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Class: Aws::SageMaker::Types::HyperParameterAlgorithmSpecification

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

Overview

Note:

When passing HyperParameterAlgorithmSpecification as input to an Aws::Client method, you can use a vanilla Hash:

{
  training_image: "AlgorithmImage",
  training_input_mode: "Pipe", # required, accepts Pipe, File
  algorithm_name: "ArnOrName",
  metric_definitions: [
    {
      name: "MetricName", # required
      regex: "MetricRegex", # required
    },
  ],
}

Specifies which training algorithm to use for training jobs that a hyperparameter tuning job launches and the metrics to monitor.

Returned by:

See Also:

Instance Attribute Summary collapse

Instance Attribute Details

#algorithm_name ⇒ String

The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this parameter, do not specify a value for TrainingImage.

Returns:

  • (String) —

    The name of the resource algorithm to use for the hyperparameter tuning job.

#metric_definitions ⇒ Array<Types::MetricDefinition>

An array of MetricDefinition objects that specify the metrics that the algorithm emits.

Returns:

#training_image ⇒ String

The registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters. Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.

Returns:

  • (String) —

    The registry path of the Docker image that contains the training algorithm.

#training_input_mode ⇒ String

The input mode that the algorithm supports: File or Pipe. In File input mode, Amazon SageMaker downloads the training data from Amazon S3 to the storage volume that is attached to the training instance and mounts the directory to the Docker volume for the training container. In Pipe input mode, Amazon SageMaker streams data directly from Amazon S3 to the container.

If you specify File mode, make sure that you provision the storage volume that is attached to the training instance with enough capacity to accommodate the training data downloaded from Amazon S3, the model artifacts, and intermediate information.

For more information about input modes, see Algorithms.

Returns:

  • (String) —

    The input mode that the algorithm supports: File or Pipe.