:lambda:eu-west-1:568282634449:function:PRE-VerificationSemanticSegmentation
      • arn:aws:lambda:eu-west-2:487402164563:function:PRE-VerificationSemanticSegmentation
      • arn:aws:lambda:eu-central-1:203001061592:function:PRE-VerificationSemanticSegmentation
      • arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VerificationSemanticSegmentation
      • arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VerificationSemanticSegmentation
      • arn:aws:lambda:ap-south-1:565803892007:function:PRE-VerificationSemanticSegmentation
      • arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VerificationSemanticSegmentation
      • arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VerificationSemanticSegmentation
      Semantic segmentation adjustment - Treats each pixel in an image as a multi-class classification and treats pixel adjusted annotations from workers as "votes" for the correct label.
      • arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentSemanticSegmentation
      • arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentSemanticSegmentation
      • arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentSemanticSegmentation
      • arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentSemanticSegmentation
      • arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentSemanticSegmentation
      • arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentSemanticSegmentation
      • arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentSemanticSegmentation
      • arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentSemanticSegmentation
      • arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentSemanticSegmentation
      • arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentSemanticSegmentation
      • arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentSemanticSegmentation
      • arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentSemanticSegmentation
      Video Frame Object Detection Adjustment - Use this task type when you want workers to adjust bounding boxes that workers have added to video frames to classify and localize objects in a sequence of video frames.
      • arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentVideoObjectDetection
      • arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentVideoObjectDetection
      • arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentVideoObjectDetection
      • arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentVideoObjectDetection
      • arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentVideoObjectDetection
      • arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentVideoObjectDetection
      • arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentVideoObjectDetection
      • arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentVideoObjectDetection
      • arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentVideoObjectDetection
      • arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentVideoObjectDetection
      • arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentVideoObjectDetection
      • arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentVideoObjectDetection
      Video Frame Object Tracking Adjustment - Use this task type when you want workers to adjust bounding boxes that workers have added to video frames to track object movement across a sequence of video frames.
      • arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentVideoObjectTracking
      • arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentVideoObjectTracking
      • arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentVideoObjectTracking
      • arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentVideoObjectTracking
      • arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentVideoObjectTracking
      • arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentVideoObjectTracking
      • arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentVideoObjectTracking
      • arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentVideoObjectTracking
      • arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentVideoObjectTracking
      • arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentVideoObjectTracking
      • arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentVideoObjectTracking
      • arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentVideoObjectTracking
      3D point cloud object detection adjustment - Adjust 3D cuboids in a point cloud frame.
      • arn:aws:lambda:us-east-1:432418664414:function:PRE-Adjustment3DPointCloudObjectDetection
      • arn:aws:lambda:us-east-2:266458841044:function:PRE-Adjustment3DPointCloudObjectDetection
      • arn:aws:lambda:us-west-2:081040173940:function:PRE-Adjustment3DPointCloudObjectDetection
      • arn:aws:lambda:eu-west-1:568282634449:function:PRE-Adjustment3DPointCloudObjectDetection
      • arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-Adjustment3DPointCloudObjectDetection
      • arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-Adjustment3DPointCloudObjectDetection
      • arn:aws:lambda:ap-south-1:565803892007:function:PRE-Adjustment3DPointCloudObjectDetection
      • arn:aws:lambda:eu-central-1:203001061592:function:PRE-Adjustment3DPointCloudObjectDetection
      • arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-Adjustment3DPointCloudObjectDetection
      • arn:aws:lambda:eu-west-2:487402164563:function:PRE-Adjustment3DPointCloudObjectDetection
      • arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-Adjustment3DPointCloudObjectDetection
      • arn:aws:lambda:ca-central-1:918755190332:function:PRE-Adjustment3DPointCloudObjectDetection
      3D point cloud object tracking adjustment - Adjust 3D cuboids across a sequence of point cloud frames.
      • arn:aws:lambda:us-east-1:432418664414:function:PRE-Adjustment3DPointCloudObjectTracking
      • arn:aws:lambda:us-east-2:266458841044:function:PRE-Adjustment3DPointCloudObjectTracking
      • arn:aws:lambda:us-west-2:081040173940:function:PRE-Adjustment3DPointCloudObjectTracking
      • arn:aws:lambda:eu-west-1:568282634449:function:PRE-Adjustment3DPointCloudObjectTracking
      • arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-Adjustment3DPointCloudObjectTracking
      • arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-Adjustment3DPointCloudObjectTracking
      • arn:aws:lambda:ap-south-1:565803892007:function:PRE-Adjustment3DPointCloudObjectTracking
      • arn:aws:lambda:eu-central-1:203001061592:function:PRE-Adjustment3DPointCloudObjectTracking
      • arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-Adjustment3DPointCloudObjectTracking
      • arn:aws:lambda:eu-west-2:487402164563:function:PRE-Adjustment3DPointCloudObjectTracking
      • arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-Adjustment3DPointCloudObjectTracking
      • arn:aws:lambda:ca-central-1:918755190332:function:PRE-Adjustment3DPointCloudObjectTracking
      3D point cloud semantic segmentation adjustment - Adjust semantic segmentation masks in a 3D point cloud.
      • arn:aws:lambda:us-east-1:432418664414:function:PRE-Adjustment3DPointCloudSemanticSegmentation
      • arn:aws:lambda:us-east-2:266458841044:function:PRE-Adjustment3DPointCloudSemanticSegmentation
      • arn:aws:lambda:us-west-2:081040173940:function:PRE-Adjustment3DPointCloudSemanticSegmentation
      • arn:aws:lambda:eu-west-1:568282634449:function:PRE-Adjustment3DPointCloudSemanticSegmentation
      • arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-Adjustment3DPointCloudSemanticSegmentation
      • arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-Adjustment3DPointCloudSemanticSegmentation
      • arn:aws:lambda:ap-south-1:565803892007:function:PRE-Adjustment3DPointCloudSemanticSegmentation
      • arn:aws:lambda:eu-central-1:203001061592:function:PRE-Adjustment3DPointCloudSemanticSegmentation
      • arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-Adjustment3DPointCloudSemanticSegmentation
      • arn:aws:lambda:eu-west-2:487402164563:function:PRE-Adjustment3DPointCloudSemanticSegmentation
      • arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-Adjustment3DPointCloudSemanticSegmentation
      • arn:aws:lambda:ca-central-1:918755190332:function:PRE-Adjustment3DPointCloudSemanticSegmentation
    • TaskKeywords (list) --

      Keywords used to describe the task so that workers on Amazon Mechanical Turk can discover the task.

      • (string) --
    • TaskTitle (string) -- [REQUIRED]

      A title for the task for your human workers.

    • TaskDescription (string) -- [REQUIRED]

      A description of the task for your human workers.

    • NumberOfHumanWorkersPerDataObject (integer) -- [REQUIRED]

      The number of human workers that will label an object.

    • TaskTimeLimitInSeconds (integer) -- [REQUIRED]

      The amount of time that a worker has to complete a task.

      If you create a custom labeling job, the maximum value for this parameter is 8 hours (28,800 seconds).

      If you create a labeling job using a built-in task type the maximum for this parameter depends on the task type you use:

      • For image and text labeling jobs, the maximum is 8 hours (28,800 seconds).
      • For 3D point cloud and video frame labeling jobs, the maximum is 30 days (2952,000 seconds) for non-AL mode. For most users, the maximum is also 30 days.
    • TaskAvailabilityLifetimeInSeconds (integer) --

      The length of time that a task remains available for labeling by human workers. The default and maximum values for this parameter depend on the type of workforce you use.

      • If you choose the Amazon Mechanical Turk workforce, the maximum is 12 hours (43,200 seconds). The default is 6 hours (21,600 seconds).
      • If you choose a private or vendor workforce, the default value is 30 days (2592,000 seconds) for non-AL mode. For most users, the maximum is also 30 days.
    • MaxConcurrentTaskCount (integer) --

      Defines the maximum number of data objects that can be labeled by human workers at the same time. Also referred to as batch size. Each object may have more than one worker at one time. The default value is 1000 objects. To increase the maximum value to 5000 objects, contact Amazon Web Services Support.

    • AnnotationConsolidationConfig (dict) -- [REQUIRED]

      Configures how labels are consolidated across human workers.

      • AnnotationConsolidationLambdaArn (string) -- [REQUIRED]

        The Amazon Resource Name (ARN) of a Lambda function implements the logic for annotation consolidation and to process output data.

        This parameter is required for all labeling jobs. For built-in task types, use one of the following Amazon SageMaker Ground Truth Lambda function ARNs for AnnotationConsolidationLambdaArn . For custom labeling workflows, see Post-annotation Lambda.

        Bounding box - Finds the most similar boxes from different workers based on the Jaccard index of the boxes.
        • arn:aws:lambda:us-east-1:432418664414:function:ACS-BoundingBox
        • arn:aws:lambda:us-east-2:266458841044:function:ACS-BoundingBox
        • arn:aws:lambda:us-west-2:081040173940:function:ACS-BoundingBox
        • arn:aws:lambda:eu-west-1:568282634449:function:ACS-BoundingBox
        • arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-BoundingBox
        • arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-BoundingBox
        • arn:aws:lambda:ap-south-1:565803892007:function:ACS-BoundingBox
        • arn:aws:lambda:eu-central-1:203001061592:function:ACS-BoundingBox
        • arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-BoundingBox
        • arn:aws:lambda:eu-west-2:487402164563:function:ACS-BoundingBox
        • arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-BoundingBox
        • arn:aws:lambda:ca-central-1:918755190332:function:ACS-BoundingBox
        Image classification - Uses a variant of the Expectation Maximization approach to estimate the true class of an image based on annotations from individual workers.
        • arn:aws:lambda:us-east-1:432418664414:function:ACS-ImageMultiClass
        • arn:aws:lambda:us-east-2:266458841044:function:ACS-ImageMultiClass
        • arn:aws:lambda:us-west-2:081040173940:function:ACS-ImageMultiClass
        • arn:aws:lambda:eu-west-1:568282634449:function:ACS-ImageMultiClass
        • arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-ImageMultiClass
        • arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-ImageMultiClass
        • arn:aws:lambda:ap-south-1:565803892007:function:ACS-ImageMultiClass
        • arn:aws:lambda:eu-central-1:203001061592:function:ACS-ImageMultiClass
        • arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-ImageMultiClass
        • arn:aws:lambda:eu-west-2:487402164563:function:ACS-ImageMultiClass
        • arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-ImageMultiClass
        • arn:aws:lambda:ca-central-1:918755190332:function:ACS-ImageMultiClass
        Multi-label image classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of an image based on annotations from individual workers.
        • arn:aws:lambda:us-east-1:432418664414:function:ACS-ImageMultiClassMultiLabel
        • arn:aws:lambda:us-east-2:266458841044:function:ACS-ImageMultiClassMultiLabel
        • arn:aws:lambda:us-west-2:081040173940:function:ACS-ImageMultiClassMultiLabel
        • arn:aws:lambda:eu-west-1:568282634449:function:ACS-ImageMultiClassMultiLabel
        • arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-ImageMultiClassMultiLabel
        • arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-ImageMultiClassMultiLabel
        • arn:aws:lambda:ap-south-1:565803892007:function:ACS-ImageMultiClassMultiLabel
        • arn:aws:lambda:eu-central-1:203001061592:function:ACS-ImageMultiClassMultiLabel
        • arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-ImageMultiClassMultiLabel
        • arn:aws:lambda:eu-west-2:487402164563:function:ACS-ImageMultiClassMultiLabel
        • arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-ImageMultiClassMultiLabel
        • arn:aws:lambda:ca-central-1:918755190332:function:ACS-ImageMultiClassMultiLabel
        Semantic segmentation - Treats each pixel in an image as a multi-class classification and treats pixel annotations from workers as "votes" for the correct label.
        • arn:aws:lambda:us-east-1:432418664414:function:ACS-SemanticSegmentation
        • arn:aws:lambda:us-east-2:266458841044:function:ACS-SemanticSegmentation
        • arn:aws:lambda:us-west-2:081040173940:function:ACS-SemanticSegmentation
        • arn:aws:lambda:eu-west-1:568282634449:function:ACS-SemanticSegmentation
        • arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-SemanticSegmentation
        • arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-SemanticSegmentation
        • arn:aws:lambda:ap-south-1:565803892007:function:ACS-SemanticSegmentation
        • arn:aws:lambda:eu-central-1:203001061592:function:ACS-SemanticSegmentation
        • arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-SemanticSegmentation
        • arn:aws:lambda:eu-west-2:487402164563:function:ACS-SemanticSegmentation
        • arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-SemanticSegmentation
        • arn:aws:lambda:ca-central-1:918755190332:function:ACS-SemanticSegmentation
        Text classification - Uses a variant of the Expectation Maximization approach to estimate the true class of text based on annotations from individual workers.
        • arn:aws:lambda:us-east-1:432418664414:function:ACS-TextMultiClass
        • arn:aws:lambda:us-east-2:266458841044:function:ACS-TextMultiClass
        • arn:aws:lambda:us-west-2:081040173940:function:ACS-TextMultiClass
        • arn:aws:lambda:eu-west-1:568282634449:function:ACS-TextMultiClass
        • arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-TextMultiClass
        • arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-TextMultiClass
        • arn:aws:lambda:ap-south-1:565803892007:function:ACS-TextMultiClass
        • arn:aws:lambda:eu-central-1:203001061592:function:ACS-TextMultiClass
        • arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-TextMultiClass
        • arn:aws:lambda:eu-west-2:487402164563:function:ACS-TextMultiClass
        • arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-TextMultiClass
        • arn:aws:lambda:ca-central-1:918755190332:function:ACS-TextMultiClass
        Multi-label text classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of text based on annotations from individual workers.
        • arn:aws:lambda:us-east-1:432418664414:function:ACS-TextMultiClassMultiLabel
        • arn:aws:lambda:us-east-2:266458841044:function:ACS-TextMultiClassMultiLabel
        • arn:aws:lambda:us-west-2:081040173940:function:ACS-TextMultiClassMultiLabel
        • arn:aws:lambda:eu-west-1:568282634449:function:ACS-TextMultiClassMultiLabel
        • arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-TextMultiClassMultiLabel
        • arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-TextMultiClassMultiLabel
        • arn:aws:lambda:ap-south-1:565803892007:function:ACS-TextMultiClassMultiLabel
        • arn:aws:lambda:eu-central-1:203001061592:function:ACS-TextMultiClassMultiLabel
        • arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-TextMultiClassMultiLabel
        • arn:aws:lambda:eu-west-2:487402164563:function:ACS-TextMultiClassMultiLabel
        • arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-TextMultiClassMultiLabel
        • arn:aws:lambda:ca-central-1:918755190332:function:ACS-TextMultiClassMultiLabel
        Named entity recognition - Groups similar selections and calculates aggregate boundaries, resolving to most-assigned label.
        • arn:aws:lambda:us-east-1:432418664414:function:ACS-NamedEntityRecognition
        • arn:aws:lambda:us-east-2:266458841044:function:ACS-NamedEntityRecognition
        • arn:aws:lambda:us-west-2:081040173940:function:ACS-NamedEntityRecognition
        • arn:aws:lambda:eu-west-1:568282634449:function:ACS-NamedEntityRecognition
        • arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-NamedEntityRecognition
        • arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-NamedEntityRecognition
        • arn:aws:lambda:ap-south-1:565803892007:function:ACS-NamedEntityRecognition
        • arn:aws:lambda:eu-central-1:203001061592:function:ACS-NamedEntityRecognition
        • arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-NamedEntityRecognition
        • arn:aws:lambda:eu-west-2:487402164563:function:ACS-NamedEntityRecognition
        • arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-NamedEntityRecognition
        • arn:aws:lambda:ca-central-1:918755190332:function:ACS-NamedEntityRecognition
        Video Classification - Use this task type when you need workers to classify videos using predefined labels that you specify. Workers are shown videos and are asked to choose one label for each video.
        • arn:aws:lambda:us-east-1:432418664414:function:ACS-VideoMultiClass
        • arn:aws:lambda:us-east-2:266458841044:function:ACS-VideoMultiClass
        • arn:aws:lambda:us-west-2:081040173940:function:ACS-VideoMultiClass
        • arn:aws:lambda:eu-west-1:568282634449:function:ACS-VideoMultiClass
        • arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VideoMultiClass
        • arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VideoMultiClass
        • arn:aws:lambda:ap-south-1:565803892007:function:ACS-VideoMultiClass
        • arn:aws:lambda:eu-central-1:203001061592:function:ACS-VideoMultiClass
        • arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VideoMultiClass
        • arn:aws:lambda:eu-west-2:487402164563:function:ACS-VideoMultiClass
        • arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VideoMultiClass
        • arn:aws:lambda:ca-central-1:918755190332:function:ACS-VideoMultiClass
        Video Frame Object Detection - Use this task type to have workers identify and locate objects in a sequence of video frames (images extracted from a video) using bounding boxes. For example, you can use this task to ask workers to identify and localize various objects in a series of video frames, such as cars, bikes, and pedestrians.
        • arn:aws:lambda:us-east-1:432418664414:function:ACS-VideoObjectDetection
        • arn:aws:lambda:us-east-2:266458841044:function:ACS-VideoObjectDetection
        • arn:aws:lambda:us-west-2:081040173940:function:ACS-VideoObjectDetection
        • arn:aws:lambda:eu-west-1:568282634449:function:ACS-VideoObjectDetection
        • arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VideoObjectDetection
        • arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VideoObjectDetection
        • arn:aws:lambda:ap-south-1:565803892007:function:ACS-VideoObjectDetection
        • arn:aws:lambda:eu-central-1:203001061592:function:ACS-VideoObjectDetection
        • arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VideoObjectDetection
        • arn:aws:lambda:eu-west-2:487402164563:function:ACS-VideoObjectDetection
        • arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VideoObjectDetection
        • arn:aws:lambda:ca-central-1:918755190332:function:ACS-VideoObjectDetection
        Video Frame Object Tracking - Use this task type to have workers track the movement of objects in a sequence of video frames (images extracted from a video) using bounding boxes. For example, you can use this task to ask workers to track the movement of objects, such as cars, bikes, and pedestrians.
        • arn:aws:lambda:us-east-1:432418664414:function:ACS-VideoObjectTracking
        • arn:aws:lambda:us-east-2:266458841044:function:ACS-VideoObjectTracking
        • arn:aws:lambda:us-west-2:081040173940:function:ACS-VideoObjectTracking
        • arn:aws:lambda:eu-west-1:568282634449:function:ACS-VideoObjectTracking
        • arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VideoObjectTracking
        • arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VideoObjectTracking
        • arn:aws:lambda:ap-south-1:565803892007:function:ACS-VideoObjectTracking
        • arn:aws:lambda:eu-central-1:203001061592:function:ACS-VideoObjectTracking
        • arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VideoObjectTracking
        • arn:aws:lambda:eu-west-2:487402164563:function:ACS-VideoObjectTracking
        • arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VideoObjectTracking
        • arn:aws:lambda:ca-central-1:918755190332:function:ACS-VideoObjectTracking
        3D Point Cloud Object Detection - Use this task type when you want workers to classify objects in a 3D point cloud by drawing 3D cuboids around objects. For example, you can use this task type to ask workers to identify different types of objects in a point cloud, such as cars, bikes, and pedestrians.
        • arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudObjectDetection
        • arn:aws:lambda:us-east-2:266458841044:function:ACS-3DPointCloudObjectDetection
        • arn:aws:lambda:us-west-2:081040173940:function:ACS-3DPointCloudObjectDetection
        • arn:aws:lambda:eu-west-1:568282634449:function:ACS-3DPointCloudObjectDetection
        • arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-3DPointCloudObjectDetection
        • arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-3DPointCloudObjectDetection
        • arn:aws:lambda:ap-south-1:565803892007:function:ACS-3DPointCloudObjectDetection
        • arn:aws:lambda:eu-central-1:203001061592:function:ACS-3DPointCloudObjectDetection
        • arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-3DPointCloudObjectDetection
        • arn:aws:lambda:eu-west-2:487402164563:function:ACS-3DPointCloudObjectDetection
        • arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-3DPointCloudObjectDetection
        • arn:aws:lambda:ca-central-1:918755190332:function:ACS-3DPointCloudObjectDetection
        3D Point Cloud Object Tracking - Use this task type when you want workers to draw 3D cuboids around objects that appear in a sequence of 3D point cloud frames. For example, you can use this task type to ask workers to track the movement of vehicles across multiple point cloud frames.
        • arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudObjectTracking
        • arn:aws:lambda:us-east-2:266458841044:function:ACS-3DPointCloudObjectTracking
        • arn:aws:lambda:us-west-2:081040173940:function:ACS-3DPointCloudObjectTracking
        • arn:aws:lambda:eu-west-1:568282634449:function:ACS-3DPointCloudObjectTracking
        • arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-3DPointCloudObjectTracking
        • arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-3DPointCloudObjectTracking
        • arn:aws:lambda:ap-south-1:565803892007:function:ACS-3DPointCloudObjectTracking
        • arn:aws:lambda:eu-central-1:203001061592:function:ACS-3DPointCloudObjectTracking
        • arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-3DPointCloudObjectTracking
        • arn:aws:lambda:eu-west-2:487402164563:function:ACS-3DPointCloudObjectTracking
        • arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-3DPointCloudObjectTracking
        • arn:aws:lambda:ca-central-1:918755190332:function:ACS-3DPointCloudObjectTracking
        3D Point Cloud Semantic Segmentation - Use this task type when you want workers to create a point-level semantic segmentation masks by painting objects in a 3D point cloud using different colors where each color is assigned to one of the classes you specify.
        • arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudSemanticSegmentation
        • arn:aws:lambda:us-east-2:266458841044:function:ACS-3DPointCloudSemanticSegmentation
        • arn:aws:lambda:us-west-2:081040173940:function:ACS-3DPointCloudSemanticSegmentation
        • arn:aws:lambda:eu-west-1:568282634449:function:ACS-3DPointCloudSemanticSegmentation
        • arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-3DPointCloudSemanticSegmentation
        • arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-3DPointCloudSemanticSegmentation
        • arn:aws:lambda:ap-south-1:565803892007:function:ACS-3DPointCloudSemanticSegmentation
        • arn:aws:lambda:eu-central-1:203001061592:function:ACS-3DPointCloudSemanticSegmentation
        • arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-3DPointCloudSemanticSegmentation
        • arn:aws:lambda:eu-west-2:487402164563:function:ACS-3DPointCloudSemanticSegmentation
        • arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-3DPointCloudSemanticSegmentation
        • arn:aws:lambda:ca-central-1:918755190332:function:ACS-3DPointCloudSemanticSegmentation
        Use the following ARNs for Label Verification and Adjustment Jobs

        Use label verification and adjustment jobs to review and adjust labels. To learn more, see Verify and Adjust Labels.

        Semantic Segmentation Adjustment - Treats each pixel in an image as a multi-class classification and treats pixel adjusted annotations from workers as "votes" for the correct label.
        • arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentSemanticSegmentation
        • arn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentSemanticSegmentation
        • arn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentSemanticSegmentation
        • arn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentSemanticSegmentation
        • arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentSemanticSegmentation
        • arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentSemanticSegmentation
        • arn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentSemanticSegmentation
        • arn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentSemanticSegmentation
        • arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentSemanticSegmentation
        • arn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentSemanticSegmentation
        • arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentSemanticSegmentation
        • arn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentSemanticSegmentation
        Semantic Segmentation Verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgment for semantic segmentation labels based on annotations from individual workers.
        • arn:aws:lambda:us-east-1:432418664414:function:ACS-VerificationSemanticSegmentation
        • arn:aws:lambda:us-east-2:266458841044:function:ACS-VerificationSemanticSegmentation
        • arn:aws:lambda:us-west-2:081040173940:function:ACS-VerificationSemanticSegmentation
        • arn:aws:lambda:eu-west-1:568282634449:function:ACS-VerificationSemanticSegmentation
        • arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VerificationSemanticSegmentation
        • arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VerificationSemanticSegmentation
        • arn:aws:lambda:ap-south-1:565803892007:function:ACS-VerificationSemanticSegmentation
        • arn:aws:lambda:eu-central-1:203001061592:function:ACS-VerificationSemanticSegmentation
        • arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VerificationSemanticSegmentation
        • arn:aws:lambda:eu-west-2:487402164563:function:ACS-VerificationSemanticSegmentation
        • arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VerificationSemanticSegmentation
        • arn:aws:lambda:ca-central-1:918755190332:function:ACS-VerificationSemanticSegmentation
        Bounding Box Adjustment - Finds the most similar boxes from different workers based on the Jaccard index of the adjusted annotations.
        • arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentBoundingBox
        • arn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentBoundingBox
        • arn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentBoundingBox
        • arn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentBoundingBox
        • arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentBoundingBox
        • arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentBoundingBox
        • arn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentBoundingBox
        • arn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentBoundingBox
        • arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentBoundingBox
        • arn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentBoundingBox
        • arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentBoundingBox
        • arn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentBoundingBox
        Bounding Box Verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgement for bounding box labels based on annotations from individual workers.
        • arn:aws:lambda:us-east-1:432418664414:function:ACS-VerificationBoundingBox
        • arn:aws:lambda:us-east-2:266458841044:function:ACS-VerificationBoundingBox
        • arn:aws:lambda:us-west-2:081040173940:function:ACS-VerificationBoundingBox