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/1.5.0 (build 46538f8)",
           date="2020-05-21T11:02:02.789Z")
 @Stability(value=Experimental)
public interface SagemakerTrainTaskProps
extends software.amazon.jsii.JsiiSerializable

Properties for creating an Amazon SageMaker training job.

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() Algorithm-specific parameters that influence the quality of the model.
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() Specifies 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)
 @NotNull
AlgorithmSpecification getAlgorithmSpecification()

Identifies the training algorithm to use.

EXPERIMENTAL

getInputDataConfig

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

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

EXPERIMENTAL

getOutputDataConfig

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

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

EXPERIMENTAL

getTrainingJobName

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

Training Job Name.

EXPERIMENTAL

getHyperparameters

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

Algorithm-specific parameters that influence the quality of the model.

Set hyperparameters before you start the learning process. For a list of hyperparameters provided by Amazon SageMaker

Default: - No hyperparameters

EXPERIMENTAL

See Also:

https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html

getIntegrationPattern

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

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

Default: - 1 instance of EC2 `M4.XLarge` with `10GB` volume

EXPERIMENTAL

getRole

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

Sets a time limit for training.

Default: - max runtime of 1 hour

EXPERIMENTAL

getTags

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

Tags to be applied to the train job.

Default: - No tags

EXPERIMENTAL

getVpcConfig

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

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

Default: - No VPC

EXPERIMENTAL

builder

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

Returns:

a SagemakerTrainTaskProps.Builder of SagemakerTrainTaskProps