software.amazon.awssdk.services.sagemaker.model

Interface HyperParameterTrainingJobDefinition.Builder

All Superinterfaces:

Buildable, CopyableBuilder<HyperParameterTrainingJobDefinition.Builder,HyperParameterTrainingJobDefinition>, SdkBuilder<HyperParameterTrainingJobDefinition.Builder,HyperParameterTrainingJobDefinition>, SdkPojo

Enclosing class:

HyperParameterTrainingJobDefinition

public static interface HyperParameterTrainingJobDefinition.Builder
extends SdkPojo, CopyableBuilder<HyperParameterTrainingJobDefinition.Builder,HyperParameterTrainingJobDefinition>

Method Summary

Modifier and Type	Method and Description
default HyperParameterTrainingJobDefinition.Builder	algorithmSpecification(Consumer<HyperParameterAlgorithmSpecification.Builder> algorithmSpecification) The HyperParameterAlgorithmSpecification object that specifies the algorithm to use for the training jobs that the tuning job launches.
HyperParameterTrainingJobDefinition.Builder	algorithmSpecification(HyperParameterAlgorithmSpecification algorithmSpecification) The HyperParameterAlgorithmSpecification object that specifies the algorithm to use for the training jobs that the tuning job launches.
HyperParameterTrainingJobDefinition.Builder	enableNetworkIsolation(Boolean enableNetworkIsolation) Isolates the training container.
HyperParameterTrainingJobDefinition.Builder	inputDataConfig(Channel... inputDataConfig) An array of Channel objects that specify the input for the training jobs that the tuning job launches.
HyperParameterTrainingJobDefinition.Builder	inputDataConfig(Collection<Channel> inputDataConfig) An array of Channel objects that specify the input for the training jobs that the tuning job launches.
HyperParameterTrainingJobDefinition.Builder	inputDataConfig(Consumer<Channel.Builder>... inputDataConfig) An array of Channel objects that specify the input for the training jobs that the tuning job launches.
default HyperParameterTrainingJobDefinition.Builder	outputDataConfig(Consumer<OutputDataConfig.Builder> outputDataConfig) Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
HyperParameterTrainingJobDefinition.Builder	outputDataConfig(OutputDataConfig outputDataConfig) Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
default HyperParameterTrainingJobDefinition.Builder	resourceConfig(Consumer<ResourceConfig.Builder> resourceConfig) The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
HyperParameterTrainingJobDefinition.Builder	resourceConfig(ResourceConfig resourceConfig) The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
HyperParameterTrainingJobDefinition.Builder	roleArn(String roleArn) The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
HyperParameterTrainingJobDefinition.Builder	staticHyperParameters(Map<String,String> staticHyperParameters) Specifies the values of hyperparameters that do not change for the tuning job.
default HyperParameterTrainingJobDefinition.Builder	stoppingCondition(Consumer<StoppingCondition.Builder> stoppingCondition) Sets a maximum duration for the training jobs that the tuning job launches.
HyperParameterTrainingJobDefinition.Builder	stoppingCondition(StoppingCondition stoppingCondition) Sets a maximum duration for the training jobs that the tuning job launches.
default HyperParameterTrainingJobDefinition.Builder	vpcConfig(Consumer<VpcConfig.Builder> vpcConfig) The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to.
HyperParameterTrainingJobDefinition.Builder	vpcConfig(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 interface software.amazon.awssdk.core.SdkPojo

sdkFields

Methods inherited from interface software.amazon.awssdk.utils.builder.CopyableBuilder

copy

Methods inherited from interface software.amazon.awssdk.utils.builder.SdkBuilder

applyMutation, build

Method Detail

staticHyperParameters

HyperParameterTrainingJobDefinition.Builder staticHyperParameters(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.

algorithmSpecification

HyperParameterTrainingJobDefinition.Builder algorithmSpecification(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.

algorithmSpecification

default HyperParameterTrainingJobDefinition.Builder algorithmSpecification(Consumer<HyperParameterAlgorithmSpecification.Builder> algorithmSpecification)

The HyperParameterAlgorithmSpecification object that specifies the algorithm to use for the training jobs that the tuning job launches.

This is a convenience that creates an instance of the HyperParameterAlgorithmSpecification.Builder avoiding the need to create one manually via HyperParameterAlgorithmSpecification.builder(). When the Consumer completes, SdkBuilder.build() is called immediately and its result is passed to algorithmSpecification(HyperParameterAlgorithmSpecification) .

Parameters:

algorithmSpecification - a consumer that will call methods on HyperParameterAlgorithmSpecification.Builder

Returns:

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

See Also:

algorithmSpecification(HyperParameterAlgorithmSpecification)

roleArn

HyperParameterTrainingJobDefinition.Builder roleArn(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.

inputDataConfig

HyperParameterTrainingJobDefinition.Builder inputDataConfig(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.

inputDataConfig

HyperParameterTrainingJobDefinition.Builder inputDataConfig(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.

inputDataConfig

HyperParameterTrainingJobDefinition.Builder inputDataConfig(Consumer<Channel.Builder>... inputDataConfig)

An array of Channel objects that specify the input for the training jobs that the tuning job launches.

This is a convenience that creates an instance of the List.Builder avoiding the need to create one manually via List#builder(). When the Consumer completes, List.Builder#build() is called immediately and its result is passed to #inputDataConfig(List).

Parameters:

inputDataConfig - a consumer that will call methods on List.Builder

Returns:

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

See Also:

#inputDataConfig(List)

vpcConfig

HyperParameterTrainingJobDefinition.Builder vpcConfig(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 Protect Training Jobs by Using an Amazon Virtual Private Cloud.

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 Protect Training Jobs by Using an Amazon Virtual Private Cloud.

Returns:

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

vpcConfig

default HyperParameterTrainingJobDefinition.Builder vpcConfig(Consumer<VpcConfig.Builder> 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 Protect Training Jobs by Using an Amazon Virtual Private Cloud.

This is a convenience that creates an instance of the VpcConfig.Builder avoiding the need to create one manually via VpcConfig.builder(). When the Consumer completes, SdkBuilder.build() is called immediately and its result is passed to vpcConfig(VpcConfig).

Parameters:

vpcConfig - a consumer that will call methods on VpcConfig.Builder

Returns:

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

See Also:

vpcConfig(VpcConfig)

outputDataConfig

HyperParameterTrainingJobDefinition.Builder outputDataConfig(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.

outputDataConfig

default HyperParameterTrainingJobDefinition.Builder outputDataConfig(Consumer<OutputDataConfig.Builder> outputDataConfig)

Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.

This is a convenience that creates an instance of the OutputDataConfig.Builder avoiding the need to create one manually via OutputDataConfig.builder(). When the Consumer completes, SdkBuilder.build() is called immediately and its result is passed to outputDataConfig(OutputDataConfig).

Parameters:

outputDataConfig - a consumer that will call methods on OutputDataConfig.Builder

Returns:

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

See Also:

outputDataConfig(OutputDataConfig)

resourceConfig

HyperParameterTrainingJobDefinition.Builder resourceConfig(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.

resourceConfig

default HyperParameterTrainingJobDefinition.Builder resourceConfig(Consumer<ResourceConfig.Builder> 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.

This is a convenience that creates an instance of the ResourceConfig.Builder avoiding the need to create one manually via ResourceConfig.builder(). When the Consumer completes, SdkBuilder.build() is called immediately and its result is passed to resourceConfig(ResourceConfig).

Parameters:

resourceConfig - a consumer that will call methods on ResourceConfig.Builder

Returns:

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

See Also:

resourceConfig(ResourceConfig)

stoppingCondition

HyperParameterTrainingJobDefinition.Builder stoppingCondition(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.

stoppingCondition

default HyperParameterTrainingJobDefinition.Builder stoppingCondition(Consumer<StoppingCondition.Builder> 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.

This is a convenience that creates an instance of the StoppingCondition.Builder avoiding the need to create one manually via StoppingCondition.builder(). When the Consumer completes, SdkBuilder.build() is called immediately and its result is passed to stoppingCondition(StoppingCondition).

Parameters:

stoppingCondition - a consumer that will call methods on StoppingCondition.Builder

Returns:

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

See Also:

stoppingCondition(StoppingCondition)

enableNetworkIsolation

HyperParameterTrainingJobDefinition.Builder enableNetworkIsolation(Boolean enableNetworkIsolation)

Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specifed VPC, but the training container does not have network access.

The Semantic Segmentation built-in algorithm does not support network isolation.

Parameters:

enableNetworkIsolation - Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specifed VPC, but the training container does not have network access.

The Semantic Segmentation built-in algorithm does not support network isolation.

Returns:

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