
# Generated by default/object.tt
package Paws::SageMaker::HyperParameterTuningJobConfig;
  use Moose;
  has HyperParameterTuningJobObjective => (is => 'ro', isa => 'Paws::SageMaker::HyperParameterTuningJobObjective');
  has ParameterRanges => (is => 'ro', isa => 'Paws::SageMaker::ParameterRanges');
  has ResourceLimits => (is => 'ro', isa => 'Paws::SageMaker::ResourceLimits', required => 1);
  has Strategy => (is => 'ro', isa => 'Str', required => 1);
  has TrainingJobEarlyStoppingType => (is => 'ro', isa => 'Str');
  has TuningJobCompletionCriteria => (is => 'ro', isa => 'Paws::SageMaker::TuningJobCompletionCriteria');

1;

### main pod documentation begin ###

=head1 NAME

Paws::SageMaker::HyperParameterTuningJobConfig

=head1 USAGE

This class represents one of two things:

=head3 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  });

=head3 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

=head1 DESCRIPTION

Configures a hyperparameter tuning job.

=head1 ATTRIBUTES


=head2 HyperParameterTuningJobObjective => L<Paws::SageMaker::HyperParameterTuningJobObjective>

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


=head2 ParameterRanges => L<Paws::SageMaker::ParameterRanges>

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


=head2 B<REQUIRED> ResourceLimits => L<Paws::SageMaker::ResourceLimits>

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


=head2 B<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 C<Bayesian>. To randomly
search, set it to C<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).


=head2 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 C<OFF>):

=over

=item OFF

Training jobs launched by the hyperparameter tuning job do not use
early stopping.

=item 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).

=back


=head2 TuningJobCompletionCriteria => L<Paws::SageMaker::TuningJobCompletionCriteria>

The tuning job's completion criteria.


=head1 SEE ALSO

This class forms part of L<Paws>, describing an object used in L<Paws::SageMaker>

=head1 BUGS and CONTRIBUTIONS

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

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

=cut