Uses of Class
com.amazonaws.services.sagemaker.model.ResourceConfig

Packages that use ResourceConfig

Package	Description
com.amazonaws.services.sagemaker.model

Uses of ResourceConfig in com.amazonaws.services.sagemaker.model

Methods in com.amazonaws.services.sagemaker.model that return ResourceConfig

Modifier and Type	Method and Description
ResourceConfig	ResourceConfig.clone()
ResourceConfig	DescribeTrainingJobResult.getResourceConfig() Resources, including ML compute instances and ML storage volumes, that are configured for model training.
ResourceConfig	TrainingJobDefinition.getResourceConfig() The resources, including the ML compute instances and ML storage volumes, to use for model training.
ResourceConfig	CreateTrainingJobRequest.getResourceConfig() The resources, including the ML compute instances and ML storage volumes, to use for model training.
ResourceConfig	HyperParameterTrainingJobDefinition.getResourceConfig() The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
ResourceConfig	TrainingJob.getResourceConfig() Resources, including ML compute instances and ML storage volumes, that are configured for model training.
ResourceConfig	ResourceConfig.withInstanceCount(Integer instanceCount) The number of ML compute instances to use.
ResourceConfig	ResourceConfig.withInstanceGroups(Collection<InstanceGroup> instanceGroups) The configuration of a heterogeneous cluster in JSON format.
ResourceConfig	ResourceConfig.withInstanceGroups(InstanceGroup... instanceGroups) The configuration of a heterogeneous cluster in JSON format.
ResourceConfig	ResourceConfig.withInstanceType(String instanceType) The ML compute instance type.
ResourceConfig	ResourceConfig.withInstanceType(TrainingInstanceType instanceType) The ML compute instance type.
ResourceConfig	ResourceConfig.withKeepAlivePeriodInSeconds(Integer keepAlivePeriodInSeconds) The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
ResourceConfig	ResourceConfig.withVolumeKmsKeyId(String volumeKmsKeyId) The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
ResourceConfig	ResourceConfig.withVolumeSizeInGB(Integer volumeSizeInGB) The size of the ML storage volume that you want to provision.

Methods in com.amazonaws.services.sagemaker.model with parameters of type ResourceConfig

Modifier and Type	Method and Description
void	DescribeTrainingJobResult.setResourceConfig(ResourceConfig resourceConfig) Resources, including ML compute instances and ML storage volumes, that are configured for model training.
void	TrainingJobDefinition.setResourceConfig(ResourceConfig resourceConfig) The resources, including the ML compute instances and ML storage volumes, to use for model training.
void	CreateTrainingJobRequest.setResourceConfig(ResourceConfig resourceConfig) The resources, including the ML compute instances and ML storage volumes, to use for model training.
void	HyperParameterTrainingJobDefinition.setResourceConfig(ResourceConfig resourceConfig) The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
void	TrainingJob.setResourceConfig(ResourceConfig resourceConfig) Resources, including ML compute instances and ML storage volumes, that are configured for model training.
DescribeTrainingJobResult	DescribeTrainingJobResult.withResourceConfig(ResourceConfig resourceConfig) Resources, including ML compute instances and ML storage volumes, that are configured for model training.
TrainingJobDefinition	TrainingJobDefinition.withResourceConfig(ResourceConfig resourceConfig) The resources, including the ML compute instances and ML storage volumes, to use for model training.
CreateTrainingJobRequest	CreateTrainingJobRequest.withResourceConfig(ResourceConfig resourceConfig) The resources, including the ML compute instances and ML storage volumes, to use for model training.
HyperParameterTrainingJobDefinition	HyperParameterTrainingJobDefinition.withResourceConfig(ResourceConfig resourceConfig) The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
TrainingJob	TrainingJob.withResourceConfig(ResourceConfig resourceConfig) Resources, including ML compute instances and ML storage volumes, that are configured for model training.