class Aws::SageMaker::Types::HyperParameterAlgorithmSpecification

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

@note When making an API call, you may pass HyperParameterAlgorithmSpecification

data as a hash:

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

@!attribute [rw] training_image

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][1]. 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][2].

[1]: https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-algo-docker-registry-paths.html
[2]: https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html
@return [String]

@!attribute [rw] training_input_mode

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][1].

[1]: https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html
@return [String]

@!attribute [rw] algorithm_name

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`.
@return [String]

@!attribute [rw] metric_definitions

An array of MetricDefinition objects that specify the metrics that
the algorithm emits.
@return [Array<Types::MetricDefinition>]

@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/HyperParameterAlgorithmSpecification AWS API Documentation

Constants

SENSITIVE