com.amazonaws.services.sagemaker.model

Class CreateHyperParameterTuningJobRequest

java.lang.Object

com.amazonaws.AmazonWebServiceRequest

com.amazonaws.services.sagemaker.model.CreateHyperParameterTuningJobRequest

All Implemented Interfaces:

HandlerContextAware, ReadLimitInfo, Serializable, Cloneable

@Generated(value="com.amazonaws:aws-java-sdk-code-generator")
public class CreateHyperParameterTuningJobRequest
extends AmazonWebServiceRequest
implements Serializable, Cloneable

See Also:

AWS API Documentation, Serialized Form

Field Summary

Fields inherited from class com.amazonaws.AmazonWebServiceRequest

NOOP

Constructor Summary

Constructors

Constructor and Description
CreateHyperParameterTuningJobRequest()

Method Summary

Modifier and Type	Method and Description
CreateHyperParameterTuningJobRequest	clone() Creates a shallow clone of this object for all fields except the handler context.
boolean	equals(Object obj)
Autotune	getAutotune() Configures SageMaker Automatic model tuning (AMT) to automatically find optimal parameters for the following fields:
HyperParameterTuningJobConfig	getHyperParameterTuningJobConfig() The HyperParameterTuningJobConfig object that describes the tuning job, including the search strategy, the objective metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the tuning job.
String	getHyperParameterTuningJobName() The name of the tuning job.
List<Tag>	getTags() An array of key-value pairs.
HyperParameterTrainingJobDefinition	getTrainingJobDefinition() The HyperParameterTrainingJobDefinition object that describes the training jobs that this tuning job launches, including static hyperparameters, input data configuration, output data configuration, resource configuration, and stopping condition.
List<HyperParameterTrainingJobDefinition>	getTrainingJobDefinitions() A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.
HyperParameterTuningJobWarmStartConfig	getWarmStartConfig() Specifies the configuration for starting the hyperparameter tuning job using one or more previous tuning jobs as a starting point.
int	hashCode()
void	setAutotune(Autotune autotune) Configures SageMaker Automatic model tuning (AMT) to automatically find optimal parameters for the following fields:
void	setHyperParameterTuningJobConfig(HyperParameterTuningJobConfig hyperParameterTuningJobConfig) The HyperParameterTuningJobConfig object that describes the tuning job, including the search strategy, the objective metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the tuning job.
void	setHyperParameterTuningJobName(String hyperParameterTuningJobName) The name of the tuning job.
void	setTags(Collection<Tag> tags) An array of key-value pairs.
void	setTrainingJobDefinition(HyperParameterTrainingJobDefinition trainingJobDefinition) The HyperParameterTrainingJobDefinition object that describes the training jobs that this tuning job launches, including static hyperparameters, input data configuration, output data configuration, resource configuration, and stopping condition.
void	setTrainingJobDefinitions(Collection<HyperParameterTrainingJobDefinition> trainingJobDefinitions) A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.
void	setWarmStartConfig(HyperParameterTuningJobWarmStartConfig warmStartConfig) Specifies the configuration for starting the hyperparameter tuning job using one or more previous tuning jobs as a starting point.
String	toString() Returns a string representation of this object.
CreateHyperParameterTuningJobRequest	withAutotune(Autotune autotune) Configures SageMaker Automatic model tuning (AMT) to automatically find optimal parameters for the following fields:
CreateHyperParameterTuningJobRequest	withHyperParameterTuningJobConfig(HyperParameterTuningJobConfig hyperParameterTuningJobConfig) The HyperParameterTuningJobConfig object that describes the tuning job, including the search strategy, the objective metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the tuning job.
CreateHyperParameterTuningJobRequest	withHyperParameterTuningJobName(String hyperParameterTuningJobName) The name of the tuning job.
CreateHyperParameterTuningJobRequest	withTags(Collection<Tag> tags) An array of key-value pairs.
CreateHyperParameterTuningJobRequest	withTags(Tag... tags) An array of key-value pairs.
CreateHyperParameterTuningJobRequest	withTrainingJobDefinition(HyperParameterTrainingJobDefinition trainingJobDefinition) The HyperParameterTrainingJobDefinition object that describes the training jobs that this tuning job launches, including static hyperparameters, input data configuration, output data configuration, resource configuration, and stopping condition.
CreateHyperParameterTuningJobRequest	withTrainingJobDefinitions(Collection<HyperParameterTrainingJobDefinition> trainingJobDefinitions) A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.
CreateHyperParameterTuningJobRequest	withTrainingJobDefinitions(HyperParameterTrainingJobDefinition... trainingJobDefinitions) A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.
CreateHyperParameterTuningJobRequest	withWarmStartConfig(HyperParameterTuningJobWarmStartConfig warmStartConfig) Specifies the configuration for starting the hyperparameter tuning job using one or more previous tuning jobs as a starting point.

Methods inherited from class com.amazonaws.AmazonWebServiceRequest

addHandlerContext, getCloneRoot, getCloneSource, getCustomQueryParameters, getCustomRequestHeaders, getGeneralProgressListener, getHandlerContext, getReadLimit, getRequestClientOptions, getRequestCredentials, getRequestCredentialsProvider, getRequestMetricCollector, getSdkClientExecutionTimeout, getSdkRequestTimeout, putCustomQueryParameter, putCustomRequestHeader, setGeneralProgressListener, setRequestCredentials, setRequestCredentialsProvider, setRequestMetricCollector, setSdkClientExecutionTimeout, setSdkRequestTimeout, withGeneralProgressListener, withRequestCredentialsProvider, withRequestMetricCollector, withSdkClientExecutionTimeout, withSdkRequestTimeout

Methods inherited from class java.lang.Object

getClass, notify, notifyAll, wait, wait, wait

Constructor Detail

CreateHyperParameterTuningJobRequest

public CreateHyperParameterTuningJobRequest()

Method Detail

setHyperParameterTuningJobName

public void setHyperParameterTuningJobName(String hyperParameterTuningJobName)

The name of the tuning job. This name is the prefix for the names of all training jobs that this tuning job launches. The name must be unique within the same Amazon Web Services account and Amazon Web Services Region. The name must have 1 to 32 characters. Valid characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case sensitive.

Parameters:

hyperParameterTuningJobName - The name of the tuning job. This name is the prefix for the names of all training jobs that this tuning job launches. The name must be unique within the same Amazon Web Services account and Amazon Web Services Region. The name must have 1 to 32 characters. Valid characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case sensitive.

getHyperParameterTuningJobName

public String getHyperParameterTuningJobName()

The name of the tuning job. This name is the prefix for the names of all training jobs that this tuning job launches. The name must be unique within the same Amazon Web Services account and Amazon Web Services Region. The name must have 1 to 32 characters. Valid characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case sensitive.

Returns:

The name of the tuning job. This name is the prefix for the names of all training jobs that this tuning job launches. The name must be unique within the same Amazon Web Services account and Amazon Web Services Region. The name must have 1 to 32 characters. Valid characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case sensitive.

withHyperParameterTuningJobName

public CreateHyperParameterTuningJobRequest withHyperParameterTuningJobName(String hyperParameterTuningJobName)

The name of the tuning job. This name is the prefix for the names of all training jobs that this tuning job launches. The name must be unique within the same Amazon Web Services account and Amazon Web Services Region. The name must have 1 to 32 characters. Valid characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case sensitive.

Parameters:

hyperParameterTuningJobName - The name of the tuning job. This name is the prefix for the names of all training jobs that this tuning job launches. The name must be unique within the same Amazon Web Services account and Amazon Web Services Region. The name must have 1 to 32 characters. Valid characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case sensitive.

Returns:

Returns a reference to this object so that method calls can be chained together.

setHyperParameterTuningJobConfig

public void setHyperParameterTuningJobConfig(HyperParameterTuningJobConfig hyperParameterTuningJobConfig)

The HyperParameterTuningJobConfig object that describes the tuning job, including the search strategy, the objective metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the tuning job. For more information, see How Hyperparameter Tuning Works.

Parameters:

hyperParameterTuningJobConfig - The HyperParameterTuningJobConfig object that describes the tuning job, including the search strategy, the objective metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the tuning job. For more information, see How Hyperparameter Tuning Works.

getHyperParameterTuningJobConfig

public HyperParameterTuningJobConfig getHyperParameterTuningJobConfig()

The HyperParameterTuningJobConfig object that describes the tuning job, including the search strategy, the objective metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the tuning job. For more information, see How Hyperparameter Tuning Works.

Returns:

The HyperParameterTuningJobConfig object that describes the tuning job, including the search strategy, the objective metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the tuning job. For more information, see How Hyperparameter Tuning Works.

withHyperParameterTuningJobConfig

public CreateHyperParameterTuningJobRequest withHyperParameterTuningJobConfig(HyperParameterTuningJobConfig hyperParameterTuningJobConfig)

The HyperParameterTuningJobConfig object that describes the tuning job, including the search strategy, the objective metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the tuning job. For more information, see How Hyperparameter Tuning Works.

Parameters:

hyperParameterTuningJobConfig - The HyperParameterTuningJobConfig object that describes the tuning job, including the search strategy, the objective metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the tuning job. For more information, see How Hyperparameter Tuning Works.

Returns:

Returns a reference to this object so that method calls can be chained together.

setTrainingJobDefinition

public void setTrainingJobDefinition(HyperParameterTrainingJobDefinition trainingJobDefinition)

The HyperParameterTrainingJobDefinition object that describes the training jobs that this tuning job launches, including static hyperparameters, input data configuration, output data configuration, resource configuration, and stopping condition.

Parameters:

trainingJobDefinition - The HyperParameterTrainingJobDefinition object that describes the training jobs that this tuning job launches, including static hyperparameters, input data configuration, output data configuration, resource configuration, and stopping condition.

getTrainingJobDefinition

public HyperParameterTrainingJobDefinition getTrainingJobDefinition()

The HyperParameterTrainingJobDefinition object that describes the training jobs that this tuning job launches, including static hyperparameters, input data configuration, output data configuration, resource configuration, and stopping condition.

Returns:

The HyperParameterTrainingJobDefinition object that describes the training jobs that this tuning job launches, including static hyperparameters, input data configuration, output data configuration, resource configuration, and stopping condition.

withTrainingJobDefinition

public CreateHyperParameterTuningJobRequest withTrainingJobDefinition(HyperParameterTrainingJobDefinition trainingJobDefinition)

The HyperParameterTrainingJobDefinition object that describes the training jobs that this tuning job launches, including static hyperparameters, input data configuration, output data configuration, resource configuration, and stopping condition.

Parameters:

trainingJobDefinition - The HyperParameterTrainingJobDefinition object that describes the training jobs that this tuning job launches, including static hyperparameters, input data configuration, output data configuration, resource configuration, and stopping condition.

Returns:

Returns a reference to this object so that method calls can be chained together.

getTrainingJobDefinitions

public List<HyperParameterTrainingJobDefinition> getTrainingJobDefinitions()

A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.

Returns:

A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.

setTrainingJobDefinitions

public void setTrainingJobDefinitions(Collection<HyperParameterTrainingJobDefinition> trainingJobDefinitions)

A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.

Parameters:

trainingJobDefinitions - A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.

withTrainingJobDefinitions

public CreateHyperParameterTuningJobRequest withTrainingJobDefinitions(HyperParameterTrainingJobDefinition... trainingJobDefinitions)

A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.

NOTE: This method appends the values to the existing list (if any). Use setTrainingJobDefinitions(java.util.Collection) or withTrainingJobDefinitions(java.util.Collection) if you want to override the existing values.

Parameters:

trainingJobDefinitions - A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.

Returns:

Returns a reference to this object so that method calls can be chained together.

withTrainingJobDefinitions

public CreateHyperParameterTuningJobRequest withTrainingJobDefinitions(Collection<HyperParameterTrainingJobDefinition> trainingJobDefinitions)

A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.

Parameters:

trainingJobDefinitions - A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.

Returns:

Returns a reference to this object so that method calls can be chained together.

setWarmStartConfig

public void setWarmStartConfig(HyperParameterTuningJobWarmStartConfig warmStartConfig)

Specifies the configuration for starting the hyperparameter tuning job using one or more previous tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.

All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric. If you specify IDENTICAL_DATA_AND_ALGORITHM as the WarmStartType value for the warm start configuration, the training job that performs the best in the new tuning job is compared to the best training jobs from the parent tuning jobs. From these, the training job that performs the best as measured by the objective metric is returned as the overall best training job.

All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count against the limit of training jobs for the tuning job.

Parameters:

warmStartConfig - Specifies the configuration for starting the hyperparameter tuning job using one or more previous tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.

All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric. If you specify IDENTICAL_DATA_AND_ALGORITHM as the WarmStartType value for the warm start configuration, the training job that performs the best in the new tuning job is compared to the best training jobs from the parent tuning jobs. From these, the training job that performs the best as measured by the objective metric is returned as the overall best training job.

All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count against the limit of training jobs for the tuning job.

getWarmStartConfig

public HyperParameterTuningJobWarmStartConfig getWarmStartConfig()

Specifies the configuration for starting the hyperparameter tuning job using one or more previous tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.

All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric. If you specify IDENTICAL_DATA_AND_ALGORITHM as the WarmStartType value for the warm start configuration, the training job that performs the best in the new tuning job is compared to the best training jobs from the parent tuning jobs. From these, the training job that performs the best as measured by the objective metric is returned as the overall best training job.

All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count against the limit of training jobs for the tuning job.

Returns:

Specifies the configuration for starting the hyperparameter tuning job using one or more previous tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.

All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric. If you specify IDENTICAL_DATA_AND_ALGORITHM as the WarmStartType value for the warm start configuration, the training job that performs the best in the new tuning job is compared to the best training jobs from the parent tuning jobs. From these, the training job that performs the best as measured by the objective metric is returned as the overall best training job.

All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count against the limit of training jobs for the tuning job.

withWarmStartConfig

public CreateHyperParameterTuningJobRequest withWarmStartConfig(HyperParameterTuningJobWarmStartConfig warmStartConfig)

Specifies the configuration for starting the hyperparameter tuning job using one or more previous tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.

All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric. If you specify IDENTICAL_DATA_AND_ALGORITHM as the WarmStartType value for the warm start configuration, the training job that performs the best in the new tuning job is compared to the best training jobs from the parent tuning jobs. From these, the training job that performs the best as measured by the objective metric is returned as the overall best training job.

All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count against the limit of training jobs for the tuning job.

Parameters:

warmStartConfig - Specifies the configuration for starting the hyperparameter tuning job using one or more previous tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.

All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric. If you specify IDENTICAL_DATA_AND_ALGORITHM as the WarmStartType value for the warm start configuration, the training job that performs the best in the new tuning job is compared to the best training jobs from the parent tuning jobs. From these, the training job that performs the best as measured by the objective metric is returned as the overall best training job.

All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count against the limit of training jobs for the tuning job.

Returns:

Returns a reference to this object so that method calls can be chained together.

getTags

public List<Tag> getTags()

An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.

Returns:

An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.

setTags

public void setTags(Collection<Tag> tags)

An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.

Parameters:

tags - An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.

withTags

public CreateHyperParameterTuningJobRequest withTags(Tag... tags)

An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.

NOTE: This method appends the values to the existing list (if any). Use setTags(java.util.Collection) or withTags(java.util.Collection) if you want to override the existing values.

Parameters:

tags - An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.

Returns:

Returns a reference to this object so that method calls can be chained together.

withTags

public CreateHyperParameterTuningJobRequest withTags(Collection<Tag> tags)

An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.

Parameters:

tags - An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.

Returns:

Returns a reference to this object so that method calls can be chained together.

setAutotune

public void setAutotune(Autotune autotune)

Configures SageMaker Automatic model tuning (AMT) to automatically find optimal parameters for the following fields:

• ParameterRanges: The names and ranges of parameters that a hyperparameter tuning job can optimize.

• ResourceLimits: The maximum resources that can be used for a training job. These resources include the maximum number of training jobs, the maximum runtime of a tuning job, and the maximum number of training jobs to run at the same time.

• TrainingJobEarlyStoppingType: A flag that specifies whether or not to use early stopping for training jobs launched by a hyperparameter tuning job.

• RetryStrategy: The number of times to retry a training job.

• Strategy: Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training jobs that it launches.

• ConvergenceDetected: A flag to indicate that Automatic model tuning (AMT) has detected model convergence.

Parameters:

autotune - Configures SageMaker Automatic model tuning (AMT) to automatically find optimal parameters for the following fields:

• ParameterRanges: The names and ranges of parameters that a hyperparameter tuning job can optimize.

• ResourceLimits< /a>: The maximum resources that can be used for a training job. These resources include the maximum number of training jobs, the maximum runtime of a tuning job, and the maximum number of training jobs to run at the same time.

• TrainingJobEarlyStoppingType: A flag that specifies whether or not to use early stopping for training jobs launched by a hyperparameter tuning job.

• RetryStrategy: The number of times to retry a training job.

• Strategy: Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training jobs that it launches.

• ConvergenceDetected: A flag to indicate that Automatic model tuning (AMT) has detected model convergence.

getAutotune

public Autotune getAutotune()

Configures SageMaker Automatic model tuning (AMT) to automatically find optimal parameters for the following fields:

• ParameterRanges: The names and ranges of parameters that a hyperparameter tuning job can optimize.

• ResourceLimits: The maximum resources that can be used for a training job. These resources include the maximum number of training jobs, the maximum runtime of a tuning job, and the maximum number of training jobs to run at the same time.

• TrainingJobEarlyStoppingType: A flag that specifies whether or not to use early stopping for training jobs launched by a hyperparameter tuning job.

• RetryStrategy: The number of times to retry a training job.

• Strategy: Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training jobs that it launches.

• ConvergenceDetected: A flag to indicate that Automatic model tuning (AMT) has detected model convergence.

Returns:

Configures SageMaker Automatic model tuning (AMT) to automatically find optimal parameters for the following fields:

• ParameterRanges: The names and ranges of parameters that a hyperparameter tuning job can optimize.

• ResourceLimits : The maximum resources that can be used for a training job. These resources include the maximum number of training jobs, the maximum runtime of a tuning job, and the maximum number of training jobs to run at the same time.

• TrainingJobEarlyStoppingType: A flag that specifies whether or not to use early stopping for training jobs launched by a hyperparameter tuning job.

• RetryStrategy: The number of times to retry a training job.

• Strategy: Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training jobs that it launches.

• ConvergenceDetected: A flag to indicate that Automatic model tuning (AMT) has detected model convergence.

withAutotune

public CreateHyperParameterTuningJobRequest withAutotune(Autotune autotune)

Configures SageMaker Automatic model tuning (AMT) to automatically find optimal parameters for the following fields:

• ParameterRanges: The names and ranges of parameters that a hyperparameter tuning job can optimize.

• ResourceLimits: The maximum resources that can be used for a training job. These resources include the maximum number of training jobs, the maximum runtime of a tuning job, and the maximum number of training jobs to run at the same time.

• TrainingJobEarlyStoppingType: A flag that specifies whether or not to use early stopping for training jobs launched by a hyperparameter tuning job.

• RetryStrategy: The number of times to retry a training job.

• Strategy: Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training jobs that it launches.

• ConvergenceDetected: A flag to indicate that Automatic model tuning (AMT) has detected model convergence.

Parameters:

autotune - Configures SageMaker Automatic model tuning (AMT) to automatically find optimal parameters for the following fields:

• ParameterRanges: The names and ranges of parameters that a hyperparameter tuning job can optimize.

• ResourceLimits< /a>: The maximum resources that can be used for a training job. These resources include the maximum number of training jobs, the maximum runtime of a tuning job, and the maximum number of training jobs to run at the same time.

• TrainingJobEarlyStoppingType: A flag that specifies whether or not to use early stopping for training jobs launched by a hyperparameter tuning job.

• RetryStrategy: The number of times to retry a training job.

• Strategy: Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training jobs that it launches.

• ConvergenceDetected: A flag to indicate that Automatic model tuning (AMT) has detected model convergence.

Returns:

Returns a reference to this object so that method calls can be chained together.

toString

public 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.

Overrides:

toString in class Object

Returns:

A string representation of this object.

See Also:

Object.toString()

equals

public boolean equals(Object obj)

Overrides:

equals in class Object

hashCode

public int hashCode()

Overrides:

hashCode in class Object

clone

public CreateHyperParameterTuningJobRequest clone()

Description copied from class: AmazonWebServiceRequest

Creates a shallow clone of this object for all fields except the handler context. Explicitly does not clone the deep structure of the other fields in the message.

Overrides:

clone in class AmazonWebServiceRequest

See Also:

Object.clone()