HyperParameterTuningJobConfig
Configures a hyperparameter tuning job.
Contents
- 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 strategy, set this to
Bayesian. To randomly search, set it toRandom. For information about search strategies, see How Hyperparameter Tuning Works.Type: String
Valid Values:
Bayesian | RandomRequired: 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
-
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 | AutoRequired: No
- TuningJobCompletionCriteria
-
The tuning job's completion criteria.
Type: TuningJobCompletionCriteria object
Required: No
See Also
For more information about using this API in one of the language-specific Amazon SDKs, see the following: