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

TrainingJobDefinition

class TrainingJobDefinition

Defines the input needed to run a training job using the algorithm.

Types

object Companion

Properties

hyperParameters

val hyperParameters: Map<String, String>?

The hyperparameters used for the training job.

inputDataConfig

val inputDataConfig: List<Channel>?

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

outputDataConfig

val outputDataConfig: OutputDataConfig?

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

val resourceConfig: ResourceConfig?

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

stoppingCondition

val 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

val trainingInputMode: TrainingInputMode?

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

Functions

inline fun copy(block: TrainingJobDefinition.Builder.() -> Unit = {}): TrainingJobDefinition

open operator override fun equals(other: Any?): Boolean

open override fun hashCode(): Int

open override fun toString(): String