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[ aws . sagemaker ]

create-model

Description

Creates a model in Amazon SageMaker. In the request, you name the model and describe one or more containers. For each container, you specify the docker image containing inference code, artifacts (from prior training), and custom environment map that the inference code uses when you deploy the model into production.

Use this API to create a model only if you want to use Amazon SageMaker hosting services. To host your model, you create an endpoint configuration with the create-endpoint-config API, and then create an endpoint with the create-endpoint API.

Amazon SageMaker then deploys all of the containers that you defined for the model in the hosting environment.

In the create-model 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. 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.

See also: AWS API Documentation

See 'aws help' for descriptions of global parameters.

Synopsis

  create-model
--model-name <value>
--primary-container <value>
--execution-role-arn <value>
[--tags <value>]
[--cli-input-json <value>]
[--generate-cli-skeleton <value>]

Options

--model-name (string)

The name of the new model.

--primary-container (structure)

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 into production.

Shorthand Syntax:

ContainerHostname=string,Image=string,ModelDataUrl=string,Environment={KeyName1=string,KeyName2=string}

JSON Syntax:

{
  "ContainerHostname": "string",
  "Image": "string",
  "ModelDataUrl": "string",
  "Environment": {"string": "string"
    ...}
}

--execution-role-arn (string)

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. Deploying on ML compute instances is part of model hosting. For more information, see Amazon SageMaker Roles .

--tags (list)

An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide .

Shorthand Syntax:

Key=string,Value=string ...

JSON Syntax:

[
  {
    "Key": "string",
    "Value": "string"
  }
  ...
]

--cli-input-json (string) Performs service operation based on the JSON string provided. The JSON string follows the format provided by --generate-cli-skeleton. If other arguments are provided on the command line, the CLI values will override the JSON-provided values.

--generate-cli-skeleton (string) Prints a JSON skeleton to standard output without sending an API request. If provided with no value or the value input, prints a sample input JSON that can be used as an argument for --cli-input-json. If provided with the value output, it validates the command inputs and returns a sample output JSON for that command.

See 'aws help' for descriptions of global parameters.

Output

ModelArn -> (string)

The ARN of the model created in Amazon SageMaker.