AWS CLI Command Reference

[ aws . sagemaker ]

create-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 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 .

See also: AWS API Documentation

See ‘aws help’ for descriptions of global parameters.

Synopsis

  create-compilation-job
--compilation-job-name <value>
--role-arn <value>
--input-config <value>
--output-config <value>
--stopping-condition <value>
[--cli-input-json | --cli-input-yaml]
[--generate-cli-skeleton <value>]
[--cli-auto-prompt <value>]

Options

--compilation-job-name (string)

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

--role-arn (string)

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.

--input-config (structure)

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.

S3Uri -> (string)

The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).

DataInputConfig -> (string)

Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. The data inputs are InputConfig$Framework specific.

  • TensorFlow : You must specify the name and shape (NHWC format) of the expected data inputs using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.

    • Examples for one input:

      • If using the console, {"input":[1,1024,1024,3]}

      • If using the CLI, {\"input\":[1,1024,1024,3]}

    • Examples for two inputs:

      • If using the console, {"data1": [1,28,28,1], "data2":[1,28,28,1]}

      • If using the CLI, {\"data1\": [1,28,28,1], \"data2\":[1,28,28,1]}

  • KERAS : You must specify the name and shape (NCHW format) of expected data inputs using a dictionary format for your trained model. Note that while Keras model artifacts should be uploaded in NHWC (channel-last) format, DataInputConfig should be specified in NCHW (channel-first) format. The dictionary formats required for the console and CLI are different.

    • Examples for one input:

      • If using the console, {"input_1":[1,3,224,224]}

      • If using the CLI, {\"input_1\":[1,3,224,224]}

    • Examples for two inputs:

      • If using the console, {"input_1": [1,3,224,224], "input_2":[1,3,224,224]}

      • If using the CLI, {\"input_1\": [1,3,224,224], \"input_2\":[1,3,224,224]}

  • MXNET/ONNX : You must specify the name and shape (NCHW format) of the expected data inputs in order using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.

    • Examples for one input:

      • If using the console, {"data":[1,3,1024,1024]}

      • If using the CLI, {\"data\":[1,3,1024,1024]}

    • Examples for two inputs:

      • If using the console, {"var1": [1,1,28,28], "var2":[1,1,28,28]}

      • If using the CLI, {\"var1\": [1,1,28,28], \"var2\":[1,1,28,28]}

  • PyTorch : You can either specify the name and shape (NCHW format) of expected data inputs in order using a dictionary format for your trained model or you can specify the shape only using a list format. The dictionary formats required for the console and CLI are different. The list formats for the console and CLI are the same.

    • Examples for one input in dictionary format:

      • If using the console, {"input0":[1,3,224,224]}

      • If using the CLI, {\"input0\":[1,3,224,224]}

    • Example for one input in list format: [[1,3,224,224]]

    • Examples for two inputs in dictionary format:

      • If using the console, {"input0":[1,3,224,224], "input1":[1,3,224,224]}

      • If using the CLI, {\"input0\":[1,3,224,224], \"input1\":[1,3,224,224]}

    • Example for two inputs in list format: [[1,3,224,224], [1,3,224,224]]

  • XGBOOST : input data name and shape are not needed.

Framework -> (string)

Identifies the framework in which the model was trained. For example: TENSORFLOW.

Shorthand Syntax:

S3Uri=string,DataInputConfig=string,Framework=string

JSON Syntax:

{
  "S3Uri": "string",
  "DataInputConfig": "string",
  "Framework": "TENSORFLOW"|"KERAS"|"MXNET"|"ONNX"|"PYTORCH"|"XGBOOST"|"TFLITE"
}

--output-config (structure)

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

S3OutputLocation -> (string)

Identifies the S3 path where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix.

TargetDevice -> (string)

Identifies the device that you want to run your model on after it has been compiled. For example: ml_c5.

Shorthand Syntax:

S3OutputLocation=string,TargetDevice=string

JSON Syntax:

{
  "S3OutputLocation": "string",
  "TargetDevice": "lambda"|"ml_m4"|"ml_m5"|"ml_c4"|"ml_c5"|"ml_p2"|"ml_p3"|"ml_inf1"|"jetson_tx1"|"jetson_tx2"|"jetson_nano"|"jetson_xavier"|"rasp3b"|"imx8qm"|"deeplens"|"rk3399"|"rk3288"|"aisage"|"sbe_c"|"qcs605"|"qcs603"|"sitara_am57x"|"amba_cv22"
}

--stopping-condition (structure)

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.

MaxRuntimeInSeconds -> (integer)

The maximum length of time, in seconds, that the training or compilation job can run. If job does not complete during this time, Amazon SageMaker ends the job. If value is not specified, default value is 1 day. The maximum value is 28 days.

MaxWaitTimeInSeconds -> (integer)

The maximum length of time, in seconds, how long you are willing to wait for a managed spot training job to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the training job runs. It must be equal to or greater than MaxRuntimeInSeconds .

Shorthand Syntax:

MaxRuntimeInSeconds=integer,MaxWaitTimeInSeconds=integer

JSON Syntax:

{
  "MaxRuntimeInSeconds": integer,
  "MaxWaitTimeInSeconds": integer
}

--cli-input-json | --cli-input-yaml (string) Reads arguments from the JSON string provided. The JSON string follows the format provided by --generate-cli-skeleton. If other arguments are provided on the command line, those values will override the JSON-provided values. It is not possible to pass arbitrary binary values using a JSON-provided value as the string will be taken literally. This may not be specified along with --cli-input-yaml.

--generate-cli-skeleton (string) Prints a JSON skeleton to standard output without sending an API request. If provided with no value or the value input, prints a sample input JSON that can be used as an argument for --cli-input-json. Similarly, if provided yaml-input it will print a sample input YAML that can be used with --cli-input-yaml. If provided with the value output, it validates the command inputs and returns a sample output JSON for that command.

--cli-auto-prompt (boolean) Automatically prompt for CLI input parameters.

See ‘aws help’ for descriptions of global parameters.

Output

CompilationJobArn -> (string)

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

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

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