NAME

Paws::SageMaker::HyperParameterAlgorithmSpecification

USAGE

This class represents one of two things:

Arguments in a call to a service

Use the attributes of this class as arguments to methods. You shouldn't make instances of this class. Each attribute should be used as a named argument in the calls that expect this type of object.

As an example, if Att1 is expected to be a Paws::SageMaker::HyperParameterAlgorithmSpecification object:

  $service_obj->Method(Att1 => { AlgorithmName => $value, ..., TrainingInputMode => $value  });

Results returned from an API call

Use accessors for each attribute. If Att1 is expected to be an Paws::SageMaker::HyperParameterAlgorithmSpecification object:

  $result = $service_obj->Method(...);
  $result->Att1->AlgorithmName

DESCRIPTION

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

ATTRIBUTES

AlgorithmName => Str

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".

MetricDefinitions => ArrayRef[Paws::SageMaker::MetricDefinition]

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

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 (https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-algo-docker-registry-paths.html). 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 (https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html).

REQUIRED TrainingInputMode => Str

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 (https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html).

BUGS and CONTRIBUTIONS

The source code is located here: <https://github.com/pplu/aws-sdk-perl>

Please report bugs to: <https://github.com/pplu/aws-sdk-perl/issues>