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Class: Aws::SageMaker::Types::DescribeTransformJobResponse

Inherits:
Struct
  • Object
show all
Defined in:
(unknown)

Overview

Instance Attribute Summary collapse

Instance Attribute Details

#batch_strategyString

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.

To enable the batch strategy, you must set SplitType to Line, RecordIO, or TFRecord.

Possible values:

  • MultiRecord
  • SingleRecord

Returns:

  • (String)

    Specifies the number of records to include in a mini-batch for an HTTP inference request.

#creation_timeTime

A timestamp that shows when the transform Job was created.

Returns:

  • (Time)

    A timestamp that shows when the transform Job was created.

#environmentHash<String,String>

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

Returns:

  • (Hash<String,String>)

    The environment variables to set in the Docker container.

#failure_reasonString

If the transform job failed, FailureReason describes why it failed. A transform job creates a log file, which includes error messages, and stores it as an Amazon S3 object. For more information, see Log Amazon SageMaker Events with Amazon CloudWatch.

Returns:

  • (String)

    If the transform job failed, FailureReason describes why it failed.

#labeling_job_arnString

The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the transform or training job.

Returns:

  • (String)

    The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the transform or training job.

#max_concurrent_transformsInteger

The maximum number of parallel requests on each instance node that can be launched in a transform job. The default value is 1.

Returns:

  • (Integer)

    The maximum number of parallel requests on each instance node that can be launched in a transform job.

#max_payload_in_mbInteger

The maximum payload size, in MB, used in the transform job.

Returns:

  • (Integer)

    The maximum payload size, in MB, used in the transform job.

#model_nameString

The name of the model used in the transform job.

Returns:

  • (String)

    The name of the model used in the transform job.

#transform_end_timeTime

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.

Returns:

  • (Time)

    Indicates when the transform job has been completed, or has stopped or failed.

#transform_inputTypes::TransformInput

Describes the dataset to be transformed and the Amazon S3 location where it is stored.

Returns:

  • (Types::TransformInput)

    Describes the dataset to be transformed and the Amazon S3 location where it is stored.

#transform_job_arnString

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

Returns:

  • (String)

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

#transform_job_nameString

The name of the transform job.

Returns:

  • (String)

    The name of the transform job.

#transform_job_statusString

The status of the transform job. If the transform job failed, the reason is returned in the FailureReason field.

Possible values:

  • InProgress
  • Completed
  • Failed
  • Stopping
  • Stopped

Returns:

  • (String)

    The status of the transform job.

#transform_outputTypes::TransformOutput

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

Returns:

  • (Types::TransformOutput)

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

#transform_resourcesTypes::TransformResources

Describes the resources, including ML instance types and ML instance count, to use for the transform job.

Returns:

  • (Types::TransformResources)

    Describes the resources, including ML instance types and ML instance count, to use for the transform job.

#transform_start_timeTime

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

Returns:

  • (Time)

    Indicates when the transform job starts on ML instances.