Request Syntax Request Parameters Response Syntax Response Elements Errors See Also

CreateModel

Creates a model in 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 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. 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. SageMaker uses your model and your dataset to get inferences which are then saved to a specified S3 location.

In the request, you also provide an IAM role that 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


{
   "Containers": [ 
      { 
         "AdditionalModelDataSources": [ 
            { 
               "ChannelName": "string",
               "S3DataSource": { 
                  "CompressionType": "string",
                  "HubAccessConfig": { 
                     "HubContentArn": "string"
                  },
                  "ManifestS3Uri": "string",
                  "ModelAccessConfig": { 
                     "AcceptEula": boolean
                  },
                  "S3DataType": "string",
                  "S3Uri": "string"
               }
            }
         ],
         "ContainerHostname": "string",
         "Environment": { 
            "string" : "string" 
         },
         "Image": "string",
         "ImageConfig": { 
            "RepositoryAccessMode": "string",
            "RepositoryAuthConfig": { 
               "RepositoryCredentialsProviderArn": "string"
            }
         },
         "InferenceSpecificationName": "string",
         "Mode": "string",
         "ModelDataSource": { 
            "S3DataSource": { 
               "CompressionType": "string",
               "HubAccessConfig": { 
                  "HubContentArn": "string"
               },
               "ManifestS3Uri": "string",
               "ModelAccessConfig": { 
                  "AcceptEula": boolean
               },
               "S3DataType": "string",
               "S3Uri": "string"
            }
         },
         "ModelDataUrl": "string",
         "ModelPackageName": "string",
         "MultiModelConfig": { 
            "ModelCacheSetting": "string"
         }
      }
   ],
   "EnableNetworkIsolation": boolean,
   "ExecutionRoleArn": "string",
   "InferenceExecutionConfig": { 
      "Mode": "string"
   },
   "ModelName": "string",
   "PrimaryContainer": { 
      "AdditionalModelDataSources": [ 
         { 
            "ChannelName": "string",
            "S3DataSource": { 
               "CompressionType": "string",
               "HubAccessConfig": { 
                  "HubContentArn": "string"
               },
               "ManifestS3Uri": "string",
               "ModelAccessConfig": { 
                  "AcceptEula": boolean
               },
               "S3DataType": "string",
               "S3Uri": "string"
            }
         }
      ],
      "ContainerHostname": "string",
      "Environment": { 
         "string" : "string" 
      },
      "Image": "string",
      "ImageConfig": { 
         "RepositoryAccessMode": "string",
         "RepositoryAuthConfig": { 
            "RepositoryCredentialsProviderArn": "string"
         }
      },
      "InferenceSpecificationName": "string",
      "Mode": "string",
      "ModelDataSource": { 
         "S3DataSource": { 
            "CompressionType": "string",
            "HubAccessConfig": { 
               "HubContentArn": "string"
            },
            "ManifestS3Uri": "string",
            "ModelAccessConfig": { 
               "AcceptEula": boolean
            },
            "S3DataType": "string",
            "S3Uri": "string"
         }
      },
      "ModelDataUrl": "string",
      "ModelPackageName": "string",
      "MultiModelConfig": { 
         "ModelCacheSetting": "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.

Containers

Specifies the containers in the inference pipeline.

Type: Array of ContainerDefinition objects

Array Members: Maximum number of 15 items.

Required: No

EnableNetworkIsolation

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

Type: Boolean

Required: No

ExecutionRoleArn

The Amazon Resource Name (ARN) of the IAM role that 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 SageMaker Roles.

Note

To be able to pass this role to 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: No

InferenceExecutionConfig

Specifies details of how containers in a multi-container endpoint are called.

Type: InferenceExecutionConfig object

Required: No

ModelName

The name of the new model.

Type: String

Length Constraints: Maximum length of 63.

Pattern: ^[a-zA-Z0-9]([\-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: No

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.

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 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.

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 SageMaker.

Type: String

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

Pattern: arn:aws[a-z\-]*:sagemaker:[a-z0-9\-]*:[0-9]{12}:model/.*

Errors

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

ResourceLimitExceeded

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

HTTP Status Code: 400