sagemaker/aws.sdk.kotlin.services.sagemaker.model/CreateTrainingJobRequest/Builder/resourceConfig

resourceConfig

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

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

var resourceConfig: ResourceConfig?

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

ML storage volumes store model artifacts and incremental states. Training algorithms might also use ML storage volumes for scratch space. If you want SageMaker to use the ML storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.