aws-java-sdk-sagemaker/src/main/java/com/amazonaws/services/sagemaker/AmazonSageMakerClient.java
@Override
    public CreateCompilationJobResult createCompilationJob(CreateCompilationJobRequest request) {
        request = beforeClientExecution(request);
        return executeCreateCompilationJob(request);
    }
/**
     * <p>
     * Starts a model compilation job. After the model has been compiled, Amazon SageMaker saves the resulting model
     * artifacts to an Amazon Simple Storage Service (Amazon S3) bucket that you specify.
     * </p>
     * <p>
     * If you choose to host your model using Amazon SageMaker 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.
     * </p>
     * <p>
     * In the request body, you provide the following:
     * </p>
     * <ul>
     * <li>
     * <p>
     * A name for the compilation job
     * </p>
     * </li>
     * <li>
     * <p>
     * Information about the input model artifacts
     * </p>
     * </li>
     * <li>
     * <p>
     * The output location for the compiled model and the device (target) that the model runs on
     * </p>
     * </li>
     * <li>
     * <p>
     * <code>The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker assumes to perform the model compilation job</code>
     * </p>
     * </li>
     * </ul>
     * <p>
     * You can also provide a <code>Tag</code> to track the model compilation job's resource use and costs. The response
     * body contains the <code>CompilationJobArn</code> for the compiled job.
     * </p>
     * <p>
     * To stop a model compilation job, use <a>StopCompilationJob</a>. To get information about a particular model
     * compilation job, use <a>DescribeCompilationJob</a>. To get information about multiple model compilation jobs, use
     * <a>ListCompilationJobs</a>.
     * </p>
     * 
     * @param createCompilationJobRequest
     * @return Result of the CreateCompilationJob operation returned by the service.
     * @throws ResourceInUseException
     *         Resource being accessed is in use.
     * @throws ResourceLimitExceededException
     *         You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
     *         created.
     * @sample AmazonSageMaker.CreateCompilationJob
     * @see <a href="http://docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateCompilationJob" target="_top">AWS
     *      API Documentation</a>
     */

Starts a model compilation job. After the model has been compiled, Amazon SageMaker 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 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 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.

@param createCompilationJobRequest
@return Result of the CreateCompilationJob operation returned by the service.
@throws ResourceInUseException
Resource being accessed is in use.
@throws ResourceLimitExceededException
You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
created.
Starts a model compilation job . After the model has been compiled Amazon SageMaker 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 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 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 . @param createCompilationJobRequest @return Result of the CreateCompilationJob operation returned by the service . @throws ResourceInUseException Resource being accessed is in use . @throws ResourceLimitExceededException You have exceeded an Amazon SageMaker resource limit . For example you might have too many training jobs created .
@Override
    public CreateCompilationJobResult createCompilationJob(CreateCompilationJobRequest request) {
        request = beforeClientExecution(request);
        return executeCreateCompilationJob(request);
    }
@ Override public CreateCompilationJobResult createCompilationJob ( CreateCompilationJobRequest request ) { request = beforeClientExecution ( request ) ; return executeCreateCompilationJob ( request ) ; }
<p>
 Starts a model compilation job.
<p > Starts a model compilation job .