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pub struct HyperParameterTuningJobConfig {
    pub hyper_parameter_tuning_job_objective: Option<HyperParameterTuningJobObjective>,
    pub parameter_ranges: Option<ParameterRanges>,
    pub resource_limits: ResourceLimits,
    pub strategy: String,
    pub training_job_early_stopping_type: Option<String>,
    pub tuning_job_completion_criteria: Option<TuningJobCompletionCriteria>,
}
Expand description

Configures a hyperparameter tuning job.

Fields

hyper_parameter_tuning_job_objective: Option<HyperParameterTuningJobObjective>

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

parameter_ranges: Option<ParameterRanges>

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

resource_limits: ResourceLimits

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

strategy: String

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.

training_job_early_stopping_type: Option<String>

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.

tuning_job_completion_criteria: Option<TuningJobCompletionCriteria>

The tuning job's completion criteria.

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