createModel method

Future<CreateModelOutput> createModel({ required String executionRoleArn, required String modelName, List<ContainerDefinition>? containers, bool? enableNetworkIsolation, ContainerDefinition? primaryContainer, List<Tag>? tags, VpcConfig? vpcConfig, })

Creates a model in Amazon SageMaker. In the request, you name the model and describe a primary container. For the primary container, you specify the Docker image that contains inference code, artifacts (from prior training), and a custom environment map that the inference code uses when you deploy the model for predictions.

Use this API to create a model if you want to use Amazon SageMaker hosting services or run a batch transform job.

To host your model, you create an endpoint configuration with the CreateEndpointConfig API, and then create an endpoint with the CreateEndpoint API. Amazon SageMaker then deploys all of the containers that you defined for the model in the hosting environment.

For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (AWS SDK for Python (Boto 3)).

To run a batch transform using your model, you start a job with the CreateTransformJob API. Amazon SageMaker uses your model and your dataset to get inferences which are then saved to a specified S3 location.

In the CreateModel request, you must define a container with the PrimaryContainer parameter.

In the request, you also provide an IAM role that Amazon SageMaker can assume to access model artifacts and docker image for deployment on ML compute hosting instances or for batch transform jobs. In addition, you also use the IAM role to manage permissions the inference code needs. For example, if the inference code access any other AWS resources, you grant necessary permissions via this role.

May throw ResourceLimitExceeded.

Parameter executionRoleArn : The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker can assume to access model artifacts and docker image for deployment on ML compute instances or for batch transform jobs. Deploying on ML compute instances is part of model hosting. For more information, see Amazon SageMaker Roles.

Parameter modelName : The name of the new model.

Parameter containers : Specifies the containers in the inference pipeline.

Parameter enableNetworkIsolation : Isolates the model container. No inbound or outbound network calls can be made to or from the model container.

Parameter primaryContainer : The location of the primary docker image containing inference code, associated artifacts, and custom environment map that the inference code uses when the model is deployed for predictions.

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

Parameter vpcConfig : A VpcConfig object that specifies the VPC that you want your model to connect to. Control access to and from your model container by configuring the VPC. VpcConfig is used in hosting services and in batch transform. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Data in Batch Transform Jobs by Using an Amazon Virtual Private Cloud.

Implementation

Future<CreateModelOutput> createModel({
  required String executionRoleArn,
  required String modelName,
  List<ContainerDefinition>? containers,
  bool? enableNetworkIsolation,
  ContainerDefinition? primaryContainer,
  List<Tag>? tags,
  VpcConfig? vpcConfig,
}) async {
  ArgumentError.checkNotNull(executionRoleArn, 'executionRoleArn');
  _s.validateStringLength(
    'executionRoleArn',
    executionRoleArn,
    20,
    2048,
    isRequired: true,
  );
  ArgumentError.checkNotNull(modelName, 'modelName');
  _s.validateStringLength(
    'modelName',
    modelName,
    0,
    63,
    isRequired: true,
  );
  final headers = <String, String>{
    'Content-Type': 'application/x-amz-json-1.1',
    'X-Amz-Target': 'SageMaker.CreateModel'
  };
  final jsonResponse = await _protocol.send(
    method: 'POST',
    requestUri: '/',
    exceptionFnMap: _exceptionFns,
    // TODO queryParams
    headers: headers,
    payload: {
      'ExecutionRoleArn': executionRoleArn,
      'ModelName': modelName,
      if (containers != null) 'Containers': containers,
      if (enableNetworkIsolation != null)
        'EnableNetworkIsolation': enableNetworkIsolation,
      if (primaryContainer != null) 'PrimaryContainer': primaryContainer,
      if (tags != null) 'Tags': tags,
      if (vpcConfig != null) 'VpcConfig': vpcConfig,
    },
  );

  return CreateModelOutput.fromJson(jsonResponse.body);
}