Request Syntax Request Parameters Response Syntax Response Elements Errors See Also

CreateCompilationJob

Starts a model compilation job. After the model has been compiled, Amazon SageMaker AI saves the resulting model artifacts to an Amazon Simple Storage Service (Amazon S3) bucket that you specify.

If you choose to host your model using Amazon SageMaker AI hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts with AWS IoT Greengrass. In that case, deploy them as an ML resource.

In the request body, you provide the following:

A name for the compilation job
Information about the input model artifacts
The output location for the compiled model and the device (target) that the model runs on
The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker AI assumes to perform the model compilation job.

You can also provide a Tag to track the model compilation job's resource use and costs. The response body contains the CompilationJobArn for the compiled job.

To stop a model compilation job, use StopCompilationJob. To get information about a particular model compilation job, use DescribeCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.

Request Syntax


{
   "CompilationJobName": "string",
   "InputConfig": { 
      "DataInputConfig": "string",
      "Framework": "string",
      "FrameworkVersion": "string",
      "S3Uri": "string"
   },
   "ModelPackageVersionArn": "string",
   "OutputConfig": { 
      "CompilerOptions": "string",
      "KmsKeyId": "string",
      "S3OutputLocation": "string",
      "TargetDevice": "string",
      "TargetPlatform": { 
         "Accelerator": "string",
         "Arch": "string",
         "Os": "string"
      }
   },
   "RoleArn": "string",
   "StoppingCondition": { 
      "MaxPendingTimeInSeconds": number,
      "MaxRuntimeInSeconds": number,
      "MaxWaitTimeInSeconds": number
   },
   "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.

CompilationJobName

A name for the model compilation job. The name must be unique within the AWS Region and within your AWS account.

Type: String

Length Constraints: Minimum length of 1. Maximum length of 63.

Pattern: ^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,62}$

Required: Yes

InputConfig

Provides information about the location of input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.

Type: InputConfig object

Required: No

ModelPackageVersionArn

The Amazon Resource Name (ARN) of a versioned model package. Provide either a ModelPackageVersionArn or an InputConfig object in the request syntax. The presence of both objects in the CreateCompilationJob request will return an exception.

Type: String

Length Constraints: Minimum length of 1. Maximum length of 2048.

Pattern: ^arn:aws(-cn|-us-gov|-iso-f)?:sagemaker:[a-z0-9\-]{9,16}:[0-9]{12}:model-package/[\S]{1,2048}$

Required: No

OutputConfig

Provides information about the output location for the compiled model and the target device the model runs on.

Type: OutputConfig object

Required: Yes

RoleArn

The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker AI to perform tasks on your behalf.

During model compilation, Amazon SageMaker AI needs your permission to:

Read input data from an S3 bucket
Write model artifacts to an S3 bucket
Write logs to Amazon CloudWatch Logs
Publish metrics to Amazon CloudWatch

You grant permissions for all of these tasks to an IAM role. To pass this role to Amazon SageMaker AI, the caller of this API must have the iam:PassRole permission. For more information, see Amazon SageMaker AI Roles.

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

StoppingCondition

Specifies a limit to how long a model compilation job can run. When the job reaches the time limit, Amazon SageMaker AI ends the compilation job. Use this API to cap model training costs.

Type: StoppingCondition object

Required: Yes

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 compilation job to connect to. Control access to your models by configuring the VPC. For more information, see Protect Compilation Jobs by Using an Amazon Virtual Private Cloud.

Type: NeoVpcConfig object

Required: No

Response Syntax


{
   "CompilationJobArn": "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.

CompilationJobArn

If the action is successful, the service sends back an HTTP 200 response. Amazon SageMaker AI returns the following data in JSON format:

CompilationJobArn: The Amazon Resource Name (ARN) of the compiled job.

Type: String

Length Constraints: Maximum length of 256.

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

Errors

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

ResourceInUse

Resource being accessed is in use.

HTTP Status Code: 400

ResourceLimitExceeded

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

HTTP Status Code: 400