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Source code for pytorch_lightning.plugins.environments.kubeflow_environment

# 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
from pytorch_lightning.utilities import rank_zero_deprecation

log = logging.getLogger(__name__)


[docs]class KubeflowEnvironment(ClusterEnvironment): """Environment for distributed training using the `PyTorchJob`_ operator from `Kubeflow`_ .. _PyTorchJob: https://www.kubeflow.org/docs/components/training/pytorch/ .. _Kubeflow: https://www.kubeflow.org """ def __init__(self) -> None: super().__init__() # TODO: remove in 1.7 if hasattr(self, "is_using_kubeflow") and callable(self.is_using_kubeflow): rank_zero_deprecation( f"`{self.__class__.__name__}.is_using_kubeflow` has been deprecated in v1.6 and will be removed in" f" v1.7. Implement the static method `detect()` instead (do not forget to add the `@staticmethod`" f" decorator)." ) @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: """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"} env_vars = os.environ.keys() return required_env_vars.issubset(env_vars) and excluded_env_vars.isdisjoint(env_vars)
[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()

© Copyright Copyright (c) 2018-2021, William Falcon et al... Revision 46f718d2.

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