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sagemaker_create_model_package

Creates a model package that you can use to create Amazon SageMaker models or list on AWS Marketplace, or a versioned model that is part of a model group


Description

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

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

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.

Usage

sagemaker_create_model_package(ModelPackageName, ModelPackageGroupName,
  ModelPackageDescription, InferenceSpecification,
  ValidationSpecification, SourceAlgorithmSpecification,
  CertifyForMarketplace, Tags, ModelApprovalStatus, MetadataProperties,
  ModelMetrics, ClientToken)

Arguments

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.

ModelPackageGroupName

The name of the model group that this model version belongs to.

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

ModelPackageDescription

A description of the model package.

InferenceSpecification

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

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

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

  • The input and output content formats that the model package supports for inference.

ValidationSpecification

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

SourceAlgorithmSpecification

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

CertifyForMarketplace

Whether to certify the model package for listing on AWS Marketplace.

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

Tags

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

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.

MetadataProperties
ModelMetrics

A structure that contains model metrics reports.

ClientToken

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

Value

A list with the following syntax:

list(
  ModelPackageArn = "string"
)

Request syntax

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

paws.machine.learning

Amazon Web Services Machine Learning Services

v0.1.11
Apache License (>= 2.0)
Authors
David Kretch [aut, cre], Adam Banker [aut], Amazon.com, Inc. [cph]
Initial release

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