software.amazon.awscdk.services.stepfunctions.tasks

Class SagemakerTrainTask.Builder

java.lang.Object

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

Enclosing class:

SagemakerTrainTask

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

A fluent builder for SagemakerTrainTask.

Method Summary

Modifier and Type	Method and Description
SagemakerTrainTask.Builder	algorithmSpecification(AlgorithmSpecification algorithmSpecification) Identifies the training algorithm to use.
SagemakerTrainTask	build()
static SagemakerTrainTask.Builder	create() EXPERIMENTAL
SagemakerTrainTask.Builder	hyperparameters(Map<String,Object> hyperparameters) Hyperparameters to be used for the train job.
SagemakerTrainTask.Builder	inputDataConfig(List<Channel> inputDataConfig) Describes the various datasets (e.g.
SagemakerTrainTask.Builder	integrationPattern(ServiceIntegrationPattern integrationPattern) The service integration pattern indicates different ways to call SageMaker APIs.
SagemakerTrainTask.Builder	outputDataConfig(OutputDataConfig outputDataConfig) Identifies the Amazon S3 location where you want Amazon SageMaker to save the results of model training.
SagemakerTrainTask.Builder	resourceConfig(ResourceConfig resourceConfig) Identifies the resources, ML compute instances, and ML storage volumes to deploy for model training.
SagemakerTrainTask.Builder	role(IRole role) Role for the Training Job.
SagemakerTrainTask.Builder	stoppingCondition(StoppingCondition stoppingCondition) Sets a time limit for training.
SagemakerTrainTask.Builder	tags(Map<String,String> tags) Tags to be applied to the train job.
SagemakerTrainTask.Builder	trainingJobName(String trainingJobName) Training Job Name.
SagemakerTrainTask.Builder	vpcConfig(VpcConfig vpcConfig) Specifies the VPC that you want your training job to connect to.

Methods inherited from class java.lang.Object

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

Method Detail

create

@Stability(value=Experimental)
public static SagemakerTrainTask.Builder create()

EXPERIMENTAL

Returns:

a new instance of SagemakerTrainTask.Builder.

algorithmSpecification

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

Identifies the training algorithm to use.

EXPERIMENTAL

Parameters:

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

Returns:

this

inputDataConfig

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

Describes the various datasets (e.g. train, validation, test) and the Amazon S3 location where stored.

EXPERIMENTAL

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 SagemakerTrainTask.Builder outputDataConfig(OutputDataConfig outputDataConfig)

Identifies the Amazon S3 location where you want Amazon SageMaker to save the results of model training.

EXPERIMENTAL

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 SagemakerTrainTask.Builder trainingJobName(String trainingJobName)

Training Job Name.

EXPERIMENTAL

Parameters:

trainingJobName - Training Job Name. This parameter is required.

Returns:

this

hyperparameters

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

Hyperparameters to be used for the train job.

EXPERIMENTAL

Parameters:

hyperparameters - Hyperparameters to be used for the train job. This parameter is required.

Returns:

this

integrationPattern

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

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

The valid value is either FIRE_AND_FORGET or SYNC.

Default: FIRE_AND_FORGET

EXPERIMENTAL

Parameters:

integrationPattern - The service integration pattern indicates different ways to call SageMaker APIs. This parameter is required.

Returns:

this

resourceConfig

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

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

EXPERIMENTAL

Parameters:

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

Returns:

this

role

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

Role for the Training Job.

The role must be granted all necessary permissions for the SageMaker training job to be able to operate.

See https://docs.aws.amazon.com/fr_fr/sagemaker/latest/dg/sagemaker-roles.html#sagemaker-roles-createtrainingjob-perms

Default: - a role with appropriate permissions will be created.

EXPERIMENTAL

Parameters:

role - Role for the Training Job. This parameter is required.

Returns:

this

stoppingCondition

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

Sets a time limit for training.

EXPERIMENTAL

Parameters:

stoppingCondition - Sets a time limit for training. This parameter is required.

Returns:

this

tags

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

Tags to be applied to the train job.

EXPERIMENTAL

Parameters:

tags - Tags to be applied to the train job. This parameter is required.

Returns:

this

vpcConfig

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

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

EXPERIMENTAL

Parameters:

vpcConfig - Specifies the VPC that you want your training job to connect to. This parameter is required.

Returns:

this

build

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