createModelPackage method

Future<CreateModelPackageOutput> createModelPackage({ bool? certifyForMarketplace, String? clientToken, InferenceSpecification? inferenceSpecification, MetadataProperties? metadataProperties, ModelApprovalStatus? modelApprovalStatus, ModelMetrics? modelMetrics, String? modelPackageDescription, String? modelPackageGroupName, String? modelPackageName, SourceAlgorithmSpecification? sourceAlgorithmSpecification, List<Tag>? tags, ModelPackageValidationSpecification? validationSpecification, })

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.

• 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.

May throw ConflictException. May throw ResourceLimitExceeded.

Parameter 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.

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

Parameter 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.

Parameter 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.

Parameter modelMetrics : A structure that contains model metrics reports.

Parameter modelPackageDescription : A description of the model package.

Parameter 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.

Parameter 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.

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

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

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

Implementation

Future<CreateModelPackageOutput> createModelPackage({
  bool? certifyForMarketplace,
  String? clientToken,
  InferenceSpecification? inferenceSpecification,
  MetadataProperties? metadataProperties,
  ModelApprovalStatus? modelApprovalStatus,
  ModelMetrics? modelMetrics,
  String? modelPackageDescription,
  String? modelPackageGroupName,
  String? modelPackageName,
  SourceAlgorithmSpecification? sourceAlgorithmSpecification,
  List<Tag>? tags,
  ModelPackageValidationSpecification? validationSpecification,
}) async {
  _s.validateStringLength(
    'clientToken',
    clientToken,
    1,
    36,
  );
  _s.validateStringLength(
    'modelPackageDescription',
    modelPackageDescription,
    0,
    1024,
  );
  _s.validateStringLength(
    'modelPackageGroupName',
    modelPackageGroupName,
    1,
    63,
  );
  _s.validateStringLength(
    'modelPackageName',
    modelPackageName,
    1,
    63,
  );
  final headers = <String, String>{
    'Content-Type': 'application/x-amz-json-1.1',
    'X-Amz-Target': 'SageMaker.CreateModelPackage'
  };
  final jsonResponse = await _protocol.send(
    method: 'POST',
    requestUri: '/',
    exceptionFnMap: _exceptionFns,
    // TODO queryParams
    headers: headers,
    payload: {
      if (certifyForMarketplace != null)
        'CertifyForMarketplace': certifyForMarketplace,
      'ClientToken': clientToken ?? _s.generateIdempotencyToken(),
      if (inferenceSpecification != null)
        'InferenceSpecification': inferenceSpecification,
      if (metadataProperties != null)
        'MetadataProperties': metadataProperties,
      if (modelApprovalStatus != null)
        'ModelApprovalStatus': modelApprovalStatus.toValue(),
      if (modelMetrics != null) 'ModelMetrics': modelMetrics,
      if (modelPackageDescription != null)
        'ModelPackageDescription': modelPackageDescription,
      if (modelPackageGroupName != null)
        'ModelPackageGroupName': modelPackageGroupName,
      if (modelPackageName != null) 'ModelPackageName': modelPackageName,
      if (sourceAlgorithmSpecification != null)
        'SourceAlgorithmSpecification': sourceAlgorithmSpecification,
      if (tags != null) 'Tags': tags,
      if (validationSpecification != null)
        'ValidationSpecification': validationSpecification,
    },
  );

  return CreateModelPackageOutput.fromJson(jsonResponse.body);
}