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.
SEE ALSO
This class forms part of Paws, describing an object used in Paws::SageMaker
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