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AWS::SageMaker::TransformJob - AWS CloudFormation
SyntaxPropertiesReturn values

This is the new CloudFormation Template Reference Guide. Please update your bookmarks and links. For help getting started with CloudFormation, see the AWS CloudFormation User Guide.

AWS::SageMaker::TransformJob

A batch transform job. For information about SageMaker batch transform, see Use Batch Transform.

Syntax

To declare this entity in your CloudFormation template, use the following syntax:

JSON

{
  "Type" : "AWS::SageMaker::TransformJob",
  "Properties" : {
      "BatchStrategy" : String,
      "DataCaptureConfig" : DataCaptureConfig,
      "DataProcessing" : DataProcessing,
      "Environment" : {Key: Value, ...},
      "ExperimentConfig" : ExperimentConfig,
      "MaxConcurrentTransforms" : Integer,
      "MaxPayloadInMB" : Integer,
      "ModelClientConfig" : ModelClientConfig,
      "ModelName" : String,
      "Tags" : [ TagsItems, ... ],
      "TransformInput" : TransformInput,
      "TransformOutput" : TransformOutput,
      "TransformResources" : TransformResources
    }
}

Properties

BatchStrategy

Specifies the number of records to include in a mini-batch for an HTTP inference request. A record is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record.

Required: No

Type: String

Allowed values: MultiRecord | SingleRecord

Update requires: Replacement

DataCaptureConfig

Configuration to control how SageMaker AI captures inference data.

Required: No

Type: DataCaptureConfig

Update requires: Replacement

DataProcessing

The data structure used to specify the data to be used for inference in a batch transform job and to associate the data that is relevant to the prediction results in the output. The input filter provided allows you to exclude input data that is not needed for inference in a batch transform job. The output filter provided allows you to include input data relevant to interpreting the predictions in the output from the job. For more information, see Associate Prediction Results with their Corresponding Input Records.

Required: No

Type: DataProcessing

Update requires: Replacement

Environment

The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.

Required: No

Type: Object of String

Pattern: [a-zA-Z_][a-zA-Z0-9_]*

Maximum: 10240

Update requires: Replacement

ExperimentConfig

Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:

Required: No

Type: ExperimentConfig

Update requires: Replacement

MaxConcurrentTransforms

The maximum number of parallel requests that can be sent to each instance in a transform job. If MaxConcurrentTransforms is set to 0 or left unset, SageMaker checks the optional execution-parameters to determine the settings for your chosen algorithm. If the execution-parameters endpoint is not enabled, the default value is 1. For built-in algorithms, you don't need to set a value for MaxConcurrentTransforms.

Required: No

Type: Integer

Minimum: 0

Update requires: Replacement

MaxPayloadInMB

The maximum allowed size of the payload, in MB. A payload is the data portion of a record (without metadata). The value in MaxPayloadInMB must be greater than, or equal to, the size of a single record. To estimate the size of a record in MB, divide the size of your dataset by the number of records. To ensure that the records fit within the maximum payload size, we recommend using a slightly larger value. The default value is 6 MB. For cases where the payload might be arbitrarily large and is transmitted using HTTP chunked encoding, set the value to 0. This feature works only in supported algorithms. Currently, SageMaker built-in algorithms do not support HTTP chunked encoding.

Required: No

Type: Integer

Minimum: 0

Update requires: Replacement

ModelClientConfig

Configures the timeout and maximum number of retries for processing a transform job invocation.

Required: No

Type: ModelClientConfig

Update requires: Replacement

ModelName

The name of the model associated with the transform job.

Required: Yes

Type: String

Pattern: ^[a-zA-Z0-9]([\-a-zA-Z0-9]*[a-zA-Z0-9])?$

Maximum: 63

Update requires: Replacement

Tags

A list of tags associated with the transform job.

Required: No

Type: Array of TagsItems

Maximum: 50

Update requires: Replacement

TransformInput

A description of the input source and the way the transform job consumes it.

Required: Yes

Type: TransformInput

Update requires: Replacement

TransformOutput

Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.

Required: Yes

Type: TransformOutput

Update requires: Replacement

TransformResources

Identifies the ML compute instances for the transform job.

Required: Yes

Type: TransformResources

Update requires: Replacement

Return values

Ref

Fn::GetAtt

CreationTime

A timestamp that shows when the transform Job was created.

TransformEndTime

Indicates when the transform job has been completed, or has stopped or failed. You are billed for the time interval between this time and the value of TransformStartTime.

TransformJobArn

The Amazon Resource Name (ARN) of the transform job.

TransformJobName

The name of the transform job.

TransformJobStatus

The status of the transform job.

Transform job statuses are:

  • InProgress - The job is in progress.

  • Completed - The job has completed.

  • Failed - The transform job has failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeTransformJob call.

  • Stopping - The transform job is stopping.

  • Stopped - The transform job has stopped.

TransformStartTime

Indicates when the transform job starts on ML instances. You are billed for the time interval between this time and the value of TransformEndTime.