Class: Aws::SageMaker::Types::TransformJob

Inherits:

Struct

• Object
• Struct
• Aws::SageMaker::Types::TransformJob

Defined in:

(unknown)

Overview

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

Returned by:

• Aws::SageMaker::Types::TrialComponentSourceDetail#transform_job

See Also:

• AWS API Documentation

Instance Attribute Summary

• #auto_ml_job_arn ⇒ String

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

• #batch_strategy ⇒ 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.

• #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.

• #environment ⇒ 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.

• #failure_reason ⇒ String

  If the transform job failed, the reason it failed.

• #labeling_job_arn ⇒ String

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

• #max_concurrent_transforms ⇒ Integer

  The maximum number of parallel requests that can be sent to each instance in a transform job.

• #max_payload_in_mb ⇒ Integer

  The maximum allowed size of the payload, in MB.

• #model_client_config ⇒ Types::ModelClientConfig

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

  .

• #model_name ⇒ String

  The name of the model associated with the transform job.

• #tags ⇒ Array<Types::Tag>

  A list of tags associated with the transform job.

• #transform_end_time ⇒ Time

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

• #transform_input ⇒ Types::TransformInput

  Describes the input source of a transform job and the way the transform job consumes it.

  .

• #transform_job_arn ⇒ String

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

• #transform_job_name ⇒ String

  The name of the transform job.

• #transform_job_status ⇒ String

  The status of the transform job.

• #transform_output ⇒ Types::TransformOutput

  Describes the results of a transform job.

  .

• #transform_resources ⇒ Types::TransformResources

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

  .

• #transform_start_time ⇒ Time

  Indicates when the transform job starts on ML instances.

Instance Attribute Details

#auto_ml_job_arn ⇒ String

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

Returns:

• (String) —

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

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:

• CreateProcessingJob

• CreateTrainingJob

• CreateTransformJob

Returns:

• (Types::ExperimentConfig) —

  Associates a SageMaker job as a trial component with an experiment and trial.

#failure_reason ⇒ String

If the transform job failed, the reason it failed.

Returns:

• (String) —

  If the transform job failed, the reason it failed.

#labeling_job_arn ⇒ String

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

Returns:

• (String) —

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

#max_concurrent_transforms ⇒ Integer

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.

Returns:

• (Integer) —

  The maximum number of parallel requests that can be sent to each instance in a transform job.

#max_payload_in_mb ⇒ Integer

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.

Returns:

• (Integer) —

  The maximum allowed size of the payload, in MB.

#model_client_config ⇒ Types::ModelClientConfig

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

Returns:

• (Types::ModelClientConfig) —

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

  .

#model_name ⇒ String

The name of the model associated with the transform job.

Returns:

• (String) —

  The name of the model associated with the transform job.

#tags ⇒ Array<Types::Tag>

A list of tags associated with the transform job.

Returns:

• (Array<Types::Tag>) —

  A list of tags associated with 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 input source of a transform job and the way the transform job consumes it.

Returns:

• (Types::TransformInput) —

  Describes the input source of a transform job and the way the transform job consumes it.

  .

#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.

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.

  Possible values:

  • InProgress
  • Completed
  • Failed
  • Stopping
  • Stopped

Returns:

• (String) —

  The status of the transform job.

#transform_output ⇒ Types::TransformOutput

Describes the results of a transform job.

Returns:

• (Types::TransformOutput) —

  Describes the results of a transform job.

  .

#transform_resources ⇒ Types::TransformResources

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

Returns:

• (Types::TransformResources) —

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