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AWS::SageMaker::HyperParameterTuningJob HyperParameterTuningJobConfig
Configures a hyperparameter tuning job.
Syntax
To declare this entity in your CloudFormation template, use the following syntax:
JSON
{ "HyperParameterTuningJobObjective" :HyperParameterTuningJobObjective, "ParameterRanges" :ParameterRanges, "RandomSeed" :Integer, "ResourceLimits" :ResourceLimits, "Strategy" :String, "StrategyConfig" :StrategyConfig, "TrainingJobEarlyStoppingType" :String, "TuningJobCompletionCriteria" :TuningJobCompletionCriteria}
YAML
HyperParameterTuningJobObjective:HyperParameterTuningJobObjectiveParameterRanges:ParameterRangesRandomSeed:IntegerResourceLimits:ResourceLimitsStrategy:StringStrategyConfig:StrategyConfigTrainingJobEarlyStoppingType:StringTuningJobCompletionCriteria:TuningJobCompletionCriteria
Properties
HyperParameterTuningJobObjective-
The HyperParameterTuningJobObjective specifies the objective metric used to evaluate the performance of training jobs launched by this tuning job.
Required: No
Type: HyperParameterTuningJobObjective
Update requires: Replacement
ParameterRanges-
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.
Required: No
Type: ParameterRanges
Update requires: Replacement
RandomSeed-
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.
Required: No
Type: Integer
Minimum:
0Update requires: Replacement
ResourceLimits-
The ResourceLimits object that specifies the maximum number of training and parallel training jobs that can be used for this hyperparameter tuning job.
Required: Yes
Type: ResourceLimits
Update requires: Replacement
Strategy-
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.
Required: Yes
Type: String
Allowed values:
Bayesian | Random | Hyperband | GridUpdate requires: Replacement
StrategyConfig-
The configuration for the
Hyperbandoptimization strategy. This parameter should be provided only ifHyperbandis selected as the strategy forHyperParameterTuningJobConfig.Required: No
Type: StrategyConfig
Update requires: Replacement
TrainingJobEarlyStoppingType-
Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job. Because the
Hyperbandstrategy has its own advanced internal early stopping mechanism,TrainingJobEarlyStoppingTypemust beOFFto useHyperband. This parameter can take on one of the following values (the default value isOFF):- 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.
Required: No
Type: String
Allowed values:
Off | AutoUpdate requires: Replacement
TuningJobCompletionCriteria-
The tuning job's completion criteria.
Required: No
Type: TuningJobCompletionCriteria
Update requires: Replacement
