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

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

Overview

Returned by:

See Also:

Instance Attribute Summary collapse

Instance Attribute Details

#auto_ml_job_arn ⇒ String

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

Returns:

  • (String) —

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

#batch_strategy ⇒ String

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_time ⇒ Time

A timestamp that shows when the transform Job was created.

Returns:

  • (Time) —

    A timestamp that shows when the transform Job was created.

#data_processing ⇒ Types::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.

Returns:

  • (Types::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.

#environment ⇒ Hash<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.

#experiment_config ⇒ Types::ExperimentConfig

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

Returns:

#failure_reason ⇒ String

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_arn ⇒ String

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_transforms ⇒ Integer

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_mb ⇒ Integer

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_client_config ⇒ Types::ModelClientConfig

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

Returns:

#model_name ⇒ String

The name of the model used in the transform job.

Returns:

  • (String) —

    The name of the model used in the transform job.

#transform_end_time ⇒ Time

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_input ⇒ Types::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_arn ⇒ String

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

Returns:

  • (String) —

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

#transform_job_name ⇒ String

The name of the transform job.

Returns:

  • (String) —

    The name of the transform job.

#transform_job_status ⇒ String

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_output ⇒ Types::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_resources ⇒ Types::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_time ⇒ Time

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.