HyperParameterTuningJobConfig

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

Type: ResourceLimits object

Required: Yes

Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job it launches. For information about search strategies, see How Hyperparameter Tuning Works.

Type: String

Valid Values: Bayesian | Random | Hyperband | Grid

Required: Yes

The HyperParameterTuningJobObjective specifies the objective metric used to evaluate the performance of training jobs launched by this tuning job.

Type: HyperParameterTuningJobObjective object

Required: No

The ParameterRanges object that specifies the ranges of hyperparameters that this tuning job searches over to find the optimal configuration for the highest model performance against your chosen objective metric.

Type: ParameterRanges object

Required: No

A value used to initialize a pseudo-random number generator. Setting a random seed and using the same seed later for the same tuning job will allow hyperparameter optimization to find more a consistent hyperparameter configuration between the two runs.

Type: Integer

Valid Range: Minimum value of 0.

Required: No

The configuration for the Hyperband optimization strategy. This parameter should be provided only if Hyperband is selected as the strategy for HyperParameterTuningJobConfig.

Type: HyperParameterTuningJobStrategyConfig object

Required: No

Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job. Because the Hyperband strategy has its own advanced internal early stopping mechanism, TrainingJobEarlyStoppingType must be OFF to use Hyperband. This parameter can take on one of the following values (the default value is OFF):

Type: String

Valid Values: Off | Auto

Required: No

The tuning job's completion criteria.

Type: TuningJobCompletionCriteria object

Required: No