HyperParameterTrainingJobDefinition (AWS SDK for Java

java.lang.Object
- com.amazonaws.services.sagemaker.model.HyperParameterTrainingJobDefinition

All Implemented Interfaces:: StructuredPojo, Serializable, Cloneable

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

Defines the training jobs launched by a hyperparameter tuning job.

See Also:: AWS API Documentation, Serialized Form

Constructor Summary

Constructors
Constructor and Description

HyperParameterTrainingJobDefinition()

Constructors
Constructor and Description
`HyperParameterTrainingJobDefinition()`

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`HyperParameterTrainingJobDefinition`	`addStaticHyperParametersEntry(String key, String value)`
`HyperParameterTrainingJobDefinition`	`clearStaticHyperParametersEntries()` Removes all the entries added into StaticHyperParameters.
`HyperParameterTrainingJobDefinition`	`clone()`
`boolean`	`equals(Object obj)`
`HyperParameterAlgorithmSpecification`	`getAlgorithmSpecification()` The HyperParameterAlgorithmSpecification object that specifies the algorithm to use for the training jobs that the tuning job launches.
`List<Channel>`	`getInputDataConfig()` An array of Channel objects that specify the input for the training jobs that the tuning job launches.
`OutputDataConfig`	`getOutputDataConfig()` Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
`ResourceConfig`	`getResourceConfig()` The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
`String`	`getRoleArn()` The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
`Map<String,String>`	`getStaticHyperParameters()` Specifies the values of hyperparameters that do not change for the tuning job.
`StoppingCondition`	`getStoppingCondition()` Sets a maximum duration for the training jobs that the tuning job launches.
`VpcConfig`	`getVpcConfig()` The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to.
`int`	`hashCode()`
`void`	`marshall(ProtocolMarshaller protocolMarshaller)` Marshalls this structured data using the given `ProtocolMarshaller`.
`void`	`setAlgorithmSpecification(HyperParameterAlgorithmSpecification algorithmSpecification)` The HyperParameterAlgorithmSpecification object that specifies the algorithm to use for the training jobs that the tuning job launches.
`void`	`setInputDataConfig(Collection<Channel> inputDataConfig)` An array of Channel objects that specify the input for the training jobs that the tuning job launches.
`void`	`setOutputDataConfig(OutputDataConfig outputDataConfig)` Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
`void`	`setResourceConfig(ResourceConfig resourceConfig)` The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
`void`	`setRoleArn(String roleArn)` The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
`void`	`setStaticHyperParameters(Map<String,String> staticHyperParameters)` Specifies the values of hyperparameters that do not change for the tuning job.
`void`	`setStoppingCondition(StoppingCondition stoppingCondition)` Sets a maximum duration for the training jobs that the tuning job launches.
`void`	`setVpcConfig(VpcConfig vpcConfig)` The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to.
`String`	`toString()` Returns a string representation of this object; useful for testing and debugging.
`HyperParameterTrainingJobDefinition`	`withAlgorithmSpecification(HyperParameterAlgorithmSpecification algorithmSpecification)` The HyperParameterAlgorithmSpecification object that specifies the algorithm to use for the training jobs that the tuning job launches.
`HyperParameterTrainingJobDefinition`	`withInputDataConfig(Channel... inputDataConfig)` An array of Channel objects that specify the input for the training jobs that the tuning job launches.
`HyperParameterTrainingJobDefinition`	`withInputDataConfig(Collection<Channel> inputDataConfig)` An array of Channel objects that specify the input for the training jobs that the tuning job launches.
`HyperParameterTrainingJobDefinition`	`withOutputDataConfig(OutputDataConfig outputDataConfig)` Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
`HyperParameterTrainingJobDefinition`	`withResourceConfig(ResourceConfig resourceConfig)` The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
`HyperParameterTrainingJobDefinition`	`withRoleArn(String roleArn)` The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
`HyperParameterTrainingJobDefinition`	`withStaticHyperParameters(Map<String,String> staticHyperParameters)` Specifies the values of hyperparameters that do not change for the tuning job.
`HyperParameterTrainingJobDefinition`	`withStoppingCondition(StoppingCondition stoppingCondition)` Sets a maximum duration for the training jobs that the tuning job launches.
`HyperParameterTrainingJobDefinition`	`withVpcConfig(VpcConfig vpcConfig)` The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to.

Methods inherited from class java.lang.Object
getClass, notify, notifyAll, wait, wait, wait

- Constructor Detail
  - HyperParameterTrainingJobDefinition
```
public HyperParameterTrainingJobDefinition()
```
- Method Detail
  - getStaticHyperParameters
```
public Map<String,String> getStaticHyperParameters()
```
    Specifies the values of hyperparameters that do not change for the tuning job.
    
    Returns:
    
    Specifies the values of hyperparameters that do not change for the tuning job.
  - setStaticHyperParameters
```
public void setStaticHyperParameters(Map<String,String> staticHyperParameters)
```
    Specifies the values of hyperparameters that do not change for the tuning job.
    
    Parameters:
    
    staticHyperParameters - Specifies the values of hyperparameters that do not change for the tuning job.
  - withStaticHyperParameters
```
public HyperParameterTrainingJobDefinition withStaticHyperParameters(Map<String,String> staticHyperParameters)
```
    Specifies the values of hyperparameters that do not change for the tuning job.
    
    Parameters:
    
    staticHyperParameters - Specifies the values of hyperparameters that do not change for the tuning job.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - addStaticHyperParametersEntry
```
public HyperParameterTrainingJobDefinition addStaticHyperParametersEntry(String key,
                                                                         String value)
```
  - clearStaticHyperParametersEntries
```
public HyperParameterTrainingJobDefinition clearStaticHyperParametersEntries()
```
    Removes all the entries added into StaticHyperParameters.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setAlgorithmSpecification
```
public void setAlgorithmSpecification(HyperParameterAlgorithmSpecification algorithmSpecification)
```
    The HyperParameterAlgorithmSpecification object that specifies the algorithm to use for the training jobs that the tuning job launches.
    
    Parameters:
    
    algorithmSpecification - The HyperParameterAlgorithmSpecification object that specifies the algorithm to use for the training jobs that the tuning job launches.
  - getAlgorithmSpecification
```
public HyperParameterAlgorithmSpecification getAlgorithmSpecification()
```
    The HyperParameterAlgorithmSpecification object that specifies the algorithm to use for the training jobs that the tuning job launches.
    
    Returns:
    
    The HyperParameterAlgorithmSpecification object that specifies the algorithm to use for the training jobs that the tuning job launches.
  - withAlgorithmSpecification
```
public HyperParameterTrainingJobDefinition withAlgorithmSpecification(HyperParameterAlgorithmSpecification algorithmSpecification)
```
    The HyperParameterAlgorithmSpecification object that specifies the algorithm to use for the training jobs that the tuning job launches.
    
    Parameters:
    
    algorithmSpecification - The HyperParameterAlgorithmSpecification object that specifies the algorithm to use for the training jobs that the tuning job launches.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setRoleArn
```
public void setRoleArn(String roleArn)
```
    The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
    
    Parameters:
    
    roleArn - The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
  - getRoleArn
```
public String getRoleArn()
```
    The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
    
    Returns:
    
    The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
  - withRoleArn
```
public HyperParameterTrainingJobDefinition withRoleArn(String roleArn)
```
    The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
    
    Parameters:
    
    roleArn - The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - getInputDataConfig
```
public List<Channel> getInputDataConfig()
```
    An array of Channel objects that specify the input for the training jobs that the tuning job launches.
    
    Returns:
    
    An array of Channel objects that specify the input for the training jobs that the tuning job launches.
  - setInputDataConfig
```
public void setInputDataConfig(Collection<Channel> inputDataConfig)
```
    An array of Channel objects that specify the input for the training jobs that the tuning job launches.
    
    Parameters:
    
    inputDataConfig - An array of Channel objects that specify the input for the training jobs that the tuning job launches.
  - withInputDataConfig
```
public HyperParameterTrainingJobDefinition withInputDataConfig(Channel... inputDataConfig)
```
    An array of Channel objects that specify the input for the training jobs that the tuning job launches.
    
    NOTE: This method appends the values to the existing list (if any). Use setInputDataConfig(java.util.Collection) or withInputDataConfig(java.util.Collection) if you want to override the existing values.
    
    Parameters:
    
    inputDataConfig - An array of Channel objects that specify the input for the training jobs that the tuning job launches.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - withInputDataConfig
```
public HyperParameterTrainingJobDefinition withInputDataConfig(Collection<Channel> inputDataConfig)
```
    An array of Channel objects that specify the input for the training jobs that the tuning job launches.
    
    Parameters:
    
    inputDataConfig - An array of Channel objects that specify the input for the training jobs that the tuning job launches.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setVpcConfig
```
public void setVpcConfig(VpcConfig vpcConfig)
```
    The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see train-vpc.
    
    Parameters:
    
    vpcConfig - The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see train-vpc.
  - getVpcConfig
```
public VpcConfig getVpcConfig()
```
    The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see train-vpc.
    
    Returns:
    
    The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see train-vpc.
  - withVpcConfig
```
public HyperParameterTrainingJobDefinition withVpcConfig(VpcConfig vpcConfig)
```
    The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see train-vpc.
    
    Parameters:
    
    vpcConfig - The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see train-vpc.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setOutputDataConfig
```
public void setOutputDataConfig(OutputDataConfig outputDataConfig)
```
    Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
    
    Parameters:
    
    outputDataConfig - Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
  - getOutputDataConfig
```
public OutputDataConfig getOutputDataConfig()
```
    Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
    
    Returns:
    
    Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
  - withOutputDataConfig
```
public HyperParameterTrainingJobDefinition withOutputDataConfig(OutputDataConfig outputDataConfig)
```
    Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
    
    Parameters:
    
    outputDataConfig - Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setResourceConfig
```
public void setResourceConfig(ResourceConfig resourceConfig)
```
    The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
    
    Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want Amazon SageMaker to use the storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.
    
    Parameters:
    
    resourceConfig - The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
    
    Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want Amazon SageMaker to use the storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.
  - getResourceConfig
```
public ResourceConfig getResourceConfig()
```
    The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
    
    Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want Amazon SageMaker to use the storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.
    
    Returns:
    
    The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
    
    Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want Amazon SageMaker to use the storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.
  - withResourceConfig
```
public HyperParameterTrainingJobDefinition withResourceConfig(ResourceConfig resourceConfig)
```
    The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
    
    Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want Amazon SageMaker to use the storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.
    
    Parameters:
    
    resourceConfig - The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
    
    Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want Amazon SageMaker to use the storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setStoppingCondition
```
public void setStoppingCondition(StoppingCondition stoppingCondition)
```
    Sets a maximum duration for the training jobs that the tuning job launches. Use this parameter to limit model training costs.
    
    To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal. This delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts.
    
    When Amazon SageMaker terminates a job because the stopping condition has been met, training algorithms provided by Amazon SageMaker save the intermediate results of the job.
    
    Parameters:
    
    stoppingCondition - Sets a maximum duration for the training jobs that the tuning job launches. Use this parameter to limit model training costs.
    
    To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal. This delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts.
    
    When Amazon SageMaker terminates a job because the stopping condition has been met, training algorithms provided by Amazon SageMaker save the intermediate results of the job.
  - getStoppingCondition
```
public StoppingCondition getStoppingCondition()
```
    Sets a maximum duration for the training jobs that the tuning job launches. Use this parameter to limit model training costs.
    
    To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal. This delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts.
    
    When Amazon SageMaker terminates a job because the stopping condition has been met, training algorithms provided by Amazon SageMaker save the intermediate results of the job.
    
    Returns:
    
    Sets a maximum duration for the training jobs that the tuning job launches. Use this parameter to limit model training costs.
    
    To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal. This delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts.
    
    When Amazon SageMaker terminates a job because the stopping condition has been met, training algorithms provided by Amazon SageMaker save the intermediate results of the job.
  - withStoppingCondition
```
public HyperParameterTrainingJobDefinition withStoppingCondition(StoppingCondition stoppingCondition)
```
    Sets a maximum duration for the training jobs that the tuning job launches. Use this parameter to limit model training costs.
    
    To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal. This delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts.
    
    When Amazon SageMaker terminates a job because the stopping condition has been met, training algorithms provided by Amazon SageMaker save the intermediate results of the job.
    
    Parameters:
    
    stoppingCondition - Sets a maximum duration for the training jobs that the tuning job launches. Use this parameter to limit model training costs.
    
    To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal. This delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts.
    
    When Amazon SageMaker terminates a job because the stopping condition has been met, training algorithms provided by Amazon SageMaker save the intermediate results of the job.
    
    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; useful for testing and debugging.
    
    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 HyperParameterTrainingJobDefinition clone()
```
    Overrides:
    
    clone in class Object
  - marshall
```
public void marshall(ProtocolMarshaller protocolMarshaller)
```
    Description copied from interface: StructuredPojo
    
    Marshalls this structured data using the given ProtocolMarshaller.
    
    Specified by:
    
    marshall in interface StructuredPojo
    
    Parameters:
    
    protocolMarshaller - Implementation of ProtocolMarshaller used to marshall this object's data.

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Class HyperParameterTrainingJobDefinition

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

HyperParameterTrainingJobDefinition

Method Detail

getStaticHyperParameters

setStaticHyperParameters

withStaticHyperParameters

addStaticHyperParametersEntry

clearStaticHyperParametersEntries

setAlgorithmSpecification

getAlgorithmSpecification

withAlgorithmSpecification

setRoleArn

getRoleArn

withRoleArn

getInputDataConfig

setInputDataConfig

withInputDataConfig

withInputDataConfig

setVpcConfig

getVpcConfig

withVpcConfig

setOutputDataConfig

getOutputDataConfig

withOutputDataConfig

setResourceConfig

getResourceConfig

withResourceConfig

setStoppingCondition

getStoppingCondition

withStoppingCondition

toString

equals

hashCode

clone

marshall