public static interface ResourceConfig.Builder extends CopyableBuilder<ResourceConfig.Builder,ResourceConfig>
Modifier and Type | Method and Description |
---|---|
ResourceConfig.Builder |
instanceCount(Integer instanceCount)
The number of ML compute instances to use.
|
ResourceConfig.Builder |
instanceType(String instanceType)
The ML compute instance type.
|
ResourceConfig.Builder |
instanceType(TrainingInstanceType instanceType)
The ML compute instance type.
|
ResourceConfig.Builder |
volumeSizeInGB(Integer volumeSizeInGB)
The size of the ML storage volume that you want to provision.
|
copy
apply, build
ResourceConfig.Builder instanceType(String instanceType)
The ML compute instance type.
instanceType
- The ML compute instance type.TrainingInstanceType
,
TrainingInstanceType
ResourceConfig.Builder instanceType(TrainingInstanceType instanceType)
The ML compute instance type.
instanceType
- The ML compute instance type.TrainingInstanceType
,
TrainingInstanceType
ResourceConfig.Builder instanceCount(Integer instanceCount)
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
instanceCount
- The number of ML compute instances to use. For distributed training, provide a value greater than 1.ResourceConfig.Builder volumeSizeInGB(Integer volumeSizeInGB)
The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML
storage volume for scratch space. If you want to store the training data in the ML storage volume, choose
File
as the TrainingInputMode
in the algorithm specification.
You must specify sufficient ML storage for your scenario.
Amazon SageMaker supports only the General Purpose SSD (gp2) ML storage volume type.
volumeSizeInGB
- The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use
the ML storage volume for scratch space. If you want to store the training data in the ML storage
volume, choose File
as the TrainingInputMode
in the algorithm specification.
You must specify sufficient ML storage for your scenario.
Amazon SageMaker supports only the General Purpose SSD (gp2) ML storage volume type.
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