Amazon SageMaker Service

2021/10/27 - Amazon SageMaker Service - 2 new 6 updated api methods

Changes  This release allows customers to describe one or more versioned model packages through BatchDescribeModelPackage, update project via UpdateProject, modify and read customer metadata properties using Create, Update and Describe ModelPackage and enables cross account registration of model packages.

BatchDescribeModelPackage (new) Link ¶

This action batch describes a list of versioned model packages

See also: AWS API Documentation

Request Syntax

client.batch_describe_model_package(
    ModelPackageArnList=[
        'string',
    ]
)
type ModelPackageArnList

list

param ModelPackageArnList

[REQUIRED]

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

  • (string) --

rtype

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'
                        }
                    },
                ],
                'SupportedTransformInstanceTypes': [
                    'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
                ],
                'SupportedRealtimeInferenceInstanceTypes': [
                    'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge',
                ],
                'SupportedContentTypes': [
                    'string',
                ],
                'SupportedResponseMIMETypes': [
                    'string',
                ]
            },
            '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 Amazon SageMaker, the inference code must meet Amazon SageMaker requirements. Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.

                • ImageDigest (string) --

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

                • ModelDataUrl (string) --

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

                  Note

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

                • ProductId (string) --

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

                • Environment (dict) --

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

                  • (string) --

                    • (string) --

            • 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) --

UpdateProject (new) Link ¶

Updates a machine learning (ML) project that is created from a template that sets up an ML pipeline from training to deploying an approved model.

Note

You must not update a project that is in use. If you update the ServiceCatalogProvisioningUpdateDetails of a project that is active or being created, or updated, you may lose resources already created by the project.

See also: AWS API Documentation

Request Syntax

client.update_project(
    ProjectName='string',
    ProjectDescription='string',
    ServiceCatalogProvisioningUpdateDetails={
        'ProvisioningArtifactId': 'string',
        'ProvisioningParameters': [
            {
                'Key': 'string',
                'Value': 'string'
            },
        ]
    },
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ]
)
type ProjectName

string

param ProjectName

[REQUIRED]

The name of the project.

type ProjectDescription

string

param ProjectDescription

The description for the project.

type ServiceCatalogProvisioningUpdateDetails

dict

param ServiceCatalogProvisioningUpdateDetails

The product ID and provisioning artifact ID to provision a service catalog. The provisioning artifact ID will default to the latest provisioning artifact ID of the product, if you don't provide the provisioning artifact ID. For more information, see What is AWS Service Catalog.

  • ProvisioningArtifactId (string) --

    The ID of the provisioning artifact.

  • ProvisioningParameters (list) --

    A list of key value pairs that you specify when you provision a product.

    • (dict) --

      A key value pair used when you provision a project as a service catalog product. For information, see What is Amazon Web Services Service Catalog.

      • Key (string) --

        The key that identifies a provisioning parameter.

      • Value (string) --

        The value of the provisioning parameter.

type Tags

list

param Tags

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

rtype

dict

returns

Response Syntax

{
    'ProjectArn': 'string'
}

Response Structure

  • (dict) --

    • ProjectArn (string) --

      The Amazon Resource Name (ARN) of the project.

CreateModelPackage (updated) Link ¶
Changes (request)
{'CustomerMetadataProperties': {'string': 'string'}}

Creates a model package that you can use to create Amazon SageMaker models or list on Amazon Web Services Marketplace, or a versioned model that is part of a model group. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in Amazon SageMaker.

To create a model package by specifying a Docker container that contains your inference code and the Amazon S3 location of your model artifacts, provide values for InferenceSpecification . To create a model from an algorithm resource that you created or subscribed to in Amazon Web Services Marketplace, provide a value for SourceAlgorithmSpecification .

Note

There are two types of model packages:

  • Versioned - a model that is part of a model group in the model registry.

  • Unversioned - a model package that is not part of a model group.

See also: AWS API Documentation

Request Syntax

client.create_model_package(
    ModelPackageName='string',
    ModelPackageGroupName='string',
    ModelPackageDescription='string',
    InferenceSpecification={
        'Containers': [
            {
                'ContainerHostname': 'string',
                'Image': 'string',
                'ImageDigest': 'string',
                'ModelDataUrl': 'string',
                'ProductId': 'string',
                'Environment': {
                    'string': 'string'
                }
            },
        ],
        'SupportedTransformInstanceTypes': [
            'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
        ],
        'SupportedRealtimeInferenceInstanceTypes': [
            'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge',
        ],
        'SupportedContentTypes': [
            'string',
        ],
        'SupportedResponseMIMETypes': [
            'string',
        ]
    },
    ValidationSpecification={
        'ValidationRole': 'string',
        'ValidationProfiles': [
            {
                'ProfileName': 'string',
                'TransformJobDefinition': {
                    'MaxConcurrentTransforms': 123,
                    'MaxPayloadInMB': 123,
                    'BatchStrategy': 'MultiRecord'|'SingleRecord',
                    'Environment': {
                        'string': 'string'
                    },
                    'TransformInput': {
                        'DataSource': {
                            'S3DataSource': {
                                'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
                                'S3Uri': 'string'
                            }
                        },
                        'ContentType': 'string',
                        'CompressionType': 'None'|'Gzip',
                        'SplitType': 'None'|'Line'|'RecordIO'|'TFRecord'
                    },
                    'TransformOutput': {
                        'S3OutputPath': 'string',
                        'Accept': 'string',
                        'AssembleWith': 'None'|'Line',
                        'KmsKeyId': 'string'
                    },
                    'TransformResources': {
                        'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
                        'InstanceCount': 123,
                        'VolumeKmsKeyId': 'string'
                    }
                }
            },
        ]
    },
    SourceAlgorithmSpecification={
        'SourceAlgorithms': [
            {
                'ModelDataUrl': 'string',
                'AlgorithmName': 'string'
            },
        ]
    },
    CertifyForMarketplace=True|False,
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ],
    ModelApprovalStatus='Approved'|'Rejected'|'PendingManualApproval',
    MetadataProperties={
        'CommitId': 'string',
        'Repository': 'string',
        'GeneratedBy': 'string',
        'ProjectId': 'string'
    },
    ModelMetrics={
        'ModelQuality': {
            'Statistics': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            },
            'Constraints': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            }
        },
        'ModelDataQuality': {
            'Statistics': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            },
            'Constraints': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            }
        },
        'Bias': {
            'Report': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            }
        },
        'Explainability': {
            'Report': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            }
        }
    },
    ClientToken='string',
    CustomerMetadataProperties={
        'string': 'string'
    }
)
type ModelPackageName

string

param ModelPackageName

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

This parameter is required for unversioned models. It is not applicable to versioned models.

type ModelPackageGroupName

string

param ModelPackageGroupName

The name or Amazon Resource Name (ARN) of the model package group that this model version belongs to.

This parameter is required for versioned models, and does not apply to unversioned models.

type ModelPackageDescription

string

param ModelPackageDescription

A description of the model package.

type InferenceSpecification

dict

param InferenceSpecification

Specifies details about inference jobs that can be run with models based on this model package, including the following:

  • The Amazon ECR paths of containers that contain the inference code and model artifacts.

  • The instance types that the model package supports for transform jobs and real-time endpoints used for inference.

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

      • ImageDigest (string) --

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

      • ModelDataUrl (string) --

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

        Note

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

      • ProductId (string) --

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

      • Environment (dict) --

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

        • (string) --

          • (string) --

  • 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) --

type ValidationSpecification

dict

param ValidationSpecification

Specifies configurations for one or more transform jobs that Amazon SageMaker runs to test the model package.

  • ValidationRole (string) -- [REQUIRED]

    The IAM roles to be used for the validation of the model package.

  • ValidationProfiles (list) -- [REQUIRED]

    An array of ModelPackageValidationProfile objects, each of which specifies a batch transform job that Amazon SageMaker runs to validate your model package.

    • (dict) --

      Contains data, such as the inputs and targeted instance types that are used in the process of validating the model package.

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

      • ProfileName (string) -- [REQUIRED]

        The name of the profile for the model package.

      • TransformJobDefinition (dict) -- [REQUIRED]

        The TransformJobDefinition object that describes the transform job used for the validation of the model package.

        • MaxConcurrentTransforms (integer) --

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

        • MaxPayloadInMB (integer) --

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

        • BatchStrategy (string) --

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

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

        • Environment (dict) --

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

          • (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. For distributed transform jobs, specify a value greater than 1. The default value is 1 .

          • VolumeKmsKeyId (string) --

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

            Note

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

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

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

            The VolumeKmsKeyId can be any of the following formats:

            • 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

type SourceAlgorithmSpecification

dict

param SourceAlgorithmSpecification

Details about the algorithm that was used to create the model package.

  • SourceAlgorithms (list) -- [REQUIRED]

    A list of the algorithms that were used to create a model package.

    • (dict) --

      Specifies an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your Amazon SageMaker account or an algorithm in Amazon Web Services Marketplace that you are subscribed to.

      • ModelDataUrl (string) --

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

        Note

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

      • AlgorithmName (string) -- [REQUIRED]

        The name of an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your Amazon SageMaker account or an algorithm in Amazon Web Services Marketplace that you are subscribed to.

type CertifyForMarketplace

boolean

param CertifyForMarketplace

Whether to certify the model package for listing on Amazon Web Services Marketplace.

This parameter is optional for unversioned models, and does not apply to versioned models.

type Tags

list

param Tags

A list of key value pairs associated with the model. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide .

  • (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.

type ModelApprovalStatus

string

param ModelApprovalStatus

Whether the model is approved for deployment.

This parameter is optional for versioned models, and does not apply to unversioned models.

For versioned models, the value of this parameter must be set to Approved to deploy the model.

type MetadataProperties

dict

param MetadataProperties

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.

type ModelMetrics

dict

param ModelMetrics

A structure that contains model metrics reports.

  • ModelQuality (dict) --

    Metrics that measure the quality of a model.

    • Statistics (dict) --

      Model quality statistics.

      • ContentType (string) -- [REQUIRED]

      • ContentDigest (string) --

      • S3Uri (string) -- [REQUIRED]

    • Constraints (dict) --

      Model quality constraints.

      • ContentType (string) -- [REQUIRED]

      • ContentDigest (string) --

      • S3Uri (string) -- [REQUIRED]

  • ModelDataQuality (dict) --

    Metrics that measure the quality of the input data for a model.

    • Statistics (dict) --

      Data quality statistics for a model.

      • ContentType (string) -- [REQUIRED]

      • ContentDigest (string) --

      • S3Uri (string) -- [REQUIRED]

    • Constraints (dict) --

      Data quality constraints for a model.

      • ContentType (string) -- [REQUIRED]

      • ContentDigest (string) --

      • S3Uri (string) -- [REQUIRED]

  • Bias (dict) --

    Metrics that measure bais in a model.

    • Report (dict) --

      The bias report for a model

      • ContentType (string) -- [REQUIRED]

      • ContentDigest (string) --

      • S3Uri (string) -- [REQUIRED]

  • Explainability (dict) --

    Metrics that help explain a model.

    • Report (dict) --

      The explainability report for a model.

      • ContentType (string) -- [REQUIRED]

      • ContentDigest (string) --

      • S3Uri (string) -- [REQUIRED]

type ClientToken

string

param ClientToken

A unique token that guarantees that the call to this API is idempotent.

This field is autopopulated if not provided.

type CustomerMetadataProperties

dict

param CustomerMetadataProperties

The metadata properties associated with the model package versions.

  • (string) --

    • (string) --

rtype

dict

returns

Response Syntax

{
    'ModelPackageArn': 'string'
}

Response Structure

  • (dict) --

    • ModelPackageArn (string) --

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

DescribeModelPackage (updated) Link ¶
Changes (response)
{'CustomerMetadataProperties': {'string': 'string'}}

Returns a description of the specified model package, which is used to create SageMaker models or list them on Amazon Web Services Marketplace.

To create models in SageMaker, buyers can subscribe to model packages listed on Amazon Web Services Marketplace.

See also: AWS API Documentation

Request Syntax

client.describe_model_package(
    ModelPackageName='string'
)
type ModelPackageName

string

param ModelPackageName

[REQUIRED]

The name or Amazon Resource Name (ARN) of the model package to describe.

When you specify a name, the name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).

rtype

dict

returns

Response Syntax

{
    'ModelPackageName': 'string',
    'ModelPackageGroupName': 'string',
    'ModelPackageVersion': 123,
    'ModelPackageArn': 'string',
    'ModelPackageDescription': 'string',
    'CreationTime': datetime(2015, 1, 1),
    'InferenceSpecification': {
        'Containers': [
            {
                'ContainerHostname': 'string',
                'Image': 'string',
                'ImageDigest': 'string',
                'ModelDataUrl': 'string',
                'ProductId': 'string',
                'Environment': {
                    'string': 'string'
                }
            },
        ],
        'SupportedTransformInstanceTypes': [
            'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
        ],
        'SupportedRealtimeInferenceInstanceTypes': [
            'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge',
        ],
        'SupportedContentTypes': [
            'string',
        ],
        'SupportedResponseMIMETypes': [
            'string',
        ]
    },
    'SourceAlgorithmSpecification': {
        'SourceAlgorithms': [
            {
                'ModelDataUrl': 'string',
                'AlgorithmName': 'string'
            },
        ]
    },
    'ValidationSpecification': {
        'ValidationRole': 'string',
        'ValidationProfiles': [
            {
                'ProfileName': 'string',
                'TransformJobDefinition': {
                    'MaxConcurrentTransforms': 123,
                    'MaxPayloadInMB': 123,
                    'BatchStrategy': 'MultiRecord'|'SingleRecord',
                    'Environment': {
                        'string': 'string'
                    },
                    'TransformInput': {
                        'DataSource': {
                            'S3DataSource': {
                                'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
                                'S3Uri': 'string'
                            }
                        },
                        'ContentType': 'string',
                        'CompressionType': 'None'|'Gzip',
                        'SplitType': 'None'|'Line'|'RecordIO'|'TFRecord'
                    },
                    'TransformOutput': {
                        'S3OutputPath': 'string',
                        'Accept': 'string',
                        'AssembleWith': 'None'|'Line',
                        'KmsKeyId': 'string'
                    },
                    'TransformResources': {
                        'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
                        'InstanceCount': 123,
                        'VolumeKmsKeyId': 'string'
                    }
                }
            },
        ]
    },
    'ModelPackageStatus': 'Pending'|'InProgress'|'Completed'|'Failed'|'Deleting',
    'ModelPackageStatusDetails': {
        'ValidationStatuses': [
            {
                'Name': 'string',
                'Status': 'NotStarted'|'InProgress'|'Completed'|'Failed',
                'FailureReason': 'string'
            },
        ],
        'ImageScanStatuses': [
            {
                'Name': 'string',
                'Status': 'NotStarted'|'InProgress'|'Completed'|'Failed',
                'FailureReason': 'string'
            },
        ]
    },
    'CertifyForMarketplace': True|False,
    'ModelApprovalStatus': 'Approved'|'Rejected'|'PendingManualApproval',
    'CreatedBy': {
        'UserProfileArn': 'string',
        'UserProfileName': 'string',
        'DomainId': 'string'
    },
    'MetadataProperties': {
        'CommitId': 'string',
        'Repository': 'string',
        'GeneratedBy': 'string',
        'ProjectId': 'string'
    },
    'ModelMetrics': {
        'ModelQuality': {
            'Statistics': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            },
            'Constraints': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            }
        },
        'ModelDataQuality': {
            'Statistics': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            },
            'Constraints': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            }
        },
        'Bias': {
            'Report': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            }
        },
        'Explainability': {
            'Report': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            }
        }
    },
    'LastModifiedTime': datetime(2015, 1, 1),
    'LastModifiedBy': {
        'UserProfileArn': 'string',
        'UserProfileName': 'string',
        'DomainId': 'string'
    },
    'ApprovalDescription': 'string',
    'CustomerMetadataProperties': {
        'string': 'string'
    }
}

Response Structure

  • (dict) --

    • ModelPackageName (string) --

      The name of the model package being described.

    • ModelPackageGroupName (string) --

      If the model is a versioned model, the name of the model group that the versioned model belongs to.

    • ModelPackageVersion (integer) --

      The version of the model package.

    • ModelPackageArn (string) --

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

    • ModelPackageDescription (string) --

      A brief summary of the model package.

    • CreationTime (datetime) --

      A timestamp specifying when the model package was created.

    • InferenceSpecification (dict) --

      Details about inference jobs that can be run with models based on this model package.

      • Containers (list) --

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

        • (dict) --

          Describes the Docker container for the model package.

          • ContainerHostname (string) --

            The DNS host name for the Docker container.

          • Image (string) --

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

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

          • ImageDigest (string) --

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

          • ModelDataUrl (string) --

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

            Note

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

          • ProductId (string) --

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

          • Environment (dict) --

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

            • (string) --

              • (string) --

      • 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) --

    • SourceAlgorithmSpecification (dict) --

      Details about the algorithm that was used to create the model package.

      • SourceAlgorithms (list) --

        A list of the algorithms that were used to create a model package.

        • (dict) --

          Specifies an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your Amazon SageMaker account or an algorithm in Amazon Web Services Marketplace that you are subscribed to.

          • ModelDataUrl (string) --

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

            Note

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

          • AlgorithmName (string) --

            The name of an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your Amazon SageMaker account or an algorithm in Amazon Web Services Marketplace that you are subscribed to.

    • ValidationSpecification (dict) --

      Configurations for one or more transform jobs that SageMaker runs to test the model package.

      • ValidationRole (string) --

        The IAM roles to be used for the validation of the model package.

      • ValidationProfiles (list) --

        An array of ModelPackageValidationProfile objects, each of which specifies a batch transform job that Amazon SageMaker runs to validate your model package.

        • (dict) --

          Contains data, such as the inputs and targeted instance types that are used in the process of validating the model package.

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

          • ProfileName (string) --

            The name of the profile for the model package.

          • TransformJobDefinition (dict) --

            The TransformJobDefinition object that describes the transform job used for the validation of the model package.

            • MaxConcurrentTransforms (integer) --

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

            • MaxPayloadInMB (integer) --

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

            • BatchStrategy (string) --

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

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

            • Environment (dict) --

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

              • (string) --

                • (string) --

            • TransformInput (dict) --

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

              • DataSource (dict) --

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

                • S3DataSource (dict) --

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

                  • S3DataType (string) --

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

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

                    The following values are compatible: ManifestFile , S3Prefix

                    The following value is not compatible: AugmentedManifestFile

                  • S3Uri (string) --

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

                    • 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) --

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

              • S3OutputPath (string) --

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

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

              • Accept (string) --

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

              • AssembleWith (string) --

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

              • KmsKeyId (string) --

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

                • 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) --

              Identifies the ML compute instances for the transform job.

              • InstanceType (string) --

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

              • InstanceCount (integer) --

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

              • VolumeKmsKeyId (string) --

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

                Note

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

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

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

                The VolumeKmsKeyId can be any of the following formats:

                • 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

    • ModelPackageStatus (string) --

      The current status of the model package.

    • ModelPackageStatusDetails (dict) --

      Details about the current status of the model package.

      • ValidationStatuses (list) --

        The validation status of the model package.

        • (dict) --

          Represents the overall status of a model package.

          • Name (string) --

            The name of the model package for which the overall status is being reported.

          • Status (string) --

            The current status.

          • FailureReason (string) --

            if the overall status is Failed , the reason for the failure.

      • ImageScanStatuses (list) --

        The status of the scan of the Docker image container for the model package.

        • (dict) --

          Represents the overall status of a model package.

          • Name (string) --

            The name of the model package for which the overall status is being reported.

          • Status (string) --

            The current status.

          • FailureReason (string) --

            if the overall status is Failed , the reason for the failure.

    • CertifyForMarketplace (boolean) --

      Whether the model package is certified for listing on Amazon Web Services Marketplace.

    • ModelApprovalStatus (string) --

      The approval status of the model package.

    • CreatedBy (dict) --

      Information about the user who created or modified an experiment, trial, trial component, or project.

      • UserProfileArn (string) --

        The Amazon Resource Name (ARN) of the user's profile.

      • UserProfileName (string) --

        The name of the user's profile.

      • DomainId (string) --

        The domain associated with the user.

    • MetadataProperties (dict) --

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

      • CommitId (string) --

        The commit ID.

      • Repository (string) --

        The repository.

      • GeneratedBy (string) --

        The entity this entity was generated by.

      • ProjectId (string) --

        The project ID.

    • ModelMetrics (dict) --

      Metrics for the model.

      • ModelQuality (dict) --

        Metrics that measure the quality of a model.

        • Statistics (dict) --

          Model quality statistics.

          • ContentType (string) --

          • ContentDigest (string) --

          • S3Uri (string) --

        • Constraints (dict) --

          Model quality constraints.

          • ContentType (string) --

          • ContentDigest (string) --

          • S3Uri (string) --

      • ModelDataQuality (dict) --

        Metrics that measure the quality of the input data for a model.

        • Statistics (dict) --

          Data quality statistics for a model.

          • ContentType (string) --

          • ContentDigest (string) --

          • S3Uri (string) --

        • Constraints (dict) --

          Data quality constraints for a model.

          • ContentType (string) --

          • ContentDigest (string) --

          • S3Uri (string) --

      • Bias (dict) --

        Metrics that measure bais in a model.

        • Report (dict) --

          The bias report for a model

          • ContentType (string) --

          • ContentDigest (string) --

          • S3Uri (string) --

      • Explainability (dict) --

        Metrics that help explain a model.

        • Report (dict) --

          The explainability report for a model.

          • ContentType (string) --

          • ContentDigest (string) --

          • S3Uri (string) --

    • LastModifiedTime (datetime) --

      The last time the model package was modified.

    • LastModifiedBy (dict) --

      Information about the user who created or modified an experiment, trial, trial component, or project.

      • UserProfileArn (string) --

        The Amazon Resource Name (ARN) of the user's profile.

      • UserProfileName (string) --

        The name of the user's profile.

      • DomainId (string) --

        The domain associated with the user.

    • ApprovalDescription (string) --

      A description provided for the model approval.

    • CustomerMetadataProperties (dict) --

      The metadata properties associated with the model package versions.

      • (string) --

        • (string) --

DescribeProject (updated) Link ¶
Changes (response)
{'LastModifiedBy': {'DomainId': 'string',
                    'UserProfileArn': 'string',
                    'UserProfileName': 'string'},
 'LastModifiedTime': 'timestamp',
 'ProjectStatus': {'UpdateFailed', 'UpdateInProgress', 'UpdateCompleted'}}

Describes the details of a project.

See also: AWS API Documentation

Request Syntax

client.describe_project(
    ProjectName='string'
)
type ProjectName

string

param ProjectName

[REQUIRED]

The name of the project to describe.

rtype

dict

returns

Response Syntax

{
    'ProjectArn': 'string',
    'ProjectName': 'string',
    'ProjectId': 'string',
    'ProjectDescription': 'string',
    'ServiceCatalogProvisioningDetails': {
        'ProductId': 'string',
        'ProvisioningArtifactId': 'string',
        'PathId': 'string',
        'ProvisioningParameters': [
            {
                'Key': 'string',
                'Value': 'string'
            },
        ]
    },
    'ServiceCatalogProvisionedProductDetails': {
        'ProvisionedProductId': 'string',
        'ProvisionedProductStatusMessage': 'string'
    },
    'ProjectStatus': 'Pending'|'CreateInProgress'|'CreateCompleted'|'CreateFailed'|'DeleteInProgress'|'DeleteFailed'|'DeleteCompleted'|'UpdateInProgress'|'UpdateCompleted'|'UpdateFailed',
    'CreatedBy': {
        'UserProfileArn': 'string',
        'UserProfileName': 'string',
        'DomainId': 'string'
    },
    'CreationTime': datetime(2015, 1, 1),
    'LastModifiedTime': datetime(2015, 1, 1),
    'LastModifiedBy': {
        'UserProfileArn': 'string',
        'UserProfileName': 'string',
        'DomainId': 'string'
    }
}

Response Structure

  • (dict) --

    • ProjectArn (string) --

      The Amazon Resource Name (ARN) of the project.

    • ProjectName (string) --

      The name of the project.

    • ProjectId (string) --

      The ID of the project.

    • ProjectDescription (string) --

      The description of the project.

    • ServiceCatalogProvisioningDetails (dict) --

      Information used to provision a service catalog product. For information, see What is Amazon Web Services Service Catalog.

      • ProductId (string) --

        The ID of the product to provision.

      • ProvisioningArtifactId (string) --

        The ID of the provisioning artifact.

      • PathId (string) --

        The path identifier of the product. This value is optional if the product has a default path, and required if the product has more than one path.

      • ProvisioningParameters (list) --

        A list of key value pairs that you specify when you provision a product.

        • (dict) --

          A key value pair used when you provision a project as a service catalog product. For information, see What is Amazon Web Services Service Catalog.

          • Key (string) --

            The key that identifies a provisioning parameter.

          • Value (string) --

            The value of the provisioning parameter.

    • ServiceCatalogProvisionedProductDetails (dict) --

      Information about a provisioned service catalog product.

      • ProvisionedProductId (string) --

        The ID of the provisioned product.

      • ProvisionedProductStatusMessage (string) --

        The current status of the product.

        • AVAILABLE - Stable state, ready to perform any operation. The most recent operation succeeded and completed.

        • UNDER_CHANGE - Transitive state. Operations performed might not have valid results. Wait for an AVAILABLE status before performing operations.

        • TAINTED - Stable state, ready to perform any operation. The stack has completed the requested operation but is not exactly what was requested. For example, a request to update to a new version failed and the stack rolled back to the current version.

        • ERROR - An unexpected error occurred. The provisioned product exists but the stack is not running. For example, CloudFormation received a parameter value that was not valid and could not launch the stack.

        • PLAN_IN_PROGRESS - Transitive state. The plan operations were performed to provision a new product, but resources have not yet been created. After reviewing the list of resources to be created, execute the plan. Wait for an AVAILABLE status before performing operations.

    • ProjectStatus (string) --

      The status of the project.

    • CreatedBy (dict) --

      Information about the user who created or modified an experiment, trial, trial component, or project.

      • UserProfileArn (string) --

        The Amazon Resource Name (ARN) of the user's profile.

      • UserProfileName (string) --

        The name of the user's profile.

      • DomainId (string) --

        The domain associated with the user.

    • CreationTime (datetime) --

      The time when the project was created.

    • LastModifiedTime (datetime) --

      The timestamp when project was last modified.

    • LastModifiedBy (dict) --

      Information about the user who created or modified an experiment, trial, trial component, or project.

      • UserProfileArn (string) --

        The Amazon Resource Name (ARN) of the user's profile.

      • UserProfileName (string) --

        The name of the user's profile.

      • DomainId (string) --

        The domain associated with the user.

ListProjects (updated) Link ¶
Changes (response)
{'ProjectSummaryList': {'ProjectStatus': {'UpdateCompleted',
                                          'UpdateFailed',
                                          'UpdateInProgress'}}}

Gets a list of the projects in an Amazon Web Services account.

See also: AWS API Documentation

Request Syntax

client.list_projects(
    CreationTimeAfter=datetime(2015, 1, 1),
    CreationTimeBefore=datetime(2015, 1, 1),
    MaxResults=123,
    NameContains='string',
    NextToken='string',
    SortBy='Name'|'CreationTime',
    SortOrder='Ascending'|'Descending'
)
type CreationTimeAfter

datetime

param CreationTimeAfter

A filter that returns the projects that were created after a specified time.

type CreationTimeBefore

datetime

param CreationTimeBefore

A filter that returns the projects that were created before a specified time.

type MaxResults

integer

param MaxResults

The maximum number of projects to return in the response.

type NameContains

string

param NameContains

A filter that returns the projects whose name contains a specified string.

type NextToken

string

param NextToken

If the result of the previous ListProjects request was truncated, the response includes a NextToken . To retrieve the next set of projects, use the token in the next request.

type SortBy

string

param SortBy

The field by which to sort results. The default is CreationTime .

type SortOrder

string

param SortOrder

The sort order for results. The default is Ascending .

rtype

dict

returns

Response Syntax

{
    'ProjectSummaryList': [
        {
            'ProjectName': 'string',
            'ProjectDescription': 'string',
            'ProjectArn': 'string',
            'ProjectId': 'string',
            'CreationTime': datetime(2015, 1, 1),
            'ProjectStatus': 'Pending'|'CreateInProgress'|'CreateCompleted'|'CreateFailed'|'DeleteInProgress'|'DeleteFailed'|'DeleteCompleted'|'UpdateInProgress'|'UpdateCompleted'|'UpdateFailed'
        },
    ],
    'NextToken': 'string'
}

Response Structure

  • (dict) --

    • ProjectSummaryList (list) --

      A list of summaries of projects.

      • (dict) --

        Information about a project.

        • ProjectName (string) --

          The name of the project.

        • ProjectDescription (string) --

          The description of the project.

        • ProjectArn (string) --

          The Amazon Resource Name (ARN) of the project.

        • ProjectId (string) --

          The ID of the project.

        • CreationTime (datetime) --

          The time that the project was created.

        • ProjectStatus (string) --

          The status of the project.

    • NextToken (string) --

      If the result of the previous ListCompilationJobs request was truncated, the response includes a NextToken . To retrieve the next set of model compilation jobs, use the token in the next request.

UpdateModelPackage (updated) Link ¶
Changes (request)
{'CustomerMetadataProperties': {'string': 'string'},
 'CustomerMetadataPropertiesToRemove': ['string']}

Updates a versioned model.

See also: AWS API Documentation

Request Syntax

client.update_model_package(
    ModelPackageArn='string',
    ModelApprovalStatus='Approved'|'Rejected'|'PendingManualApproval',
    ApprovalDescription='string',
    CustomerMetadataProperties={
        'string': 'string'
    },
    CustomerMetadataPropertiesToRemove=[
        'string',
    ]
)
type ModelPackageArn

string

param ModelPackageArn

[REQUIRED]

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

type ModelApprovalStatus

string

param ModelApprovalStatus

The approval status of the model.

type ApprovalDescription

string

param ApprovalDescription

A description for the approval status of the model.

type CustomerMetadataProperties

dict

param CustomerMetadataProperties

The metadata properties associated with the model package versions.

  • (string) --

    • (string) --

type CustomerMetadataPropertiesToRemove

list

param CustomerMetadataPropertiesToRemove

The metadata properties associated with the model package versions to remove.

  • (string) --

rtype

dict

returns

Response Syntax

{
    'ModelPackageArn': 'string'
}

Response Structure

  • (dict) --

    • ModelPackageArn (string) --

      The Amazon Resource Name (ARN) of the model.