NAME

Paws::SageMaker::HyperParameterTuningJobConfig

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::HyperParameterTuningJobConfig object:

  $service_obj->Method(Att1 => { HyperParameterTuningJobObjective => $value, ..., TuningJobCompletionCriteria => $value  });

Results returned from an API call

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

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

DESCRIPTION

Configures a hyperparameter tuning job.

ATTRIBUTES

HyperParameterTuningJobObjective => Paws::SageMaker::HyperParameterTuningJobObjective

The HyperParameterTuningJobObjective object that specifies the objective metric for this tuning job.

ParameterRanges => Paws::SageMaker::ParameterRanges

The ParameterRanges object that specifies the ranges of hyperparameters that this tuning job searches.

REQUIRED ResourceLimits => Paws::SageMaker::ResourceLimits

The ResourceLimits object that specifies the maximum number of training jobs and parallel training jobs for this tuning job.

REQUIRED Strategy => Str

Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job it launches. To use the Bayesian search strategy, set this to "Bayesian". To randomly search, set it to "Random". For information about search strategies, see How Hyperparameter Tuning Works (https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-how-it-works.html).

TrainingJobEarlyStoppingType => Str

Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job. This can be one of the following values (the default value is "OFF"):

OFF: Training jobs launched by the hyperparameter tuning job do not use early stopping.
AUTO: Amazon SageMaker stops training jobs launched by the hyperparameter tuning job when they are unlikely to perform better than previously completed training jobs. For more information, see Stop Training Jobs Early (https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-early-stopping.html).

TuningJobCompletionCriteria => Paws::SageMaker::TuningJobCompletionCriteria

The tuning job's completion criteria.

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>