AWS Tools for Windows PowerShell
Command Reference

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Synopsis

Calls the Amazon SageMaker Service CreateCompilationJob API operation.

Syntax

New-SMCompilationJob
-CompilationJobName <String>
-InputConfig_DataInputConfig <String>
-InputConfig_Framework <Framework>
-StoppingCondition_MaxRuntimeInSecond <Int32>
-RoleArn <String>
-OutputConfig_S3OutputLocation <String>
-InputConfig_S3Uri <String>
-OutputConfig_TargetDevice <TargetDevice>
-Force <SwitchParameter>

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.

Parameters

-CompilationJobName <String>
A name for the model compilation job. The name must be unique within the AWS Region and within your AWS account.
Required?False
Position?1
Accept pipeline input?True (ByValue, )
-Force <SwitchParameter>
This parameter overrides confirmation prompts to force the cmdlet to continue its operation. This parameter should always be used with caution.
Required?False
Position?Named
Accept pipeline input?False
-InputConfig_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]}
  • 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.
Required?False
Position?Named
Accept pipeline input?False
-InputConfig_Framework <Framework>
Identifies the framework in which the model was trained. For example: TENSORFLOW.
Required?False
Position?Named
Accept pipeline input?False
-InputConfig_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).
Required?False
Position?Named
Accept pipeline input?False
-OutputConfig_S3OutputLocation <String>
Identifies the S3 path where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix.
Required?False
Position?Named
Accept pipeline input?False
-OutputConfig_TargetDevice <TargetDevice>
Identifies the device that you want to run your model on after it has been compiled. For example: ml_c5.
Required?False
Position?Named
Accept pipeline input?False
-RoleArn <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.
Required?False
Position?Named
Accept pipeline input?False
-StoppingCondition_MaxRuntimeInSecond <Int32>
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.
Required?False
Position?Named
Accept pipeline input?False
AliasesStoppingCondition_MaxRuntimeInSeconds

Common Credential and Region Parameters

-AccessKey <String>
The AWS access key for the user account. This can be a temporary access key if the corresponding session token is supplied to the -SessionToken parameter.
Required? False
Position? Named
Accept pipeline input? False
-Credential <AWSCredentials>
An AWSCredentials object instance containing access and secret key information, and optionally a token for session-based credentials.
Required? False
Position? Named
Accept pipeline input? False
-ProfileLocation <String>

Used to specify the name and location of the ini-format credential file (shared with the AWS CLI and other AWS SDKs)

If this optional parameter is omitted this cmdlet will search the encrypted credential file used by the AWS SDK for .NET and AWS Toolkit for Visual Studio first. If the profile is not found then the cmdlet will search in the ini-format credential file at the default location: (user's home directory)\.aws\credentials. Note that the encrypted credential file is not supported on all platforms. It will be skipped when searching for profiles on Windows Nano Server, Mac, and Linux platforms.

If this parameter is specified then this cmdlet will only search the ini-format credential file at the location given.

As the current folder can vary in a shell or during script execution it is advised that you use specify a fully qualified path instead of a relative path.

Required? False
Position? Named
Accept pipeline input? False
-ProfileName <String>
The user-defined name of an AWS credentials or SAML-based role profile containing credential information. The profile is expected to be found in the secure credential file shared with the AWS SDK for .NET and AWS Toolkit for Visual Studio. You can also specify the name of a profile stored in the .ini-format credential file used with the AWS CLI and other AWS SDKs.
Required? False
Position? Named
Accept pipeline input? False
-NetworkCredential <PSCredential>
Used with SAML-based authentication when ProfileName references a SAML role profile. Contains the network credentials to be supplied during authentication with the configured identity provider's endpoint. This parameter is not required if the user's default network identity can or should be used during authentication.
Required? False
Position? Named
Accept pipeline input? False
-SecretKey <String>
The AWS secret key for the user account. This can be a temporary secret key if the corresponding session token is supplied to the -SessionToken parameter.
Required? False
Position? Named
Accept pipeline input? False
-SessionToken <String>
The session token if the access and secret keys are temporary session-based credentials.
Required? False
Position? Named
Accept pipeline input? False
-Region <String>
The system name of the AWS region in which the operation should be invoked. For example, us-east-1, eu-west-1 etc.
Required? False
Position? Named
Accept pipeline input? False
-EndpointUrl <String>

The endpoint to make the call against.

Note: This parameter is primarily for internal AWS use and is not required/should not be specified for normal usage. The cmdlets normally determine which endpoint to call based on the region specified to the -Region parameter or set as default in the shell (via Set-DefaultAWSRegion). Only specify this parameter if you must direct the call to a specific custom endpoint.

Required? False
Position? Named
Accept pipeline input? False

Inputs

System.String
You can pipe a String object to this cmdlet for the CompilationJobName parameter.

Outputs

System.String
This cmdlet returns a System.String object. The service call response (type Amazon.SageMaker.Model.CreateCompilationJobResponse) can also be referenced from properties attached to the cmdlet entry in the $AWSHistory stack.
Developer Guide
Service API Reference
AWS Tools for PowerShell User Guide

Supported Version

AWS Tools for PowerShell: 2.x.y.z