sagemaker/aws.sdk.kotlin.services.sagemaker.model/TrainingJobDefinition/Builder

Builder

class Builder

Properties

hyperParameters

var hyperParameters: Map<String, String>?

The hyperparameters used for the training job.

inputDataConfig

var inputDataConfig: List<Channel>?

An array of Channel objects, each of which specifies an input source.

outputDataConfig

var outputDataConfig: OutputDataConfig?

the path to the S3 bucket where you want to store model artifacts. SageMaker creates subfolders for the artifacts.

resourceConfig

var resourceConfig: ResourceConfig?

The resources, including the ML compute instances and ML storage volumes, to use for model training.

stoppingCondition

var stoppingCondition: StoppingCondition?

Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.

trainingInputMode

var trainingInputMode: TrainingInputMode?

The training input mode that the algorithm supports. For more information about input modes, see Algorithms.

Functions

outputDataConfig

fun outputDataConfig(block: OutputDataConfig.Builder.() -> Unit)

construct an aws.sdk.kotlin.services.sagemaker.model.OutputDataConfig inside the given block

resourceConfig

fun resourceConfig(block: ResourceConfig.Builder.() -> Unit)

construct an aws.sdk.kotlin.services.sagemaker.model.ResourceConfig inside the given block

stoppingCondition

fun stoppingCondition(block: StoppingCondition.Builder.() -> Unit)

construct an aws.sdk.kotlin.services.sagemaker.model.StoppingCondition inside the given block