@Generated(value="com.amazonaws:aws-java-sdk-code-generator") public class HyperParameterTuningJobConfig extends Object implements Serializable, Cloneable, StructuredPojo
Configures a hyperparameter tuning job.
Constructor and Description |
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HyperParameterTuningJobConfig() |
Modifier and Type | Method and Description |
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HyperParameterTuningJobConfig |
clone() |
boolean |
equals(Object obj) |
HyperParameterTuningJobObjective |
getHyperParameterTuningJobObjective()
The
HyperParameterTuningJobObjective specifies the objective metric used to evaluate the performance of training
jobs launched by this tuning job.
|
ParameterRanges |
getParameterRanges()
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 |
getRandomSeed()
A value used to initialize a pseudo-random number generator.
|
ResourceLimits |
getResourceLimits()
The ResourceLimits
object that specifies the maximum number of training and parallel training jobs that can be used for this
hyperparameter tuning job.
|
String |
getStrategy()
Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job
it launches.
|
HyperParameterTuningJobStrategyConfig |
getStrategyConfig()
The configuration for the
Hyperband optimization strategy. |
String |
getTrainingJobEarlyStoppingType()
Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job.
|
TuningJobCompletionCriteria |
getTuningJobCompletionCriteria()
The tuning job's completion criteria.
|
int |
hashCode() |
void |
marshall(ProtocolMarshaller protocolMarshaller)
Marshalls this structured data using the given
ProtocolMarshaller . |
void |
setHyperParameterTuningJobObjective(HyperParameterTuningJobObjective hyperParameterTuningJobObjective)
The
HyperParameterTuningJobObjective specifies the objective metric used to evaluate the performance of training
jobs launched by this tuning job.
|
void |
setParameterRanges(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.
|
void |
setRandomSeed(Integer randomSeed)
A value used to initialize a pseudo-random number generator.
|
void |
setResourceLimits(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.
|
void |
setStrategy(String strategy)
Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job
it launches.
|
void |
setStrategyConfig(HyperParameterTuningJobStrategyConfig strategyConfig)
The configuration for the
Hyperband optimization strategy. |
void |
setTrainingJobEarlyStoppingType(String trainingJobEarlyStoppingType)
Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job.
|
void |
setTuningJobCompletionCriteria(TuningJobCompletionCriteria tuningJobCompletionCriteria)
The tuning job's completion criteria.
|
String |
toString()
Returns a string representation of this object.
|
HyperParameterTuningJobConfig |
withHyperParameterTuningJobObjective(HyperParameterTuningJobObjective hyperParameterTuningJobObjective)
The
HyperParameterTuningJobObjective specifies the objective metric used to evaluate the performance of training
jobs launched by this tuning job.
|
HyperParameterTuningJobConfig |
withParameterRanges(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.
|
HyperParameterTuningJobConfig |
withRandomSeed(Integer randomSeed)
A value used to initialize a pseudo-random number generator.
|
HyperParameterTuningJobConfig |
withResourceLimits(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.
|
HyperParameterTuningJobConfig |
withStrategy(HyperParameterTuningJobStrategyType strategy)
Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job
it launches.
|
HyperParameterTuningJobConfig |
withStrategy(String strategy)
Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job
it launches.
|
HyperParameterTuningJobConfig |
withStrategyConfig(HyperParameterTuningJobStrategyConfig strategyConfig)
The configuration for the
Hyperband optimization strategy. |
HyperParameterTuningJobConfig |
withTrainingJobEarlyStoppingType(String trainingJobEarlyStoppingType)
Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job.
|
HyperParameterTuningJobConfig |
withTrainingJobEarlyStoppingType(TrainingJobEarlyStoppingType trainingJobEarlyStoppingType)
Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job.
|
HyperParameterTuningJobConfig |
withTuningJobCompletionCriteria(TuningJobCompletionCriteria tuningJobCompletionCriteria)
The tuning job's completion criteria.
|
public void setStrategy(String 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.
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.HyperParameterTuningJobStrategyType
public String getStrategy()
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.
HyperParameterTuningJobStrategyType
public HyperParameterTuningJobConfig withStrategy(String 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.
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.HyperParameterTuningJobStrategyType
public HyperParameterTuningJobConfig withStrategy(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.
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.HyperParameterTuningJobStrategyType
public void setStrategyConfig(HyperParameterTuningJobStrategyConfig strategyConfig)
The configuration for the Hyperband
optimization strategy. This parameter should be provided only if
Hyperband
is selected as the strategy for HyperParameterTuningJobConfig
.
strategyConfig
- The configuration for the Hyperband
optimization strategy. This parameter should be provided
only if Hyperband
is selected as the strategy for HyperParameterTuningJobConfig
.public HyperParameterTuningJobStrategyConfig getStrategyConfig()
The configuration for the Hyperband
optimization strategy. This parameter should be provided only if
Hyperband
is selected as the strategy for HyperParameterTuningJobConfig
.
Hyperband
optimization strategy. This parameter should be provided
only if Hyperband
is selected as the strategy for HyperParameterTuningJobConfig
.public HyperParameterTuningJobConfig withStrategyConfig(HyperParameterTuningJobStrategyConfig strategyConfig)
The configuration for the Hyperband
optimization strategy. This parameter should be provided only if
Hyperband
is selected as the strategy for HyperParameterTuningJobConfig
.
strategyConfig
- The configuration for the Hyperband
optimization strategy. This parameter should be provided
only if Hyperband
is selected as the strategy for HyperParameterTuningJobConfig
.public void setHyperParameterTuningJobObjective(HyperParameterTuningJobObjective hyperParameterTuningJobObjective)
The HyperParameterTuningJobObjective specifies the objective metric used to evaluate the performance of training jobs launched by this tuning job.
hyperParameterTuningJobObjective
- The HyperParameterTuningJobObjective specifies the objective metric used to evaluate the performance of
training jobs launched by this tuning job.public HyperParameterTuningJobObjective getHyperParameterTuningJobObjective()
The HyperParameterTuningJobObjective specifies the objective metric used to evaluate the performance of training jobs launched by this tuning job.
public HyperParameterTuningJobConfig withHyperParameterTuningJobObjective(HyperParameterTuningJobObjective hyperParameterTuningJobObjective)
The HyperParameterTuningJobObjective specifies the objective metric used to evaluate the performance of training jobs launched by this tuning job.
hyperParameterTuningJobObjective
- The HyperParameterTuningJobObjective specifies the objective metric used to evaluate the performance of
training jobs launched by this tuning job.public void setResourceLimits(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.
resourceLimits
- The ResourceLimits
object that specifies the maximum number of training and parallel training jobs that can be used for
this hyperparameter tuning job.public ResourceLimits getResourceLimits()
The ResourceLimits object that specifies the maximum number of training and parallel training jobs that can be used for this hyperparameter tuning job.
public HyperParameterTuningJobConfig withResourceLimits(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.
resourceLimits
- The ResourceLimits
object that specifies the maximum number of training and parallel training jobs that can be used for
this hyperparameter tuning job.public void setParameterRanges(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.
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.public ParameterRanges getParameterRanges()
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.
public HyperParameterTuningJobConfig withParameterRanges(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.
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.public void setTrainingJobEarlyStoppingType(String 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 be OFF
to use Hyperband
. This parameter
can take on one of the following values (the default value is OFF
):
Training jobs launched by the hyperparameter tuning job do not use early stopping.
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.
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 be OFF
to use Hyperband
. This
parameter can take on one of the following values (the default value is OFF
):
Training jobs launched by the hyperparameter tuning job do not use early stopping.
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.
TrainingJobEarlyStoppingType
public String getTrainingJobEarlyStoppingType()
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
):
Training jobs launched by the hyperparameter tuning job do not use early stopping.
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.
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
):
Training jobs launched by the hyperparameter tuning job do not use early stopping.
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.
TrainingJobEarlyStoppingType
public HyperParameterTuningJobConfig withTrainingJobEarlyStoppingType(String 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 be OFF
to use Hyperband
. This parameter
can take on one of the following values (the default value is OFF
):
Training jobs launched by the hyperparameter tuning job do not use early stopping.
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.
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 be OFF
to use Hyperband
. This
parameter can take on one of the following values (the default value is OFF
):
Training jobs launched by the hyperparameter tuning job do not use early stopping.
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.
TrainingJobEarlyStoppingType
public HyperParameterTuningJobConfig withTrainingJobEarlyStoppingType(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 be OFF
to use Hyperband
. This parameter
can take on one of the following values (the default value is OFF
):
Training jobs launched by the hyperparameter tuning job do not use early stopping.
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.
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 be OFF
to use Hyperband
. This
parameter can take on one of the following values (the default value is OFF
):
Training jobs launched by the hyperparameter tuning job do not use early stopping.
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.
TrainingJobEarlyStoppingType
public void setTuningJobCompletionCriteria(TuningJobCompletionCriteria tuningJobCompletionCriteria)
The tuning job's completion criteria.
tuningJobCompletionCriteria
- The tuning job's completion criteria.public TuningJobCompletionCriteria getTuningJobCompletionCriteria()
The tuning job's completion criteria.
public HyperParameterTuningJobConfig withTuningJobCompletionCriteria(TuningJobCompletionCriteria tuningJobCompletionCriteria)
The tuning job's completion criteria.
tuningJobCompletionCriteria
- The tuning job's completion criteria.public void setRandomSeed(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.
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.public Integer getRandomSeed()
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.
public HyperParameterTuningJobConfig withRandomSeed(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.
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.public String toString()
toString
in class Object
Object.toString()
public HyperParameterTuningJobConfig clone()
public void marshall(ProtocolMarshaller protocolMarshaller)
StructuredPojo
ProtocolMarshaller
.marshall
in interface StructuredPojo
protocolMarshaller
- Implementation of ProtocolMarshaller
used to marshall this object's data.