software.amazon.awscdk.services.stepfunctions.tasks

Class SagemakerTrainTaskProps.Builder

java.lang.Object

software.amazon.awscdk.services.stepfunctions.tasks.SagemakerTrainTaskProps.Builder

Enclosing interface:

SagemakerTrainTaskProps

@Stability(value=Experimental)
public static final class SagemakerTrainTaskProps.Builder
extends Object

A builder for SagemakerTrainTaskProps

Constructor Summary

Constructors

Constructor and Description
Builder()

Method Summary

Modifier and Type	Method and Description
SagemakerTrainTaskProps.Builder	algorithmSpecification(AlgorithmSpecification algorithmSpecification) Sets the value of AlgorithmSpecification
SagemakerTrainTaskProps	build() Builds the configured instance.
SagemakerTrainTaskProps.Builder	hyperparameters(Map<String,Object> hyperparameters) Sets the value of Hyperparameters
SagemakerTrainTaskProps.Builder	inputDataConfig(List<Channel> inputDataConfig) Sets the value of InputDataConfig
SagemakerTrainTaskProps.Builder	integrationPattern(ServiceIntegrationPattern integrationPattern) Sets the value of IntegrationPattern
SagemakerTrainTaskProps.Builder	outputDataConfig(OutputDataConfig outputDataConfig) Sets the value of OutputDataConfig
SagemakerTrainTaskProps.Builder	resourceConfig(ResourceConfig resourceConfig) Sets the value of ResourceConfig
SagemakerTrainTaskProps.Builder	role(IRole role) Sets the value of Role
SagemakerTrainTaskProps.Builder	stoppingCondition(StoppingCondition stoppingCondition) Sets the value of StoppingCondition
SagemakerTrainTaskProps.Builder	tags(Map<String,String> tags) Sets the value of Tags
SagemakerTrainTaskProps.Builder	trainingJobName(String trainingJobName) Sets the value of TrainingJobName
SagemakerTrainTaskProps.Builder	vpcConfig(VpcConfig vpcConfig) Sets the value of VpcConfig

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

Builder

public Builder()

Method Detail

algorithmSpecification

@Stability(value=Experimental)
public SagemakerTrainTaskProps.Builder algorithmSpecification(AlgorithmSpecification algorithmSpecification)

Sets the value of AlgorithmSpecification

Parameters:

algorithmSpecification - Identifies the training algorithm to use. This parameter is required.

Returns:

this

inputDataConfig

@Stability(value=Experimental)
public SagemakerTrainTaskProps.Builder inputDataConfig(List<Channel> inputDataConfig)

Sets the value of InputDataConfig

Parameters:

inputDataConfig - Describes the various datasets (e.g. train, validation, test) and the Amazon S3 location where stored. This parameter is required.

Returns:

this

outputDataConfig

@Stability(value=Experimental)
public SagemakerTrainTaskProps.Builder outputDataConfig(OutputDataConfig outputDataConfig)

Sets the value of OutputDataConfig

Parameters:

outputDataConfig - Identifies the Amazon S3 location where you want Amazon SageMaker to save the results of model training. This parameter is required.

Returns:

this

trainingJobName

@Stability(value=Experimental)
public SagemakerTrainTaskProps.Builder trainingJobName(String trainingJobName)

Sets the value of TrainingJobName

Parameters:

trainingJobName - Training Job Name. This parameter is required.

Returns:

this

hyperparameters

@Stability(value=Experimental)
public SagemakerTrainTaskProps.Builder hyperparameters(Map<String,Object> hyperparameters)

Sets the value of Hyperparameters

Parameters:

hyperparameters - Hyperparameters to be used for the train job.

Returns:

this

integrationPattern

@Stability(value=Experimental)
public SagemakerTrainTaskProps.Builder integrationPattern(ServiceIntegrationPattern integrationPattern)

Sets the value of IntegrationPattern

Parameters:

integrationPattern - The service integration pattern indicates different ways to call SageMaker APIs.

Returns:

this

resourceConfig

@Stability(value=Experimental)
public SagemakerTrainTaskProps.Builder resourceConfig(ResourceConfig resourceConfig)

Sets the value of ResourceConfig

Parameters:

resourceConfig - Identifies the resources, ML compute instances, and ML storage volumes to deploy for model training.

Returns:

this

role

@Stability(value=Experimental)
public SagemakerTrainTaskProps.Builder role(IRole role)

Sets the value of Role

Parameters:

role - Role for the Training Job.

Returns:

this

stoppingCondition

@Stability(value=Experimental)
public SagemakerTrainTaskProps.Builder stoppingCondition(StoppingCondition stoppingCondition)

Sets the value of StoppingCondition

Parameters:

stoppingCondition - Sets a time limit for training.

Returns:

this

tags

@Stability(value=Experimental)
public SagemakerTrainTaskProps.Builder tags(Map<String,String> tags)

Sets the value of Tags

Parameters:

tags - Tags to be applied to the train job.

Returns:

this

vpcConfig

@Stability(value=Experimental)
public SagemakerTrainTaskProps.Builder vpcConfig(VpcConfig vpcConfig)

Sets the value of VpcConfig

Parameters:

vpcConfig - Specifies the VPC that you want your training job to connect to.

Returns:

this

build

@Stability(value=Experimental)
public SagemakerTrainTaskProps build()

Builds the configured instance.

Returns:

a new instance of SagemakerTrainTaskProps

Throws:

NullPointerException - if any required attribute was not provided