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

instance_type (str) –
volume_size_in_gb (int) –
instance_count (int) –
distributed_protocol (int) –

classmethod from_flyte_idl(pb2_object)[source]#

Parameters: pb2_object (flyteidl.plugins.sagemaker.training_job_pb2.TrainingJobResourceConfig) –
Return type: TrainingJobResourceConfig

serialize_to_string()#

Return type: str

short_string()#

Return type: Text

to_flyte_idl()[source]#

Return type: _training_job_pb2.TrainingJobResourceConfig

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