Amazon SageMaker Service

2024/02/29 - Amazon SageMaker Service - 7 updated api methods

Changes  Adds support for ModelDataSource in Model Packages to support unzipped models. Adds support to specify SourceUri for models which allows registration of models without mandating a container for hosting. Using SourceUri, customers can decouple the model from hosting information during registration.

BatchDescribeModelPackage (updated) Link ¶
Changes (response)
{'ModelPackageSummaries': {'InferenceSpecification': {'Containers': {'ModelDataSource': {'S3DataSource': {'CompressionType': 'None '
                                                                                                                             '| '
                                                                                                                             'Gzip',
                                                                                                          'ModelAccessConfig': {'AcceptEula': 'boolean'},
                                                                                                          'S3DataType': 'S3Prefix '
                                                                                                                        '| '
                                                                                                                        'S3Object',
                                                                                                          'S3Uri': 'string'}}}}}}

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',
                        'ModelDataSource': {
                            'S3DataSource': {
                                'S3Uri': 'string',
                                'S3DataType': 'S3Prefix'|'S3Object',
                                'CompressionType': 'None'|'Gzip',
                                'ModelAccessConfig': {
                                    'AcceptEula': True|False
                                }
                            }
                        },
                        'ProductId': 'string',
                        'Environment': {
                            'string': 'string'
                        },
                        'ModelInput': {
                            'DataInputConfig': 'string'
                        },
                        'Framework': 'string',
                        'FrameworkVersion': 'string',
                        'NearestModelName': 'string',
                        'AdditionalS3DataSource': {
                            'S3DataType': 'S3Object'|'S3Prefix',
                            'S3Uri': 'string',
                            'CompressionType': 'None'|'Gzip'
                        }
                    },
                ],
                '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.dl1.24xlarge'|'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'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge',
                ],
                '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.

                • ModelDataSource (dict) --

                  Specifies the location of ML model data to deploy during endpoint creation.

                  • S3DataSource (dict) --

                    Specifies the S3 location of ML model data to deploy.

                    • S3Uri (string) --

                      Specifies the S3 path of ML model data to deploy.

                    • S3DataType (string) --

                      Specifies the type of ML model data to deploy.

                      If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by S3Uri always ends with a forward slash (/).

                      If you choose S3Object , S3Uri identifies an object that is the ML model data to deploy.

                    • CompressionType (string) --

                      Specifies how the ML model data is prepared.

                      If you choose Gzip and choose S3Object as the value of S3DataType , S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.

                      If you choose None and chooose S3Object as the value of S3DataType , S3Uri identifies an object that represents an uncompressed ML model to deploy.

                      If you choose None and choose S3Prefix as the value of S3DataType , S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.

                      If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:

                      • If you choose S3Object as the value of S3DataType , then SageMaker will split the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of the file holding the content of the S3 object.

                      • If you choose S3Prefix as the value of S3DataType , then for each S3 object under the key name pefix referenced by S3Uri , SageMaker will trim its key by the prefix, and use the remainder as the path (relative to /opt/ml/model ) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object.

                      • Do not use any of the following as file names or directory names:

                        • An empty or blank string

                        • A string which contains null bytes

                        • A string longer than 255 bytes

                        • A single dot (. )

                        • A double dot (.. )

                      • Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the value of S3DataType , then it will result in name clash between /opt/ml/model/weights (a regular file) and /opt/ml/model/weights/ (a directory).

                      • Do not organize the model artifacts in S3 console using folders . When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.

                    • ModelAccessConfig (dict) --

                      Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig . You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.

                      • AcceptEula (boolean) --

                        Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.

                • 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 .

                • AdditionalS3DataSource (dict) --

                  The additional data source that is used during inference in the Docker container for your model package.

                  • S3DataType (string) --

                    The data type of the additional data source that you specify for use in inference or training.

                  • S3Uri (string) --

                    The uniform resource identifier (URI) used to identify an additional data source used in inference or training.

                  • CompressionType (string) --

                    The type of compression used for an additional data source used in inference or training. Specify None if your additional data source is not compressed.

            • 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) --

CreateAlgorithm (updated) Link ¶
Changes (request)
{'InferenceSpecification': {'Containers': {'ModelDataSource': {'S3DataSource': {'CompressionType': 'None '
                                                                                                   '| '
                                                                                                   'Gzip',
                                                                                'ModelAccessConfig': {'AcceptEula': 'boolean'},
                                                                                'S3DataType': 'S3Prefix '
                                                                                              '| '
                                                                                              'S3Object',
                                                                                'S3Uri': 'string'}}}}}

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

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.p4de.24xlarge'|'ml.p5.48xlarge'|'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'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.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'
            },
        ],
        'AdditionalS3DataSource': {
            'S3DataType': 'S3Object'|'S3Prefix',
            'S3Uri': 'string',
            'CompressionType': 'None'|'Gzip'
        }
    },
    InferenceSpecification={
        'Containers': [
            {
                'ContainerHostname': 'string',
                'Image': 'string',
                'ImageDigest': 'string',
                'ModelDataUrl': 'string',
                'ModelDataSource': {
                    'S3DataSource': {
                        'S3Uri': 'string',
                        'S3DataType': 'S3Prefix'|'S3Object',
                        'CompressionType': 'None'|'Gzip',
                        'ModelAccessConfig': {
                            'AcceptEula': True|False
                        }
                    }
                },
                'ProductId': 'string',
                'Environment': {
                    'string': 'string'
                },
                'ModelInput': {
                    'DataInputConfig': 'string'
                },
                'Framework': 'string',
                'FrameworkVersion': 'string',
                'NearestModelName': 'string',
                'AdditionalS3DataSource': {
                    'S3DataType': 'S3Object'|'S3Prefix',
                    'S3Uri': 'string',
                    'CompressionType': 'None'|'Gzip'
                }
            },
        ],
        '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.dl1.24xlarge'|'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'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge',
        ],
        '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',
                        'CompressionType': 'GZIP'|'NONE'
                    },
                    '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.p4de.24xlarge'|'ml.p5.48xlarge'|'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'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
                        'InstanceCount': 123,
                        'VolumeSizeInGB': 123,
                        'VolumeKmsKeyId': 'string',
                        'KeepAlivePeriodInSeconds': 123,
                        '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.p4de.24xlarge'|'ml.p5.48xlarge'|'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'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
                                'InstanceCount': 123,
                                'InstanceGroupName': 'string'
                            },
                        ]
                    },
                    'StoppingCondition': {
                        'MaxRuntimeInSeconds': 123,
                        'MaxWaitTimeInSeconds': 123,
                        'MaxPendingTimeInSeconds': 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'
        },
    ]
)
type AlgorithmName

string

param AlgorithmName

[REQUIRED]

The name of the algorithm.

type AlgorithmDescription

string

param AlgorithmDescription

A description of the algorithm.

type TrainingSpecification

dict

param TrainingSpecification

[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 . You can view these logs to understand how your training job performs and check for any errors encountered during training. SageMaker hyperparameter tuning captures all defined metrics. Specify one of the defined metrics to use as an objective metric using the TuningObjective parameter in the HyperParameterTrainingJobDefinition API to evaluate job performance during hyperparameter tuning.

      • 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 metrics and environment variables .

  • 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. If you want to define a custom objective metric, see Define metrics and environment variables .

      • 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.

  • AdditionalS3DataSource (dict) --

    The additional data source used during the training job.

    • S3DataType (string) -- [REQUIRED]

      The data type of the additional data source that you specify for use in inference or training.

    • S3Uri (string) -- [REQUIRED]

      The uniform resource identifier (URI) used to identify an additional data source used in inference or training.

    • CompressionType (string) --

      The type of compression used for an additional data source used in inference or training. Specify None if your additional data source is not compressed.

type InferenceSpecification

dict

param InferenceSpecification

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.

      • ModelDataSource (dict) --

        Specifies the location of ML model data to deploy during endpoint creation.

        • S3DataSource (dict) --

          Specifies the S3 location of ML model data to deploy.

          • S3Uri (string) -- [REQUIRED]

            Specifies the S3 path of ML model data to deploy.

          • S3DataType (string) -- [REQUIRED]

            Specifies the type of ML model data to deploy.

            If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by S3Uri always ends with a forward slash (/).

            If you choose S3Object , S3Uri identifies an object that is the ML model data to deploy.

          • CompressionType (string) -- [REQUIRED]

            Specifies how the ML model data is prepared.

            If you choose Gzip and choose S3Object as the value of S3DataType , S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.

            If you choose None and chooose S3Object as the value of S3DataType , S3Uri identifies an object that represents an uncompressed ML model to deploy.

            If you choose None and choose S3Prefix as the value of S3DataType , S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.

            If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:

            • If you choose S3Object as the value of S3DataType , then SageMaker will split the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of the file holding the content of the S3 object.

            • If you choose S3Prefix as the value of S3DataType , then for each S3 object under the key name pefix referenced by S3Uri , SageMaker will trim its key by the prefix, and use the remainder as the path (relative to /opt/ml/model ) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object.

            • Do not use any of the following as file names or directory names:

              • An empty or blank string

              • A string which contains null bytes

              • A string longer than 255 bytes

              • A single dot (. )

              • A double dot (.. )

            • Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the value of S3DataType , then it will result in name clash between /opt/ml/model/weights (a regular file) and /opt/ml/model/weights/ (a directory).

            • Do not organize the model artifacts in S3 console using folders . When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.

          • ModelAccessConfig (dict) --

            Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig . You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.

            • AcceptEula (boolean) -- [REQUIRED]

              Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.

      • 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 .

      • AdditionalS3DataSource (dict) --

        The additional data source that is used during inference in the Docker container for your model package.

        • S3DataType (string) -- [REQUIRED]

          The data type of the additional data source that you specify for use in inference or training.

        • S3Uri (string) -- [REQUIRED]

          The uniform resource identifier (URI) used to identify an additional data source used in inference or training.

        • CompressionType (string) --

          The type of compression used for an additional data source used in inference or training. Specify None if your additional data source is not compressed.

  • 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) --

type ValidationSpecification

dict

param ValidationSpecification

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.

                  Your input bucket must be located in same Amazon Web Services region as your training job.

                • 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. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide . If the output data is stored in Amazon S3 Express One Zone, it is encrypted with server-side encryption with Amazon S3 managed keys (SSE-S3). KMS key is not supported for Amazon S3 Express One Zone

            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 .

          • CompressionType (string) --

            The model output compression type. Select None to output an uncompressed model, recommended for large model outputs. Defaults to gzip.

        • 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.

            Note

            SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022.

            Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (ml.p4de.24xlarge ) to reduce model training time. The ml.p4de.24xlarge instances are available in the following Amazon Web Services Regions.

            • US East (N. Virginia) (us-east-1)

            • US West (Oregon) (us-west-2)

            To request quota limit increase and start using P4de instances, contact the SageMaker Training service team through your account team.

          • 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"

          • KeepAlivePeriodInSeconds (integer) --

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

          • 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.

        • 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.

          • MaxPendingTimeInSeconds (integer) --

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

      • 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

type CertifyForMarketplace

boolean

param CertifyForMarketplace

Whether to certify the algorithm so that it can be listed in Amazon Web Services Marketplace.

type Tags

list

param Tags

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.

rtype

dict

returns

Response Syntax

{
    'AlgorithmArn': 'string'
}

Response Structure

  • (dict) --

    • AlgorithmArn (string) --

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

CreateModelPackage (updated) Link ¶
Changes (request)
{'AdditionalInferenceSpecifications': {'Containers': {'ModelDataSource': {'S3DataSource': {'CompressionType': 'None '
                                                                                                              '| '
                                                                                                              'Gzip',
                                                                                           'ModelAccessConfig': {'AcceptEula': 'boolean'},
                                                                                           'S3DataType': 'S3Prefix '
                                                                                                         '| '
                                                                                                         'S3Object',
                                                                                           'S3Uri': 'string'}}}},
 'InferenceSpecification': {'Containers': {'ModelDataSource': {'S3DataSource': {'CompressionType': 'None '
                                                                                                   '| '
                                                                                                   'Gzip',
                                                                                'ModelAccessConfig': {'AcceptEula': 'boolean'},
                                                                                'S3DataType': 'S3Prefix '
                                                                                              '| '
                                                                                              'S3Object',
                                                                                'S3Uri': 'string'}}}},
 'SourceAlgorithmSpecification': {'SourceAlgorithms': {'ModelDataSource': {'S3DataSource': {'CompressionType': 'None '
                                                                                                               '| '
                                                                                                               'Gzip',
                                                                                            'ModelAccessConfig': {'AcceptEula': 'boolean'},
                                                                                            'S3DataType': 'S3Prefix '
                                                                                                          '| '
                                                                                                          'S3Object',
                                                                                            'S3Uri': 'string'}}}},
 'SourceUri': 'string'}

Creates a model package that you can use to create 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 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',
                'ModelDataSource': {
                    'S3DataSource': {
                        'S3Uri': 'string',
                        'S3DataType': 'S3Prefix'|'S3Object',
                        'CompressionType': 'None'|'Gzip',
                        'ModelAccessConfig': {
                            'AcceptEula': True|False
                        }
                    }
                },
                'ProductId': 'string',
                'Environment': {
                    'string': 'string'
                },
                'ModelInput': {
                    'DataInputConfig': 'string'
                },
                'Framework': 'string',
                'FrameworkVersion': 'string',
                'NearestModelName': 'string',
                'AdditionalS3DataSource': {
                    'S3DataType': 'S3Object'|'S3Prefix',
                    'S3Uri': 'string',
                    'CompressionType': 'None'|'Gzip'
                }
            },
        ],
        '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.dl1.24xlarge'|'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'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge',
        ],
        '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',
                'ModelDataSource': {
                    'S3DataSource': {
                        'S3Uri': 'string',
                        'S3DataType': 'S3Prefix'|'S3Object',
                        'CompressionType': 'None'|'Gzip',
                        'ModelAccessConfig': {
                            'AcceptEula': True|False
                        }
                    }
                },
                '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'
            },
            'PreTrainingReport': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            },
            'PostTrainingReport': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            }
        },
        'Explainability': {
            'Report': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            }
        }
    },
    ClientToken='string',
    Domain='string',
    Task='string',
    SamplePayloadUrl='string',
    CustomerMetadataProperties={
        'string': 'string'
    },
    DriftCheckBaselines={
        'Bias': {
            'ConfigFile': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            },
            'PreTrainingConstraints': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            },
            'PostTrainingConstraints': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            }
        },
        'Explainability': {
            'Constraints': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            },
            'ConfigFile': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            }
        },
        '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'
            }
        }
    },
    AdditionalInferenceSpecifications=[
        {
            'Name': 'string',
            'Description': 'string',
            'Containers': [
                {
                    'ContainerHostname': 'string',
                    'Image': 'string',
                    'ImageDigest': 'string',
                    'ModelDataUrl': 'string',
                    'ModelDataSource': {
                        'S3DataSource': {
                            'S3Uri': 'string',
                            'S3DataType': 'S3Prefix'|'S3Object',
                            'CompressionType': 'None'|'Gzip',
                            'ModelAccessConfig': {
                                'AcceptEula': True|False
                            }
                        }
                    },
                    'ProductId': 'string',
                    'Environment': {
                        'string': 'string'
                    },
                    'ModelInput': {
                        'DataInputConfig': 'string'
                    },
                    'Framework': 'string',
                    'FrameworkVersion': 'string',
                    'NearestModelName': 'string',
                    'AdditionalS3DataSource': {
                        'S3DataType': 'S3Object'|'S3Prefix',
                        'S3Uri': 'string',
                        'CompressionType': 'None'|'Gzip'
                    }
                },
            ],
            '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.dl1.24xlarge'|'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'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge',
            ],
            'SupportedContentTypes': [
                'string',
            ],
            'SupportedResponseMIMETypes': [
                'string',
            ]
        },
    ],
    SkipModelValidation='All'|'None',
    SourceUri='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 you can run with models based on this model package, including the following information:

  • 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 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.

      • ModelDataSource (dict) --

        Specifies the location of ML model data to deploy during endpoint creation.

        • S3DataSource (dict) --

          Specifies the S3 location of ML model data to deploy.

          • S3Uri (string) -- [REQUIRED]

            Specifies the S3 path of ML model data to deploy.

          • S3DataType (string) -- [REQUIRED]

            Specifies the type of ML model data to deploy.

            If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by S3Uri always ends with a forward slash (/).

            If you choose S3Object , S3Uri identifies an object that is the ML model data to deploy.

          • CompressionType (string) -- [REQUIRED]

            Specifies how the ML model data is prepared.

            If you choose Gzip and choose S3Object as the value of S3DataType , S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.

            If you choose None and chooose S3Object as the value of S3DataType , S3Uri identifies an object that represents an uncompressed ML model to deploy.

            If you choose None and choose S3Prefix as the value of S3DataType , S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.

            If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:

            • If you choose S3Object as the value of S3DataType , then SageMaker will split the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of the file holding the content of the S3 object.

            • If you choose S3Prefix as the value of S3DataType , then for each S3 object under the key name pefix referenced by S3Uri , SageMaker will trim its key by the prefix, and use the remainder as the path (relative to /opt/ml/model ) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object.

            • Do not use any of the following as file names or directory names:

              • An empty or blank string

              • A string which contains null bytes

              • A string longer than 255 bytes

              • A single dot (. )

              • A double dot (.. )

            • Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the value of S3DataType , then it will result in name clash between /opt/ml/model/weights (a regular file) and /opt/ml/model/weights/ (a directory).

            • Do not organize the model artifacts in S3 console using folders . When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.

          • ModelAccessConfig (dict) --

            Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig . You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.

            • AcceptEula (boolean) -- [REQUIRED]

              Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.

      • 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 .

      • AdditionalS3DataSource (dict) --

        The additional data source that is used during inference in the Docker container for your model package.

        • S3DataType (string) -- [REQUIRED]

          The data type of the additional data source that you specify for use in inference or training.

        • S3Uri (string) -- [REQUIRED]

          The uniform resource identifier (URI) used to identify an additional data source used in inference or training.

        • CompressionType (string) --

          The type of compression used for an additional data source used in inference or training. Specify None if your additional data source is not compressed.

  • 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) --

type ValidationSpecification

dict

param ValidationSpecification

Specifies configurations for one or more transform jobs that 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 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. 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

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 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 Amazon Web Services region as the algorithm.

      • ModelDataSource (dict) --

        Specifies the location of ML model data to deploy during endpoint creation.

        • S3DataSource (dict) --

          Specifies the S3 location of ML model data to deploy.

          • S3Uri (string) -- [REQUIRED]

            Specifies the S3 path of ML model data to deploy.

          • S3DataType (string) -- [REQUIRED]

            Specifies the type of ML model data to deploy.

            If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by S3Uri always ends with a forward slash (/).

            If you choose S3Object , S3Uri identifies an object that is the ML model data to deploy.

          • CompressionType (string) -- [REQUIRED]

            Specifies how the ML model data is prepared.

            If you choose Gzip and choose S3Object as the value of S3DataType , S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.

            If you choose None and chooose S3Object as the value of S3DataType , S3Uri identifies an object that represents an uncompressed ML model to deploy.

            If you choose None and choose S3Prefix as the value of S3DataType , S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.

            If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:

            • If you choose S3Object as the value of S3DataType , then SageMaker will split the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of the file holding the content of the S3 object.

            • If you choose S3Prefix as the value of S3DataType , then for each S3 object under the key name pefix referenced by S3Uri , SageMaker will trim its key by the prefix, and use the remainder as the path (relative to /opt/ml/model ) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object.

            • Do not use any of the following as file names or directory names:

              • An empty or blank string

              • A string which contains null bytes

              • A string longer than 255 bytes

              • A single dot (. )

              • A double dot (.. )

            • Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the value of S3DataType , then it will result in name clash between /opt/ml/model/weights (a regular file) and /opt/ml/model/weights/ (a directory).

            • Do not organize the model artifacts in S3 console using folders . When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.

          • ModelAccessConfig (dict) --

            Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig . You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.

            • AcceptEula (boolean) -- [REQUIRED]

              Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.

      • 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 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 .

If you supply ModelPackageGroupName , your model package belongs to the model group you specify and uses the tags associated with the model group. In this case, you cannot supply a tag argument.

  • (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]

        The metric source content type.

      • ContentDigest (string) --

        The hash key used for the metrics source.

      • S3Uri (string) -- [REQUIRED]

        The S3 URI for the metrics source.

    • Constraints (dict) --

      Model quality constraints.

      • ContentType (string) -- [REQUIRED]

        The metric source content type.

      • ContentDigest (string) --

        The hash key used for the metrics source.

      • S3Uri (string) -- [REQUIRED]

        The S3 URI for the metrics source.

  • 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]

        The metric source content type.

      • ContentDigest (string) --

        The hash key used for the metrics source.

      • S3Uri (string) -- [REQUIRED]

        The S3 URI for the metrics source.

    • Constraints (dict) --

      Data quality constraints for a model.

      • ContentType (string) -- [REQUIRED]

        The metric source content type.

      • ContentDigest (string) --

        The hash key used for the metrics source.

      • S3Uri (string) -- [REQUIRED]

        The S3 URI for the metrics source.

  • Bias (dict) --

    Metrics that measure bias in a model.

    • Report (dict) --

      The bias report for a model

      • ContentType (string) -- [REQUIRED]

        The metric source content type.

      • ContentDigest (string) --

        The hash key used for the metrics source.

      • S3Uri (string) -- [REQUIRED]

        The S3 URI for the metrics source.

    • PreTrainingReport (dict) --

      The pre-training bias report for a model.

      • ContentType (string) -- [REQUIRED]

        The metric source content type.

      • ContentDigest (string) --

        The hash key used for the metrics source.

      • S3Uri (string) -- [REQUIRED]

        The S3 URI for the metrics source.

    • PostTrainingReport (dict) --

      The post-training bias report for a model.

      • ContentType (string) -- [REQUIRED]

        The metric source content type.

      • ContentDigest (string) --

        The hash key used for the metrics source.

      • S3Uri (string) -- [REQUIRED]

        The S3 URI for the metrics source.

  • Explainability (dict) --

    Metrics that help explain a model.

    • Report (dict) --

      The explainability report for a model.

      • ContentType (string) -- [REQUIRED]

        The metric source content type.

      • ContentDigest (string) --

        The hash key used for the metrics source.

      • S3Uri (string) -- [REQUIRED]

        The S3 URI for the metrics source.

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 Domain

string

param Domain

The machine learning domain of your model package and its components. Common machine learning domains include computer vision and natural language processing.

type Task

string

param Task

The machine learning task your model package accomplishes. Common machine learning tasks include object detection and image classification. The following tasks are supported by Inference Recommender: "IMAGE_CLASSIFICATION" | "OBJECT_DETECTION" | "TEXT_GENERATION" |"IMAGE_SEGMENTATION" | "FILL_MASK" | "CLASSIFICATION" | "REGRESSION" | "OTHER" .

Specify "OTHER" if none of the tasks listed fit your use case.

type SamplePayloadUrl

string

param SamplePayloadUrl

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). This archive can hold multiple files that are all equally used in the load test. Each file in the archive must satisfy the size constraints of the InvokeEndpoint call.

type CustomerMetadataProperties

dict

param CustomerMetadataProperties

The metadata properties associated with the model package versions.

  • (string) --

    • (string) --

type DriftCheckBaselines

dict

param DriftCheckBaselines

Represents the drift check baselines that can be used when the model monitor is set using the model package. For more information, see the topic on Drift Detection against Previous Baselines in SageMaker Pipelines in the Amazon SageMaker Developer Guide .

  • Bias (dict) --

    Represents the drift check bias baselines that can be used when the model monitor is set using the model package.

    • ConfigFile (dict) --

      The bias config file for a model.

      • ContentType (string) --

        The type of content stored in the file source.

      • ContentDigest (string) --

        The digest of the file source.

      • S3Uri (string) -- [REQUIRED]

        The Amazon S3 URI for the file source.

    • PreTrainingConstraints (dict) --

      The pre-training constraints.

      • ContentType (string) -- [REQUIRED]

        The metric source content type.

      • ContentDigest (string) --

        The hash key used for the metrics source.

      • S3Uri (string) -- [REQUIRED]

        The S3 URI for the metrics source.

    • PostTrainingConstraints (dict) --

      The post-training constraints.

      • ContentType (string) -- [REQUIRED]

        The metric source content type.

      • ContentDigest (string) --

        The hash key used for the metrics source.

      • S3Uri (string) -- [REQUIRED]

        The S3 URI for the metrics source.

  • Explainability (dict) --

    Represents the drift check explainability baselines that can be used when the model monitor is set using the model package.

    • Constraints (dict) --

      The drift check explainability constraints.

      • ContentType (string) -- [REQUIRED]

        The metric source content type.

      • ContentDigest (string) --

        The hash key used for the metrics source.

      • S3Uri (string) -- [REQUIRED]

        The S3 URI for the metrics source.

    • ConfigFile (dict) --

      The explainability config file for the model.

      • ContentType (string) --

        The type of content stored in the file source.

      • ContentDigest (string) --

        The digest of the file source.

      • S3Uri (string) -- [REQUIRED]

        The Amazon S3 URI for the file source.

  • ModelQuality (dict) --

    Represents the drift check model quality baselines that can be used when the model monitor is set using the model package.

    • Statistics (dict) --

      The drift check model quality statistics.

      • ContentType (string) -- [REQUIRED]

        The metric source content type.

      • ContentDigest (string) --

        The hash key used for the metrics source.

      • S3Uri (string) -- [REQUIRED]

        The S3 URI for the metrics source.

    • Constraints (dict) --

      The drift check model quality constraints.

      • ContentType (string) -- [REQUIRED]

        The metric source content type.

      • ContentDigest (string) --

        The hash key used for the metrics source.

      • S3Uri (string) -- [REQUIRED]

        The S3 URI for the metrics source.

  • ModelDataQuality (dict) --

    Represents the drift check model data quality baselines that can be used when the model monitor is set using the model package.

    • Statistics (dict) --

      The drift check model data quality statistics.

      • ContentType (string) -- [REQUIRED]

        The metric source content type.

      • ContentDigest (string) --

        The hash key used for the metrics source.

      • S3Uri (string) -- [REQUIRED]

        The S3 URI for the metrics source.

    • Constraints (dict) --

      The drift check model data quality constraints.

      • ContentType (string) -- [REQUIRED]

        The metric source content type.

      • ContentDigest (string) --

        The hash key used for the metrics source.

      • S3Uri (string) -- [REQUIRED]

        The S3 URI for the metrics source.

type AdditionalInferenceSpecifications

list

param AdditionalInferenceSpecifications

An array of additional Inference Specification objects. Each additional Inference Specification specifies artifacts based on this model package that can be used on inference endpoints. Generally used with SageMaker Neo to store the compiled artifacts.

  • (dict) --

    A structure of additional Inference Specification. Additional Inference Specification specifies details about inference jobs that can be run with models based on this model package

    • Name (string) -- [REQUIRED]

      A unique name to identify the additional inference specification. The name must be unique within the list of your additional inference specifications for a particular model package.

    • Description (string) --

      A description of the additional Inference specification

    • 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.

        • ModelDataSource (dict) --

          Specifies the location of ML model data to deploy during endpoint creation.

          • S3DataSource (dict) --

            Specifies the S3 location of ML model data to deploy.

            • S3Uri (string) -- [REQUIRED]

              Specifies the S3 path of ML model data to deploy.

            • S3DataType (string) -- [REQUIRED]

              Specifies the type of ML model data to deploy.

              If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by S3Uri always ends with a forward slash (/).

              If you choose S3Object , S3Uri identifies an object that is the ML model data to deploy.

            • CompressionType (string) -- [REQUIRED]

              Specifies how the ML model data is prepared.

              If you choose Gzip and choose S3Object as the value of S3DataType , S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.

              If you choose None and chooose S3Object as the value of S3DataType , S3Uri identifies an object that represents an uncompressed ML model to deploy.

              If you choose None and choose S3Prefix as the value of S3DataType , S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.

              If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:

              • If you choose S3Object as the value of S3DataType , then SageMaker will split the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of the file holding the content of the S3 object.

              • If you choose S3Prefix as the value of S3DataType , then for each S3 object under the key name pefix referenced by S3Uri , SageMaker will trim its key by the prefix, and use the remainder as the path (relative to /opt/ml/model ) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object.

              • Do not use any of the following as file names or directory names:

                • An empty or blank string

                • A string which contains null bytes

                • A string longer than 255 bytes

                • A single dot (. )

                • A double dot (.. )

              • Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the value of S3DataType , then it will result in name clash between /opt/ml/model/weights (a regular file) and /opt/ml/model/weights/ (a directory).

              • Do not organize the model artifacts in S3 console using folders . When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.

            • ModelAccessConfig (dict) --

              Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig . You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.

              • AcceptEula (boolean) -- [REQUIRED]

                Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.

        • 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 .

        • AdditionalS3DataSource (dict) --

          The additional data source that is used during inference in the Docker container for your model package.

          • S3DataType (string) -- [REQUIRED]

            The data type of the additional data source that you specify for use in inference or training.

          • S3Uri (string) -- [REQUIRED]

            The uniform resource identifier (URI) used to identify an additional data source used in inference or training.

          • CompressionType (string) --

            The type of compression used for an additional data source used in inference or training. Specify None if your additional data source is not compressed.

    • SupportedTransformInstanceTypes (list) --

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

      • (string) --

    • SupportedRealtimeInferenceInstanceTypes (list) --

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

      • (string) --

    • SupportedContentTypes (list) --

      The supported MIME types for the input data.

      • (string) --

    • SupportedResponseMIMETypes (list) --

      The supported MIME types for the output data.

      • (string) --

type SkipModelValidation

string

param SkipModelValidation

Indicates if you want to skip model validation.

type SourceUri

string

param SourceUri

The URI of the source for the model package. If you want to clone a model package, set it to the model package Amazon Resource Name (ARN). If you want to register a model, set it to the model ARN.

rtype

dict

returns

Response Syntax

{
    'ModelPackageArn': 'string'
}

Response Structure

  • (dict) --

    • ModelPackageArn (string) --

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

DescribeAlgorithm (updated) Link ¶
Changes (response)
{'InferenceSpecification': {'Containers': {'ModelDataSource': {'S3DataSource': {'CompressionType': 'None '
                                                                                                   '| '
                                                                                                   'Gzip',
                                                                                'ModelAccessConfig': {'AcceptEula': 'boolean'},
                                                                                'S3DataType': 'S3Prefix '
                                                                                              '| '
                                                                                              'S3Object',
                                                                                'S3Uri': 'string'}}}}}

Returns a description of the specified algorithm that is in your account.

See also: AWS API Documentation

Request Syntax

client.describe_algorithm(
    AlgorithmName='string'
)
type AlgorithmName

string

param AlgorithmName

[REQUIRED]

The name of the algorithm to describe.

rtype

dict

returns

Response Syntax

{
    'AlgorithmName': 'string',
    'AlgorithmArn': 'string',
    'AlgorithmDescription': 'string',
    'CreationTime': datetime(2015, 1, 1),
    'TrainingSpecification': {
        'TrainingImage': 'string',
        'TrainingImageDigest': 'string',
        'SupportedHyperParameters': [
            {
                'Name': 'string',
                'Description': 'string',
                'Type': 'Integer'|'Continuous'|'Categorical'|'FreeText',
                'Range': {
                    'IntegerParameterRangeSpecification': {
                        'MinValue': 'string',
                        'MaxValue': 'string'
                    },
                    'ContinuousParameterRangeSpecification': {
                        'MinValue': 'string',
                        'MaxValue': 'string'
                    },
                    'CategoricalParameterRangeSpecification': {
                        'Values': [
                            'string',
                        ]
                    }
                },
                'IsTunable': True|False,
                'IsRequired': True|False,
                'DefaultValue': 'string'
            },
        ],
        'SupportedTrainingInstanceTypes': [
            'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'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'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.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'
            },
        ],
        'AdditionalS3DataSource': {
            'S3DataType': 'S3Object'|'S3Prefix',
            'S3Uri': 'string',
            'CompressionType': 'None'|'Gzip'
        }
    },
    'InferenceSpecification': {
        'Containers': [
            {
                'ContainerHostname': 'string',
                'Image': 'string',
                'ImageDigest': 'string',
                'ModelDataUrl': 'string',
                'ModelDataSource': {
                    'S3DataSource': {
                        'S3Uri': 'string',
                        'S3DataType': 'S3Prefix'|'S3Object',
                        'CompressionType': 'None'|'Gzip',
                        'ModelAccessConfig': {
                            'AcceptEula': True|False
                        }
                    }
                },
                'ProductId': 'string',
                'Environment': {
                    'string': 'string'
                },
                'ModelInput': {
                    'DataInputConfig': 'string'
                },
                'Framework': 'string',
                'FrameworkVersion': 'string',
                'NearestModelName': 'string',
                'AdditionalS3DataSource': {
                    'S3DataType': 'S3Object'|'S3Prefix',
                    'S3Uri': 'string',
                    'CompressionType': 'None'|'Gzip'
                }
            },
        ],
        '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.dl1.24xlarge'|'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'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge',
        ],
        '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',
                        'CompressionType': 'GZIP'|'NONE'
                    },
                    '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.p4de.24xlarge'|'ml.p5.48xlarge'|'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'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
                        'InstanceCount': 123,
                        'VolumeSizeInGB': 123,
                        'VolumeKmsKeyId': 'string',
                        'KeepAlivePeriodInSeconds': 123,
                        '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.p4de.24xlarge'|'ml.p5.48xlarge'|'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'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
                                'InstanceCount': 123,
                                'InstanceGroupName': 'string'
                            },
                        ]
                    },
                    'StoppingCondition': {
                        'MaxRuntimeInSeconds': 123,
                        'MaxWaitTimeInSeconds': 123,
                        'MaxPendingTimeInSeconds': 123
                    }
                },
                'TransformJobDefinition': {
                    'MaxConcurrentTransforms': 123,
                    'MaxPayloadInMB': 123,
                    'BatchStrategy': 'MultiRecord'|'SingleRecord',
                    'Environment': {
                        'string': 'string'
                    },
                    'TransformInput': {
                        'DataSource': {
                            'S3DataSource': {
                                'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
                                'S3Uri': 'string'
                            }
                        },
                        'ContentType': 'string',
                        'CompressionType': 'None'|'Gzip',
                        'SplitType': 'None'|'Line'|'RecordIO'|'TFRecord'
                    },
                    'TransformOutput': {
                        'S3OutputPath': 'string',
                        'Accept': 'string',
                        'AssembleWith': 'None'|'Line',
                        'KmsKeyId': 'string'
                    },
                    'TransformResources': {
                        'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
                        'InstanceCount': 123,
                        'VolumeKmsKeyId': 'string'
                    }
                }
            },
        ]
    },
    'AlgorithmStatus': 'Pending'|'InProgress'|'Completed'|'Failed'|'Deleting',
    'AlgorithmStatusDetails': {
        'ValidationStatuses': [
            {
                'Name': 'string',
                'Status': 'NotStarted'|'InProgress'|'Completed'|'Failed',
                'FailureReason': 'string'
            },
        ],
        'ImageScanStatuses': [
            {
                'Name': 'string',
                'Status': 'NotStarted'|'InProgress'|'Completed'|'Failed',
                'FailureReason': 'string'
            },
        ]
    },
    'ProductId': 'string',
    'CertifyForMarketplace': True|False
}

Response Structure

  • (dict) --

    • AlgorithmName (string) --

      The name of the algorithm being described.

    • AlgorithmArn (string) --

      The Amazon Resource Name (ARN) of the algorithm.

    • AlgorithmDescription (string) --

      A brief summary about the algorithm.

    • CreationTime (datetime) --

      A timestamp specifying when the algorithm was created.

    • TrainingSpecification (dict) --

      Details about training jobs run by this algorithm.

      • TrainingImage (string) --

        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) --

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

          • Description (string) --

            A brief description of the hyperparameter.

          • Type (string) --

            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) --

                The minimum integer value allowed.

              • MaxValue (string) --

                The maximum integer value allowed.

            • ContinuousParameterRangeSpecification (dict) --

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

              • MinValue (string) --

                The minimum floating-point value allowed.

              • MaxValue (string) --

                The maximum floating-point value allowed.

            • CategoricalParameterRangeSpecification (dict) --

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

              • Values (list) --

                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) --

        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 . You can view these logs to understand how your training job performs and check for any errors encountered during training. SageMaker hyperparameter tuning captures all defined metrics. Specify one of the defined metrics to use as an objective metric using the TuningObjective parameter in the HyperParameterTrainingJobDefinition API to evaluate job performance during hyperparameter tuning.

          • Name (string) --

            The name of the metric.

          • Regex (string) --

            A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables .

      • TrainingChannels (list) --

        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) --

            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) --

            The supported MIME types for the data.

            • (string) --

          • SupportedCompressionTypes (list) --

            The allowed compression types, if data compression is used.

            • (string) --

          • SupportedInputModes (list) --

            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. If you want to define a custom objective metric, see Define metrics and environment variables .

          • Type (string) --

            Whether to minimize or maximize the objective metric.

          • MetricName (string) --

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

      • AdditionalS3DataSource (dict) --

        The additional data source used during the training job.

        • S3DataType (string) --

          The data type of the additional data source that you specify for use in inference or training.

        • S3Uri (string) --

          The uniform resource identifier (URI) used to identify an additional data source used in inference or training.

        • CompressionType (string) --

          The type of compression used for an additional data source used in inference or training. Specify None if your additional data source is not compressed.

    • InferenceSpecification (dict) --

      Details about inference jobs that the algorithm runs.

      • 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.

          • ModelDataSource (dict) --

            Specifies the location of ML model data to deploy during endpoint creation.

            • S3DataSource (dict) --

              Specifies the S3 location of ML model data to deploy.

              • S3Uri (string) --

                Specifies the S3 path of ML model data to deploy.

              • S3DataType (string) --

                Specifies the type of ML model data to deploy.

                If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by S3Uri always ends with a forward slash (/).

                If you choose S3Object , S3Uri identifies an object that is the ML model data to deploy.

              • CompressionType (string) --

                Specifies how the ML model data is prepared.

                If you choose Gzip and choose S3Object as the value of S3DataType , S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.

                If you choose None and chooose S3Object as the value of S3DataType , S3Uri identifies an object that represents an uncompressed ML model to deploy.

                If you choose None and choose S3Prefix as the value of S3DataType , S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.

                If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:

                • If you choose S3Object as the value of S3DataType , then SageMaker will split the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of the file holding the content of the S3 object.

                • If you choose S3Prefix as the value of S3DataType , then for each S3 object under the key name pefix referenced by S3Uri , SageMaker will trim its key by the prefix, and use the remainder as the path (relative to /opt/ml/model ) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object.

                • Do not use any of the following as file names or directory names:

                  • An empty or blank string

                  • A string which contains null bytes

                  • A string longer than 255 bytes

                  • A single dot (. )

                  • A double dot (.. )

                • Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the value of S3DataType , then it will result in name clash between /opt/ml/model/weights (a regular file) and /opt/ml/model/weights/ (a directory).

                • Do not organize the model artifacts in S3 console using folders . When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.

              • ModelAccessConfig (dict) --

                Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig . You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.

                • AcceptEula (boolean) --

                  Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.

          • 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 .

          • AdditionalS3DataSource (dict) --

            The additional data source that is used during inference in the Docker container for your model package.

            • S3DataType (string) --

              The data type of the additional data source that you specify for use in inference or training.

            • S3Uri (string) --

              The uniform resource identifier (URI) used to identify an additional data source used in inference or training.

            • CompressionType (string) --

              The type of compression used for an additional data source used in inference or training. Specify None if your additional data source is not compressed.

      • 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) --

    • ValidationSpecification (dict) --

      Details about configurations for one or more training jobs that SageMaker runs to test the algorithm.

      • ValidationRole (string) --

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

      • ValidationProfiles (list) --

        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) --

            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) --

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

            • TrainingInputMode (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.

            • HyperParameters (dict) --

              The hyperparameters used for the training job.

              • (string) --

                • (string) --

            • InputDataConfig (list) --

              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) --

                  The name of the channel.

                • DataSource (dict) --

                  The location of the channel data.

                  • S3DataSource (dict) --

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

                    • S3DataType (string) --

                      If you choose S3Prefix , S3Uri identifies a key name prefix. 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) --

                      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.

                      Your input bucket must be located in same Amazon Web Services region as your training job.

                    • 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) --

                      The file system id.

                    • FileSystemAccessMode (string) --

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

                    • FileSystemType (string) --

                      The file system type.

                    • DirectoryPath (string) --

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

                • ContentType (string) --

                  The MIME type of the data.

                • CompressionType (string) --

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

                • RecordWrapperType (string) --

                  Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, 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) --

                    Determines the shuffling order in ShuffleConfig value.

            • OutputDataConfig (dict) --

              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. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide . If the output data is stored in Amazon S3 Express One Zone, it is encrypted with server-side encryption with Amazon S3 managed keys (SSE-S3). KMS key is not supported for Amazon S3 Express One Zone

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

              • S3OutputPath (string) --

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

              • CompressionType (string) --

                The model output compression type. Select None to output an uncompressed model, recommended for large model outputs. Defaults to gzip.

            • ResourceConfig (dict) --

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

              • InstanceType (string) --

                The ML compute instance type.

                Note

                SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022.

                Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (ml.p4de.24xlarge ) to reduce model training time. The ml.p4de.24xlarge instances are available in the following Amazon Web Services Regions.

                • US East (N. Virginia) (us-east-1)

                • US West (Oregon) (us-west-2)

                To request quota limit increase and start using P4de instances, contact the SageMaker Training service team through your account team.

              • InstanceCount (integer) --

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

              • VolumeSizeInGB (integer) --

                The size of the ML storage volume that you want to provision.

                ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.

                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"

              • KeepAlivePeriodInSeconds (integer) --

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

              • 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) --

                    Specifies the instance type of the instance group.

                  • InstanceCount (integer) --

                    Specifies the number of instances of the instance group.

                  • InstanceGroupName (string) --

                    Specifies the name of the instance group.

            • StoppingCondition (dict) --

              Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, 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.

              • MaxPendingTimeInSeconds (integer) --

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

          • 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) --

              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. 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

    • AlgorithmStatus (string) --

      The current status of the algorithm.

    • AlgorithmStatusDetails (dict) --

      Details about the current status of the algorithm.

      • ValidationStatuses (list) --

        The status of algorithm validation.

        • (dict) --

          Represents the overall status of an algorithm.

          • Name (string) --

            The name of the algorithm 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 algorithm's Docker image container.

        • (dict) --

          Represents the overall status of an algorithm.

          • Name (string) --

            The name of the algorithm 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.

    • ProductId (string) --

      The product identifier of the algorithm.

    • CertifyForMarketplace (boolean) --

      Whether the algorithm is certified to be listed in Amazon Web Services Marketplace.

DescribeModelPackage (updated) Link ¶
Changes (response)
{'AdditionalInferenceSpecifications': {'Containers': {'ModelDataSource': {'S3DataSource': {'CompressionType': 'None '
                                                                                                              '| '
                                                                                                              'Gzip',
                                                                                           'ModelAccessConfig': {'AcceptEula': 'boolean'},
                                                                                           'S3DataType': 'S3Prefix '
                                                                                                         '| '
                                                                                                         'S3Object',
                                                                                           'S3Uri': 'string'}}}},
 'InferenceSpecification': {'Containers': {'ModelDataSource': {'S3DataSource': {'CompressionType': 'None '
                                                                                                   '| '
                                                                                                   'Gzip',
                                                                                'ModelAccessConfig': {'AcceptEula': 'boolean'},
                                                                                'S3DataType': 'S3Prefix '
                                                                                              '| '
                                                                                              'S3Object',
                                                                                'S3Uri': 'string'}}}},
 'SourceAlgorithmSpecification': {'SourceAlgorithms': {'ModelDataSource': {'S3DataSource': {'CompressionType': 'None '
                                                                                                               '| '
                                                                                                               'Gzip',
                                                                                            'ModelAccessConfig': {'AcceptEula': 'boolean'},
                                                                                            'S3DataType': 'S3Prefix '
                                                                                                          '| '
                                                                                                          'S3Object',
                                                                                            'S3Uri': 'string'}}}},
 'SourceUri': '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',
                'ModelDataSource': {
                    'S3DataSource': {
                        'S3Uri': 'string',
                        'S3DataType': 'S3Prefix'|'S3Object',
                        'CompressionType': 'None'|'Gzip',
                        'ModelAccessConfig': {
                            'AcceptEula': True|False
                        }
                    }
                },
                'ProductId': 'string',
                'Environment': {
                    'string': 'string'
                },
                'ModelInput': {
                    'DataInputConfig': 'string'
                },
                'Framework': 'string',
                'FrameworkVersion': 'string',
                'NearestModelName': 'string',
                'AdditionalS3DataSource': {
                    'S3DataType': 'S3Object'|'S3Prefix',
                    'S3Uri': 'string',
                    'CompressionType': 'None'|'Gzip'
                }
            },
        ],
        '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.dl1.24xlarge'|'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'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge',
        ],
        'SupportedContentTypes': [
            'string',
        ],
        'SupportedResponseMIMETypes': [
            'string',
        ]
    },
    'SourceAlgorithmSpecification': {
        'SourceAlgorithms': [
            {
                'ModelDataUrl': 'string',
                'ModelDataSource': {
                    'S3DataSource': {
                        'S3Uri': 'string',
                        'S3DataType': 'S3Prefix'|'S3Object',
                        'CompressionType': 'None'|'Gzip',
                        'ModelAccessConfig': {
                            'AcceptEula': True|False
                        }
                    }
                },
                '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',
        'IamIdentity': {
            'Arn': 'string',
            'PrincipalId': 'string',
            'SourceIdentity': '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'
            },
            'PreTrainingReport': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            },
            'PostTrainingReport': {
                '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',
        'IamIdentity': {
            'Arn': 'string',
            'PrincipalId': 'string',
            'SourceIdentity': 'string'
        }
    },
    'ApprovalDescription': 'string',
    'Domain': 'string',
    'Task': 'string',
    'SamplePayloadUrl': 'string',
    'CustomerMetadataProperties': {
        'string': 'string'
    },
    'DriftCheckBaselines': {
        'Bias': {
            'ConfigFile': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            },
            'PreTrainingConstraints': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            },
            'PostTrainingConstraints': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            }
        },
        'Explainability': {
            'Constraints': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            },
            'ConfigFile': {
                'ContentType': 'string',
                'ContentDigest': 'string',
                'S3Uri': 'string'
            }
        },
        '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'
            }
        }
    },
    'AdditionalInferenceSpecifications': [
        {
            'Name': 'string',
            'Description': 'string',
            'Containers': [
                {
                    'ContainerHostname': 'string',
                    'Image': 'string',
                    'ImageDigest': 'string',
                    'ModelDataUrl': 'string',
                    'ModelDataSource': {
                        'S3DataSource': {
                            'S3Uri': 'string',
                            'S3DataType': 'S3Prefix'|'S3Object',
                            'CompressionType': 'None'|'Gzip',
                            'ModelAccessConfig': {
                                'AcceptEula': True|False
                            }
                        }
                    },
                    'ProductId': 'string',
                    'Environment': {
                        'string': 'string'
                    },
                    'ModelInput': {
                        'DataInputConfig': 'string'
                    },
                    'Framework': 'string',
                    'FrameworkVersion': 'string',
                    'NearestModelName': 'string',
                    'AdditionalS3DataSource': {
                        'S3DataType': 'S3Object'|'S3Prefix',
                        'S3Uri': 'string',
                        'CompressionType': 'None'|'Gzip'
                    }
                },
            ],
            '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.dl1.24xlarge'|'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'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge',
            ],
            'SupportedContentTypes': [
                'string',
            ],
            'SupportedResponseMIMETypes': [
                'string',
            ]
        },
    ],
    'SkipModelValidation': 'All'|'None',
    'SourceUri': '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 you can 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 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.

          • ModelDataSource (dict) --

            Specifies the location of ML model data to deploy during endpoint creation.

            • S3DataSource (dict) --

              Specifies the S3 location of ML model data to deploy.

              • S3Uri (string) --

                Specifies the S3 path of ML model data to deploy.

              • S3DataType (string) --

                Specifies the type of ML model data to deploy.

                If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by S3Uri always ends with a forward slash (/).

                If you choose S3Object , S3Uri identifies an object that is the ML model data to deploy.

              • CompressionType (string) --

                Specifies how the ML model data is prepared.

                If you choose Gzip and choose S3Object as the value of S3DataType , S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.

                If you choose None and chooose S3Object as the value of S3DataType , S3Uri identifies an object that represents an uncompressed ML model to deploy.

                If you choose None and choose S3Prefix as the value of S3DataType , S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.

                If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:

                • If you choose S3Object as the value of S3DataType , then SageMaker will split the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of the file holding the content of the S3 object.

                • If you choose S3Prefix as the value of S3DataType , then for each S3 object under the key name pefix referenced by S3Uri , SageMaker will trim its key by the prefix, and use the remainder as the path (relative to /opt/ml/model ) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object.

                • Do not use any of the following as file names or directory names:

                  • An empty or blank string

                  • A string which contains null bytes

                  • A string longer than 255 bytes

                  • A single dot (. )

                  • A double dot (.. )

                • Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the value of S3DataType , then it will result in name clash between /opt/ml/model/weights (a regular file) and /opt/ml/model/weights/ (a directory).

                • Do not organize the model artifacts in S3 console using folders . When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.

              • ModelAccessConfig (dict) --

                Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig . You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.

                • AcceptEula (boolean) --

                  Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.

          • 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 .

          • AdditionalS3DataSource (dict) --

            The additional data source that is used during inference in the Docker container for your model package.

            • S3DataType (string) --

              The data type of the additional data source that you specify for use in inference or training.

            • S3Uri (string) --

              The uniform resource identifier (URI) used to identify an additional data source used in inference or training.

            • CompressionType (string) --

              The type of compression used for an additional data source used in inference or training. Specify None if your additional data source is not compressed.

      • 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 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 Amazon Web Services region as the algorithm.

          • ModelDataSource (dict) --

            Specifies the location of ML model data to deploy during endpoint creation.

            • S3DataSource (dict) --

              Specifies the S3 location of ML model data to deploy.

              • S3Uri (string) --

                Specifies the S3 path of ML model data to deploy.

              • S3DataType (string) --

                Specifies the type of ML model data to deploy.

                If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by S3Uri always ends with a forward slash (/).

                If you choose S3Object , S3Uri identifies an object that is the ML model data to deploy.

              • CompressionType (string) --

                Specifies how the ML model data is prepared.

                If you choose Gzip and choose S3Object as the value of S3DataType , S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.

                If you choose None and chooose S3Object as the value of S3DataType , S3Uri identifies an object that represents an uncompressed ML model to deploy.

                If you choose None and choose S3Prefix as the value of S3DataType , S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.

                If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:

                • If you choose S3Object as the value of S3DataType , then SageMaker will split the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of the file holding the content of the S3 object.

                • If you choose S3Prefix as the value of S3DataType , then for each S3 object under the key name pefix referenced by S3Uri , SageMaker will trim its key by the prefix, and use the remainder as the path (relative to /opt/ml/model ) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object.

                • Do not use any of the following as file names or directory names:

                  • An empty or blank string

                  • A string which contains null bytes

                  • A string longer than 255 bytes

                  • A single dot (. )

                  • A double dot (.. )

                • Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the value of S3DataType , then it will result in name clash between /opt/ml/model/weights (a regular file) and /opt/ml/model/weights/ (a directory).

                • Do not organize the model artifacts in S3 console using folders . When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.

              • ModelAccessConfig (dict) --

                Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig . You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.

                • AcceptEula (boolean) --

                  Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.

          • 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 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 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. 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

    • 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, lineage group, project, or model card.

      • 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.

      • IamIdentity (dict) --

        The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.

        • Arn (string) --

          The Amazon Resource Name (ARN) of the IAM identity.

        • PrincipalId (string) --

          The ID of the principal that assumes the IAM identity.

        • SourceIdentity (string) --

          The person or application which assumes the IAM identity.

    • 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) --

            The metric source content type.

          • ContentDigest (string) --

            The hash key used for the metrics source.

          • S3Uri (string) --

            The S3 URI for the metrics source.

        • Constraints (dict) --

          Model quality constraints.

          • ContentType (string) --

            The metric source content type.

          • ContentDigest (string) --

            The hash key used for the metrics source.

          • S3Uri (string) --

            The S3 URI for the metrics source.

      • ModelDataQuality (dict) --

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

        • Statistics (dict) --

          Data quality statistics for a model.

          • ContentType (string) --

            The metric source content type.

          • ContentDigest (string) --

            The hash key used for the metrics source.

          • S3Uri (string) --

            The S3 URI for the metrics source.

        • Constraints (dict) --

          Data quality constraints for a model.

          • ContentType (string) --

            The metric source content type.

          • ContentDigest (string) --

            The hash key used for the metrics source.

          • S3Uri (string) --

            The S3 URI for the metrics source.

      • Bias (dict) --

        Metrics that measure bias in a model.

        • Report (dict) --

          The bias report for a model

          • ContentType (string) --

            The metric source content type.

          • ContentDigest (string) --

            The hash key used for the metrics source.

          • S3Uri (string) --

            The S3 URI for the metrics source.

        • PreTrainingReport (dict) --

          The pre-training bias report for a model.

          • ContentType (string) --

            The metric source content type.

          • ContentDigest (string) --

            The hash key used for the metrics source.

          • S3Uri (string) --

            The S3 URI for the metrics source.

        • PostTrainingReport (dict) --

          The post-training bias report for a model.

          • ContentType (string) --

            The metric source content type.

          • ContentDigest (string) --

            The hash key used for the metrics source.

          • S3Uri (string) --

            The S3 URI for the metrics source.

      • Explainability (dict) --

        Metrics that help explain a model.

        • Report (dict) --

          The explainability report for a model.

          • ContentType (string) --

            The metric source content type.

          • ContentDigest (string) --

            The hash key used for the metrics source.

          • S3Uri (string) --

            The S3 URI for the metrics source.

    • LastModifiedTime (datetime) --

      The last time that the model package was modified.

    • LastModifiedBy (dict) --

      Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.

      • 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.

      • IamIdentity (dict) --

        The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.

        • Arn (string) --

          The Amazon Resource Name (ARN) of the IAM identity.

        • PrincipalId (string) --

          The ID of the principal that assumes the IAM identity.

        • SourceIdentity (string) --

          The person or application which assumes the IAM identity.

    • ApprovalDescription (string) --

      A description provided for the model approval.

    • Domain (string) --

      The machine learning domain of the model package you specified. Common machine learning domains include computer vision and natural language processing.

    • Task (string) --

      The machine learning task you specified that your model package accomplishes. Common machine learning tasks include object detection and image classification.

    • SamplePayloadUrl (string) --

      The Amazon Simple Storage Service (Amazon S3) path where the sample payload are stored. This path points to a single gzip compressed tar archive (.tar.gz suffix).

    • CustomerMetadataProperties (dict) --

      The metadata properties associated with the model package versions.

      • (string) --

        • (string) --

    • DriftCheckBaselines (dict) --

      Represents the drift check baselines that can be used when the model monitor is set using the model package. For more information, see the topic on Drift Detection against Previous Baselines in SageMaker Pipelines in the Amazon SageMaker Developer Guide .

      • Bias (dict) --

        Represents the drift check bias baselines that can be used when the model monitor is set using the model package.

        • ConfigFile (dict) --

          The bias config file for a model.

          • ContentType (string) --

            The type of content stored in the file source.

          • ContentDigest (string) --

            The digest of the file source.

          • S3Uri (string) --

            The Amazon S3 URI for the file source.

        • PreTrainingConstraints (dict) --

          The pre-training constraints.

          • ContentType (string) --

            The metric source content type.

          • ContentDigest (string) --

            The hash key used for the metrics source.

          • S3Uri (string) --

            The S3 URI for the metrics source.

        • PostTrainingConstraints (dict) --

          The post-training constraints.

          • ContentType (string) --

            The metric source content type.

          • ContentDigest (string) --

            The hash key used for the metrics source.

          • S3Uri (string) --

            The S3 URI for the metrics source.

      • Explainability (dict) --

        Represents the drift check explainability baselines that can be used when the model monitor is set using the model package.

        • Constraints (dict) --

          The drift check explainability constraints.

          • ContentType (string) --

            The metric source content type.

          • ContentDigest (string) --

            The hash key used for the metrics source.

          • S3Uri (string) --

            The S3 URI for the metrics source.

        • ConfigFile (dict) --

          The explainability config file for the model.

          • ContentType (string) --

            The type of content stored in the file source.

          • ContentDigest (string) --

            The digest of the file source.

          • S3Uri (string) --

            The Amazon S3 URI for the file source.

      • ModelQuality (dict) --

        Represents the drift check model quality baselines that can be used when the model monitor is set using the model package.

        • Statistics (dict) --

          The drift check model quality statistics.

          • ContentType (string) --

            The metric source content type.

          • ContentDigest (string) --

            The hash key used for the metrics source.

          • S3Uri (string) --

            The S3 URI for the metrics source.

        • Constraints (dict) --

          The drift check model quality constraints.

          • ContentType (string) --

            The metric source content type.

          • ContentDigest (string) --

            The hash key used for the metrics source.

          • S3Uri (string) --

            The S3 URI for the metrics source.

      • ModelDataQuality (dict) --

        Represents the drift check model data quality baselines that can be used when the model monitor is set using the model package.

        • Statistics (dict) --

          The drift check model data quality statistics.

          • ContentType (string) --

            The metric source content type.

          • ContentDigest (string) --

            The hash key used for the metrics source.

          • S3Uri (string) --

            The S3 URI for the metrics source.

        • Constraints (dict) --

          The drift check model data quality constraints.

          • ContentType (string) --

            The metric source content type.

          • ContentDigest (string) --

            The hash key used for the metrics source.

          • S3Uri (string) --

            The S3 URI for the metrics source.

    • AdditionalInferenceSpecifications (list) --

      An array of additional Inference Specification objects. Each additional Inference Specification specifies artifacts based on this model package that can be used on inference endpoints. Generally used with SageMaker Neo to store the compiled artifacts.

      • (dict) --

        A structure of additional Inference Specification. Additional Inference Specification specifies details about inference jobs that can be run with models based on this model package

        • Name (string) --

          A unique name to identify the additional inference specification. The name must be unique within the list of your additional inference specifications for a particular model package.

        • Description (string) --

          A description of the additional Inference specification

        • 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.

            • ModelDataSource (dict) --

              Specifies the location of ML model data to deploy during endpoint creation.

              • S3DataSource (dict) --

                Specifies the S3 location of ML model data to deploy.

                • S3Uri (string) --

                  Specifies the S3 path of ML model data to deploy.

                • S3DataType (string) --

                  Specifies the type of ML model data to deploy.

                  If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by S3Uri always ends with a forward slash (/).

                  If you choose S3Object , S3Uri identifies an object that is the ML model data to deploy.

                • CompressionType (string) --

                  Specifies how the ML model data is prepared.

                  If you choose Gzip and choose S3Object as the value of S3DataType , S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.

                  If you choose None and chooose S3Object as the value of S3DataType , S3Uri identifies an object that represents an uncompressed ML model to deploy.

                  If you choose None and choose S3Prefix as the value of S3DataType , S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.

                  If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:

                  • If you choose S3Object as the value of S3DataType , then SageMaker will split the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of the file holding the content of the S3 object.

                  • If you choose S3Prefix as the value of S3DataType , then for each S3 object under the key name pefix referenced by S3Uri , SageMaker will trim its key by the prefix, and use the remainder as the path (relative to /opt/ml/model ) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object.

                  • Do not use any of the following as file names or directory names:

                    • An empty or blank string

                    • A string which contains null bytes

                    • A string longer than 255 bytes

                    • A single dot (. )

                    • A double dot (.. )

                  • Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the value of S3DataType , then it will result in name clash between /opt/ml/model/weights (a regular file) and /opt/ml/model/weights/ (a directory).

                  • Do not organize the model artifacts in S3 console using folders . When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.

                • ModelAccessConfig (dict) --

                  Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig . You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.

                  • AcceptEula (boolean) --

                    Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.

            • 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 .

            • AdditionalS3DataSource (dict) --

              The additional data source that is used during inference in the Docker container for your model package.

              • S3DataType (string) --

                The data type of the additional data source that you specify for use in inference or training.

              • S3Uri (string) --

                The uniform resource identifier (URI) used to identify an additional data source used in inference or training.

              • CompressionType (string) --

                The type of compression used for an additional data source used in inference or training. Specify None if your additional data source is not compressed.

        • SupportedTransformInstanceTypes (list) --

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

          • (string) --

        • SupportedRealtimeInferenceInstanceTypes (list) --

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

          • (string) --

        • SupportedContentTypes (list) --

          The supported MIME types for the input data.

          • (string) --

        • SupportedResponseMIMETypes (list) --

          The supported MIME types for the output data.

          • (string) --

    • SkipModelValidation (string) --

      Indicates if you want to skip model validation.

    • SourceUri (string) --

      The URI of the source for the model package.