software.amazon.awscdk.services.stepfunctions.tasks

Interface SagemakerTrainTaskProps

All Superinterfaces:

software.amazon.jsii.JsiiSerializable

All Known Implementing Classes:

SagemakerTrainTaskProps.Jsii$Proxy

@Generated(value="jsii-pacmak/0.21.1 (build 9ff44cb)",
           date="2020-01-07T23:11:00.939Z")
 @Stability(value=Experimental)
public interface SagemakerTrainTaskProps
extends software.amazon.jsii.JsiiSerializable

EXPERIMENTAL

Nested Class Summary

Nested Classes

Modifier and Type	Interface and Description
static class	SagemakerTrainTaskProps.Builder A builder for SagemakerTrainTaskProps
static class	SagemakerTrainTaskProps.Jsii$Proxy An implementation for SagemakerTrainTaskProps

Method Summary

Modifier and Type	Method and Description
static SagemakerTrainTaskProps.Builder	builder()
AlgorithmSpecification	getAlgorithmSpecification() Identifies the training algorithm to use.
default Map<String,Object>	getHyperparameters() Hyperparameters to be used for the train job.
List<Channel>	getInputDataConfig() Describes the various datasets (e.g.
default ServiceIntegrationPattern	getIntegrationPattern() The service integration pattern indicates different ways to call SageMaker APIs.
OutputDataConfig	getOutputDataConfig() Identifies the Amazon S3 location where you want Amazon SageMaker to save the results of model training.
default ResourceConfig	getResourceConfig() Identifies the resources, ML compute instances, and ML storage volumes to deploy for model training.
default IRole	getRole() Role for the Training Job.
default StoppingCondition	getStoppingCondition() Sets a time limit for training.
default Map<String,String>	getTags() Tags to be applied to the train job.
String	getTrainingJobName() Training Job Name.
default VpcConfig	getVpcConfig() Specifies the VPC that you want your training job to connect to.

Methods inherited from interface software.amazon.jsii.JsiiSerializable

$jsii$toJson

Method Detail

getAlgorithmSpecification

@Stability(value=Experimental)
AlgorithmSpecification getAlgorithmSpecification()

Identifies the training algorithm to use.

EXPERIMENTAL

getInputDataConfig

@Stability(value=Experimental)
List<Channel> getInputDataConfig()

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

EXPERIMENTAL

getOutputDataConfig

@Stability(value=Experimental)
OutputDataConfig getOutputDataConfig()

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

EXPERIMENTAL

getTrainingJobName

@Stability(value=Experimental)
String getTrainingJobName()

Training Job Name.

EXPERIMENTAL

getHyperparameters

@Stability(value=Experimental)
default Map<String,Object> getHyperparameters()

Hyperparameters to be used for the train job.

EXPERIMENTAL

getIntegrationPattern

@Stability(value=Experimental)
default ServiceIntegrationPattern getIntegrationPattern()

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

getResourceConfig

@Stability(value=Experimental)
default ResourceConfig getResourceConfig()

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

EXPERIMENTAL

getRole

@Stability(value=Experimental)
default IRole getRole()

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

getStoppingCondition

@Stability(value=Experimental)
default StoppingCondition getStoppingCondition()

Sets a time limit for training.

EXPERIMENTAL

getTags

@Stability(value=Experimental)
default Map<String,String> getTags()

Tags to be applied to the train job.

EXPERIMENTAL

getVpcConfig

@Stability(value=Experimental)
default VpcConfig getVpcConfig()

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

EXPERIMENTAL

builder

@Stability(value=Experimental)
static SagemakerTrainTaskProps.Builder builder()

Returns:

a SagemakerTrainTaskProps.Builder of SagemakerTrainTaskProps