Amazon SageMaker
Developer Guide

CreateModel

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 containing inference code, artifacts (from prior training), and 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.

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.

Request Syntax

{ "ExecutionRoleArn": "string", "ModelName": "string", "PrimaryContainer": { "ContainerHostname": "string", "Environment": { "string" : "string" }, "Image": "string", "ModelDataUrl": "string" }, "Tags": [ { "Key": "string", "Value": "string" } ], "VpcConfig": { "SecurityGroupIds": [ "string" ], "Subnets": [ "string" ] } }

Request Parameters

For information about the parameters that are common to all actions, see Common Parameters.

The request accepts the following data in JSON format.

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.

Note

To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission.

Type: String

Length Constraints: Minimum length of 20. Maximum length of 2048.

Pattern: ^arn:aws[a-z\-]*:iam::\d{12}:role/?[a-zA-Z_0-9+=,.@\-_/]+$

Required: Yes

ModelName

The name of the new model.

Type: String

Length Constraints: Maximum length of 63.

Pattern: ^[a-zA-Z0-9](-*[a-zA-Z0-9])*

Required: Yes

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.

Type: ContainerDefinition object

Required: Yes

Tags

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

Type: Array of Tag objects

Array Members: Minimum number of 0 items. Maximum number of 50 items.

Required: No

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 currently used in hosting services but not in batch transform. For more information, see Protect Models by Using an Amazon Virtual Private Cloud.

Type: VpcConfig object

Required: No

Response Syntax

{ "ModelArn": "string" }

Response Elements

If the action is successful, the service sends back an HTTP 200 response.

The following data is returned in JSON format by the service.

ModelArn

The ARN of the model created in Amazon SageMaker.

Type: String

Length Constraints: Minimum length of 20. Maximum length of 2048.

Errors

For information about the errors that are common to all actions, see Common Errors.

ResourceLimitExceeded

You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.

HTTP Status Code: 400

See Also

For more information about using this API in one of the language-specific AWS SDKs, see the following: