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Configures a hyperparameter tuning job.

HyperParameterTuningJobObjective The HyperParameterTuningJobObjective object that specifies the objective metric for this tuning job.
Type: HyperParameterTuningJobObjective object
Required: No

ParameterRanges The ParameterRanges object that specifies the ranges of hyperparameters that this tuning job searches.
Type: ParameterRanges object
Required: No

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

Strategy Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job it launches. To use the Bayesian search stategy, set this to Bayesian. To randomly search, set it to Random. For information about search strategies, see How Hyperparameter Tuning Works.
Type: String
Valid Values:Bayesian | Random
Required: Yes

TrainingJobEarlyStoppingType 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. Type: String
Valid Values:Off | Auto
Required: No

For more information about using this API in one of the language-specific AWS SDKs, see the following: + AWS SDK for C++ + AWS SDK for Go + AWS SDK for Go - Pilot + AWS SDK for Java + AWS SDK for Ruby V2