flytekitplugins.awssagemaker.TrainingJobResourceConfig#
- class flytekitplugins.awssagemaker.TrainingJobResourceConfig(instance_type, volume_size_in_gb, instance_count=1, distributed_protocol=0)[source]#
TrainingJobResourceConfig is a pass-through, specifying the instance type to use for the training job, the number of instances to launch, and the size of the ML storage volume the user wants to provision Refer to SageMaker official doc for more details: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html
Methods
- Parameters
- classmethod from_flyte_idl(pb2_object)[source]#
- Parameters
pb2_object (flyteidl.plugins.sagemaker.training_job_pb2.TrainingJobResourceConfig) –
- Return type
- short_string()#
- Return type
Text
- verbose_string()#
- Return type
Text
Attributes
- distributed_protocol#
The distribution framework is used to determine through which mechanism the distributed training is done. enum value from DistributionFramework. :rtype: int
- instance_count#
The number of ML compute instances to use. For distributed training, provide a value greater than 1. :rtype: int
- instance_type#
The ML compute instance type. :rtype: str
- is_empty#
- volume_size_in_gb#
The size of the ML storage volume that you want to provision to store the data and intermediate artifacts, etc. :rtype: int