Class HyperParameterTuningJobConfig
- java.lang.Object
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- software.amazon.awssdk.services.sagemaker.model.HyperParameterTuningJobConfig
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- All Implemented Interfaces:
Serializable
,SdkPojo
,ToCopyableBuilder<HyperParameterTuningJobConfig.Builder,HyperParameterTuningJobConfig>
@Generated("software.amazon.awssdk:codegen") public final class HyperParameterTuningJobConfig extends Object implements SdkPojo, Serializable, ToCopyableBuilder<HyperParameterTuningJobConfig.Builder,HyperParameterTuningJobConfig>
Configures a hyperparameter tuning job.
- See Also:
- Serialized Form
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Nested Class Summary
Nested Classes Modifier and Type Class Description static interface
HyperParameterTuningJobConfig.Builder
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Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description static HyperParameterTuningJobConfig.Builder
builder()
boolean
equals(Object obj)
boolean
equalsBySdkFields(Object obj)
<T> Optional<T>
getValueForField(String fieldName, Class<T> clazz)
int
hashCode()
HyperParameterTuningJobObjective
hyperParameterTuningJobObjective()
The HyperParameterTuningJobObjective specifies the objective metric used to evaluate the performance of training jobs launched by this tuning job.ParameterRanges
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.Integer
randomSeed()
A value used to initialize a pseudo-random number generator.ResourceLimits
resourceLimits()
The ResourceLimits object that specifies the maximum number of training and parallel training jobs that can be used for this hyperparameter tuning job.List<SdkField<?>>
sdkFields()
static Class<? extends HyperParameterTuningJobConfig.Builder>
serializableBuilderClass()
HyperParameterTuningJobStrategyType
strategy()
Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job it launches.String
strategyAsString()
Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job it launches.HyperParameterTuningJobStrategyConfig
strategyConfig()
The configuration for theHyperband
optimization strategy.HyperParameterTuningJobConfig.Builder
toBuilder()
String
toString()
Returns a string representation of this object.TrainingJobEarlyStoppingType
trainingJobEarlyStoppingType()
Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job.String
trainingJobEarlyStoppingTypeAsString()
Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job.TuningJobCompletionCriteria
tuningJobCompletionCriteria()
The tuning job's completion criteria.-
Methods inherited from class java.lang.Object
clone, finalize, getClass, notify, notifyAll, wait, wait, wait
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Methods inherited from interface software.amazon.awssdk.utils.builder.ToCopyableBuilder
copy
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Method Detail
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strategy
public final HyperParameterTuningJobStrategyType 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.
If the service returns an enum value that is not available in the current SDK version,
strategy
will returnHyperParameterTuningJobStrategyType.UNKNOWN_TO_SDK_VERSION
. The raw value returned by the service is available fromstrategyAsString()
.- Returns:
- 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.
- See Also:
HyperParameterTuningJobStrategyType
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strategyAsString
public final String strategyAsString()
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.
If the service returns an enum value that is not available in the current SDK version,
strategy
will returnHyperParameterTuningJobStrategyType.UNKNOWN_TO_SDK_VERSION
. The raw value returned by the service is available fromstrategyAsString()
.- Returns:
- 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.
- See Also:
HyperParameterTuningJobStrategyType
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strategyConfig
public final HyperParameterTuningJobStrategyConfig strategyConfig()
The configuration for the
Hyperband
optimization strategy. This parameter should be provided only ifHyperband
is selected as the strategy forHyperParameterTuningJobConfig
.- Returns:
- The configuration for the
Hyperband
optimization strategy. This parameter should be provided only ifHyperband
is selected as the strategy forHyperParameterTuningJobConfig
.
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hyperParameterTuningJobObjective
public final HyperParameterTuningJobObjective hyperParameterTuningJobObjective()
The HyperParameterTuningJobObjective specifies the objective metric used to evaluate the performance of training jobs launched by this tuning job.
- Returns:
- The HyperParameterTuningJobObjective specifies the objective metric used to evaluate the performance of training jobs launched by this tuning job.
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resourceLimits
public final ResourceLimits resourceLimits()
The ResourceLimits object that specifies the maximum number of training and parallel training jobs that can be used for this hyperparameter tuning job.
- Returns:
- The ResourceLimits object that specifies the maximum number of training and parallel training jobs that can be used for this hyperparameter tuning job.
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parameterRanges
public final ParameterRanges 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.
- Returns:
- 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.
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trainingJobEarlyStoppingType
public final TrainingJobEarlyStoppingType trainingJobEarlyStoppingType()
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 beOFF
to useHyperband
. This parameter can take on one of the following values (the default value isOFF
):- OFF
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Training jobs launched by the hyperparameter tuning job do not use early stopping.
- AUTO
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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.
If the service returns an enum value that is not available in the current SDK version,
trainingJobEarlyStoppingType
will returnTrainingJobEarlyStoppingType.UNKNOWN_TO_SDK_VERSION
. The raw value returned by the service is available fromtrainingJobEarlyStoppingTypeAsString()
.- Returns:
- 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 beOFF
to 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.
- See Also:
TrainingJobEarlyStoppingType
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trainingJobEarlyStoppingTypeAsString
public final String trainingJobEarlyStoppingTypeAsString()
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 beOFF
to 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.
If the service returns an enum value that is not available in the current SDK version,
trainingJobEarlyStoppingType
will returnTrainingJobEarlyStoppingType.UNKNOWN_TO_SDK_VERSION
. The raw value returned by the service is available fromtrainingJobEarlyStoppingTypeAsString()
.- Returns:
- 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 beOFF
to 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.
- See Also:
TrainingJobEarlyStoppingType
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tuningJobCompletionCriteria
public final TuningJobCompletionCriteria tuningJobCompletionCriteria()
The tuning job's completion criteria.
- Returns:
- The tuning job's completion criteria.
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randomSeed
public final Integer 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.
- Returns:
- 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.
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toBuilder
public HyperParameterTuningJobConfig.Builder toBuilder()
- Specified by:
toBuilder
in interfaceToCopyableBuilder<HyperParameterTuningJobConfig.Builder,HyperParameterTuningJobConfig>
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builder
public static HyperParameterTuningJobConfig.Builder builder()
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serializableBuilderClass
public static Class<? extends HyperParameterTuningJobConfig.Builder> serializableBuilderClass()
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equalsBySdkFields
public final boolean equalsBySdkFields(Object obj)
- Specified by:
equalsBySdkFields
in interfaceSdkPojo
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toString
public final String toString()
Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be redacted from this string using a placeholder value.
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