Copyright	(c) 2013-2021 Brendan Hay
License	Mozilla Public License, v. 2.0.
Maintainer	Brendan Hay <brendan.g.hay+amazonka@gmail.com>
Stability	auto-generated
Portability	non-portable (GHC extensions)
Safe Haskell	None

Amazonka.SageMaker.Types.HyperParameterTuningJobConfig

Description

Synopsis

Documentation

data HyperParameterTuningJobConfig Source #

Configures a hyperparameter tuning job.

See: newHyperParameterTuningJobConfig smart constructor.

Constructors

HyperParameterTuningJobConfig'

Fields

tuningJobCompletionCriteria :: Maybe TuningJobCompletionCriteria
The tuning job's completion criteria.
parameterRanges :: Maybe ParameterRanges
The ParameterRanges object that specifies the ranges of hyperparameters that this tuning job searches.
hyperParameterTuningJobObjective :: Maybe HyperParameterTuningJobObjective
The HyperParameterTuningJobObjective object that specifies the objective metric for this tuning job.
trainingJobEarlyStoppingType :: Maybe 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.
strategy :: HyperParameterTuningJobStrategyType
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.
resourceLimits :: ResourceLimits
The ResourceLimits object that specifies the maximum number of training jobs and parallel training jobs for this tuning job.

Instances

Instances details

Eq HyperParameterTuningJobConfig Source #
Instance details Defined in Amazonka.SageMaker.Types.HyperParameterTuningJobConfig Methods (==) :: HyperParameterTuningJobConfig -> HyperParameterTuningJobConfig -> Bool # (/=) :: HyperParameterTuningJobConfig -> HyperParameterTuningJobConfig -> Bool #
Read HyperParameterTuningJobConfig Source #
Instance details Defined in Amazonka.SageMaker.Types.HyperParameterTuningJobConfig Methods readsPrec :: Int -> ReadS HyperParameterTuningJobConfig # readList :: ReadS [HyperParameterTuningJobConfig] # readPrec :: ReadPrec HyperParameterTuningJobConfig # readListPrec :: ReadPrec [HyperParameterTuningJobConfig] #
Show HyperParameterTuningJobConfig Source #
Instance details Defined in Amazonka.SageMaker.Types.HyperParameterTuningJobConfig Methods showsPrec :: Int -> HyperParameterTuningJobConfig -> ShowS # show :: HyperParameterTuningJobConfig -> String # showList :: [HyperParameterTuningJobConfig] -> ShowS #
Generic HyperParameterTuningJobConfig Source #
Instance details Defined in Amazonka.SageMaker.Types.HyperParameterTuningJobConfig Associated Types type Rep HyperParameterTuningJobConfig :: Type -> Type # Methods from :: HyperParameterTuningJobConfig -> Rep HyperParameterTuningJobConfig x # to :: Rep HyperParameterTuningJobConfig x -> HyperParameterTuningJobConfig #
NFData HyperParameterTuningJobConfig Source #
Instance details Defined in Amazonka.SageMaker.Types.HyperParameterTuningJobConfig Methods rnf :: HyperParameterTuningJobConfig -> () #
Hashable HyperParameterTuningJobConfig Source #
Instance details Defined in Amazonka.SageMaker.Types.HyperParameterTuningJobConfig Methods hashWithSalt :: Int -> HyperParameterTuningJobConfig -> Int # hash :: HyperParameterTuningJobConfig -> Int #
ToJSON HyperParameterTuningJobConfig Source #
Instance details Defined in Amazonka.SageMaker.Types.HyperParameterTuningJobConfig Methods toJSON :: HyperParameterTuningJobConfig -> Value # toEncoding :: HyperParameterTuningJobConfig -> Encoding # toJSONList :: [HyperParameterTuningJobConfig] -> Value # toEncodingList :: [HyperParameterTuningJobConfig] -> Encoding #
FromJSON HyperParameterTuningJobConfig Source #
Instance details Defined in Amazonka.SageMaker.Types.HyperParameterTuningJobConfig Methods parseJSON :: Value -> Parser HyperParameterTuningJobConfig # parseJSONList :: Value -> Parser [HyperParameterTuningJobConfig] #
type Rep HyperParameterTuningJobConfig Source #
Instance details Defined in Amazonka.SageMaker.Types.HyperParameterTuningJobConfig type Rep HyperParameterTuningJobConfig = D1 ('MetaData "HyperParameterTuningJobConfig" "Amazonka.SageMaker.Types.HyperParameterTuningJobConfig" "libZSservicesZSamazonka-sagemakerZSamazonka-sagemaker" 'False) (C1 ('MetaCons "HyperParameterTuningJobConfig'" 'PrefixI 'True) ((S1 ('MetaSel ('Just "tuningJobCompletionCriteria") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe TuningJobCompletionCriteria)) :: (S1 ('MetaSel ('Just "parameterRanges") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe ParameterRanges)) :: S1 ('MetaSel ('Just "hyperParameterTuningJobObjective") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe HyperParameterTuningJobObjective)))) :: (S1 ('MetaSel ('Just "trainingJobEarlyStoppingType") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe TrainingJobEarlyStoppingType)) :: (S1 ('MetaSel ('Just "strategy") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 HyperParameterTuningJobStrategyType) :*: S1 ('MetaSel ('Just "resourceLimits") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 ResourceLimits)))))

newHyperParameterTuningJobConfig Source #

Arguments

:: HyperParameterTuningJobStrategyType	`$sel:strategy:HyperParameterTuningJobConfig'`
-> ResourceLimits	`$sel:resourceLimits:HyperParameterTuningJobConfig'`
-> HyperParameterTuningJobConfig

Create a value of HyperParameterTuningJobConfig with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:tuningJobCompletionCriteria:HyperParameterTuningJobConfig', hyperParameterTuningJobConfig_tuningJobCompletionCriteria - The tuning job's completion criteria.

$sel:parameterRanges:HyperParameterTuningJobConfig', hyperParameterTuningJobConfig_parameterRanges - The ParameterRanges object that specifies the ranges of hyperparameters that this tuning job searches.

$sel:hyperParameterTuningJobObjective:HyperParameterTuningJobConfig', hyperParameterTuningJobConfig_hyperParameterTuningJobObjective - The HyperParameterTuningJobObjective object that specifies the objective metric for this tuning job.

$sel:trainingJobEarlyStoppingType:HyperParameterTuningJobConfig', hyperParameterTuningJobConfig_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.

$sel:strategy:HyperParameterTuningJobConfig', hyperParameterTuningJobConfig_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 to Random. For information about search strategies, see How Hyperparameter Tuning Works.

$sel:resourceLimits:HyperParameterTuningJobConfig', hyperParameterTuningJobConfig_resourceLimits - The ResourceLimits object that specifies the maximum number of training jobs and parallel training jobs for this tuning job.

hyperParameterTuningJobConfig_tuningJobCompletionCriteria :: Lens' HyperParameterTuningJobConfig (Maybe TuningJobCompletionCriteria) Source #

The tuning job's completion criteria.

hyperParameterTuningJobConfig_parameterRanges :: Lens' HyperParameterTuningJobConfig (Maybe ParameterRanges) Source #

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

hyperParameterTuningJobConfig_hyperParameterTuningJobObjective :: Lens' HyperParameterTuningJobConfig (Maybe HyperParameterTuningJobObjective) Source #

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

hyperParameterTuningJobConfig_trainingJobEarlyStoppingType :: Lens' HyperParameterTuningJobConfig (Maybe TrainingJobEarlyStoppingType) Source #

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.

hyperParameterTuningJobConfig_strategy :: Lens' HyperParameterTuningJobConfig HyperParameterTuningJobStrategyType Source #

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.

hyperParameterTuningJobConfig_resourceLimits :: Lens' HyperParameterTuningJobConfig ResourceLimits Source #

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