Source code for lightning.fabric.plugins.environments.kubeflow
# Copyright The Lightning AI team.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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import logging
import os
from lightning.fabric.plugins.environments.cluster_environment import ClusterEnvironment
log = logging.getLogger(__name__)
[docs]class KubeflowEnvironment(ClusterEnvironment):
"""Environment for distributed training using the `PyTorchJob`_ operator from `Kubeflow`_.
This environment, unlike others, does not get auto-detected and needs to be passed to the Fabric/Trainer
constructor manually.
.. _PyTorchJob: https://www.kubeflow.org/docs/components/training/pytorch/
.. _Kubeflow: https://www.kubeflow.org
"""
@property
def creates_processes_externally(self) -> bool:
return True
@property
def main_address(self) -> str:
return os.environ["MASTER_ADDR"]
@property
def main_port(self) -> int:
return int(os.environ["MASTER_PORT"])
[docs] @staticmethod
def detect() -> bool:
raise NotImplementedError("The Kubeflow environment can't be detected automatically.")
[docs] def world_size(self) -> int:
return int(os.environ["WORLD_SIZE"])
def set_world_size(self, size: int) -> None:
log.debug("KubeflowEnvironment.set_world_size was called, but setting world size is not allowed. Ignored.")
[docs] def global_rank(self) -> int:
return int(os.environ["RANK"])
def set_global_rank(self, rank: int) -> None:
log.debug("KubeflowEnvironment.set_global_rank was called, but setting global rank is not allowed. Ignored.")
[docs] def local_rank(self) -> int:
return 0
[docs] def node_rank(self) -> int:
return self.global_rank()