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Create Compilation Job

sagemaker_create_compilation_job R Documentation

Starts a model compilation job

Description

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 Amazon Web Services 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 stop_compilation_job. To get information about a particular model compilation job, use describe_compilation_job. To get information about multiple model compilation jobs, use list_compilation_jobs.

Usage

sagemaker_create_compilation_job(CompilationJobName, RoleArn,
  ModelPackageVersionArn, InputConfig, OutputConfig, VpcConfig,
  StoppingCondition, Tags)

Arguments

CompilationJobName

[required] A name for the model compilation job. The name must be unique within the Amazon Web Services Region and within your Amazon Web Services account.

RoleArn

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

During model compilation, Amazon SageMaker 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, the caller of this API must have the iam:PassRole permission. For more information, see Amazon SageMaker Roles.

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 create_compilation_job request will return an exception.

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.

OutputConfig

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

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.

StoppingCondition

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

Tags

An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

Value

A list with the following syntax: 

list(
  CompilationJobArn = "string"
)

Request syntax

svc$create_compilation_job(
  CompilationJobName = "string",
  RoleArn = "string",
  ModelPackageVersionArn = "string",
  InputConfig = list(
    S3Uri = "string",
    DataInputConfig = "string",
    Framework = "TENSORFLOW"|"KERAS"|"MXNET"|"ONNX"|"PYTORCH"|"XGBOOST"|"TFLITE"|"DARKNET"|"SKLEARN",
    FrameworkVersion = "string"
  ),
  OutputConfig = list(
    S3OutputLocation = "string",
    TargetDevice = "lambda"|"ml_m4"|"ml_m5"|"ml_m6g"|"ml_c4"|"ml_c5"|"ml_c6g"|"ml_p2"|"ml_p3"|"ml_g4dn"|"ml_inf1"|"ml_inf2"|"ml_trn1"|"ml_eia2"|"jetson_tx1"|"jetson_tx2"|"jetson_nano"|"jetson_xavier"|"rasp3b"|"rasp4b"|"imx8qm"|"deeplens"|"rk3399"|"rk3288"|"aisage"|"sbe_c"|"qcs605"|"qcs603"|"sitara_am57x"|"amba_cv2"|"amba_cv22"|"amba_cv25"|"x86_win32"|"x86_win64"|"coreml"|"jacinto_tda4vm"|"imx8mplus",
    TargetPlatform = list(
      Os = "ANDROID"|"LINUX",
      Arch = "X86_64"|"X86"|"ARM64"|"ARM_EABI"|"ARM_EABIHF",
      Accelerator = "INTEL_GRAPHICS"|"MALI"|"NVIDIA"|"NNA"
    ),
    CompilerOptions = "string",
    KmsKeyId = "string"
  ),
  VpcConfig = list(
    SecurityGroupIds = list(
      "string"
    ),
    Subnets = list(
      "string"
    )
  ),
  StoppingCondition = list(
    MaxRuntimeInSeconds = 123,
    MaxWaitTimeInSeconds = 123,
    MaxPendingTimeInSeconds = 123
  ),
  Tags = list(
    list(
      Key = "string",
      Value = "string"
    )
  )
)