lightning/pytorch_lightning/plugins/environments/kubeflow_environment.py

64 lines
2.3 KiB
Python

# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import os
from pytorch_lightning.plugins.environments.cluster_environment import ClusterEnvironment
log = logging.getLogger(__name__)
class KubeflowEnvironment(ClusterEnvironment):
"""
Environment for distributed training using the
`PyTorchJob <https://www.kubeflow.org/docs/components/training/pytorch/>`_
operator from `Kubeflow <https://www.kubeflow.org>`_
"""
@staticmethod
def is_using_kubeflow() -> bool:
""" Returns ``True`` if the current process was launched using Kubeflow PyTorchJob. """
required_env_vars = ("KUBERNETES_PORT", "MASTER_ADDR", "MASTER_PORT", "WORLD_SIZE", "RANK")
# torchelastic sets these. Make sure we're not in torchelastic
excluded_env_vars = ("GROUP_RANK", "LOCAL_RANK", "LOCAL_WORLD_SIZE")
return (all(v in os.environ for v in required_env_vars) and not any(v in os.environ for v in excluded_env_vars))
def creates_children(self) -> bool:
return True
def master_address(self) -> str:
return os.environ['MASTER_ADDR']
def master_port(self) -> int:
return int(os.environ['MASTER_PORT'])
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.")
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.")
def local_rank(self) -> int:
return 0
def node_rank(self) -> int:
return self.global_rank()