software.amazon.awscdk.services.stepfunctions.tasks

Class SagemakerTrainTaskProps.Jsii$Proxy

java.lang.Object

software.amazon.jsii.JsiiObject

software.amazon.awscdk.services.stepfunctions.tasks.SagemakerTrainTaskProps.Jsii$Proxy

All Implemented Interfaces:

SagemakerTrainTaskProps, software.amazon.jsii.JsiiSerializable

Enclosing interface:

SagemakerTrainTaskProps

public static final class SagemakerTrainTaskProps.Jsii$Proxy
extends software.amazon.jsii.JsiiObject
implements SagemakerTrainTaskProps

A proxy class which represents a concrete javascript instance of this type.

Nested Class Summary

Nested classes/interfaces inherited from class software.amazon.jsii.JsiiObject

software.amazon.jsii.JsiiObject.InitializationMode

Nested classes/interfaces inherited from interface software.amazon.awscdk.services.stepfunctions.tasks.SagemakerTrainTaskProps

SagemakerTrainTaskProps.Builder, SagemakerTrainTaskProps.Jsii$Proxy

Constructor Summary

Constructors

Modifier	Constructor and Description
protected	Jsii$Proxy(software.amazon.jsii.JsiiObject.InitializationMode mode)

Method Summary

Modifier and Type	Method and Description
AlgorithmSpecification	getAlgorithmSpecification() Identifies the training algorithm to use.
Map<String,Object>	getHyperparameters() Hyperparameters to be used for the train job.
List<Channel>	getInputDataConfig() Describes the various datasets (e.g.
OutputDataConfig	getOutputDataConfig() Identifies the Amazon S3 location where you want Amazon SageMaker to save the results of model training.
ResourceConfig	getResourceConfig() Identifies the resources, ML compute instances, and ML storage volumes to deploy for model training.
software.amazon.awscdk.services.iam.IRole	getRole() Role for the Training Job.
StoppingCondition	getStoppingCondition() Sets a time limit for training.
Boolean	getSynchronous() Specify if the task is synchronous or asychronous.
Map<String,Object>	getTags() Tags to be applied to the train job.
String	getTrainingJobName() Training Job Name.
VpcConfig	getVpcConfig() Specifies the VPC that you want your training job to connect to.

Methods inherited from class software.amazon.jsii.JsiiObject

jsiiAsyncCall, jsiiCall, jsiiGet, jsiiSet, jsiiStaticCall, jsiiStaticGet, jsiiStaticSet

Methods inherited from class java.lang.Object

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

Methods inherited from interface software.amazon.awscdk.services.stepfunctions.tasks.SagemakerTrainTaskProps

builder

Methods inherited from interface software.amazon.jsii.JsiiSerializable

$jsii$toJson

Constructor Detail

Jsii$Proxy

protected Jsii$Proxy(software.amazon.jsii.JsiiObject.InitializationMode mode)

Method Detail

getAlgorithmSpecification

@Stability(value=Experimental)
public AlgorithmSpecification getAlgorithmSpecification()

Identifies the training algorithm to use. EXPERIMENTAL

Specified by:

getAlgorithmSpecification in interface SagemakerTrainTaskProps

getInputDataConfig

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

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

Specified by:

getInputDataConfig in interface SagemakerTrainTaskProps

getOutputDataConfig

@Stability(value=Experimental)
public OutputDataConfig getOutputDataConfig()

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

Specified by:

getOutputDataConfig in interface SagemakerTrainTaskProps

getTrainingJobName

@Stability(value=Experimental)
public String getTrainingJobName()

Training Job Name. EXPERIMENTAL

Specified by:

getTrainingJobName in interface SagemakerTrainTaskProps

getHyperparameters

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

Hyperparameters to be used for the train job. EXPERIMENTAL

Specified by:

getHyperparameters in interface SagemakerTrainTaskProps

getResourceConfig

@Stability(value=Experimental)
 @Nullable
public ResourceConfig getResourceConfig()

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

Specified by:

getResourceConfig in interface SagemakerTrainTaskProps

getRole

@Stability(value=Experimental)
 @Nullable
public software.amazon.awscdk.services.iam.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

Specified by:

getRole in interface SagemakerTrainTaskProps

getStoppingCondition

@Stability(value=Experimental)
 @Nullable
public StoppingCondition getStoppingCondition()

Sets a time limit for training. EXPERIMENTAL

Specified by:

getStoppingCondition in interface SagemakerTrainTaskProps

getSynchronous

@Stability(value=Experimental)
 @Nullable
public Boolean getSynchronous()

Specify if the task is synchronous or asychronous. Default: false EXPERIMENTAL

Specified by:

getSynchronous in interface SagemakerTrainTaskProps

getTags

@Stability(value=Experimental)
 @Nullable
public Map<String,Object> getTags()

Tags to be applied to the train job. EXPERIMENTAL

Specified by:

getTags in interface SagemakerTrainTaskProps

getVpcConfig

@Stability(value=Experimental)
 @Nullable
public VpcConfig getVpcConfig()

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

Specified by:

getVpcConfig in interface SagemakerTrainTaskProps