79 lines
2.9 KiB
Python
79 lines
2.9 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import logging
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import os
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from pytorch_lightning.plugins.environments.cluster_environment import ClusterEnvironment
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from pytorch_lightning.utilities import rank_zero_deprecation
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log = logging.getLogger(__name__)
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class KubeflowEnvironment(ClusterEnvironment):
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"""Environment for distributed training using the `PyTorchJob`_ operator from `Kubeflow`_
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.. _PyTorchJob: https://www.kubeflow.org/docs/components/training/pytorch/
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.. _Kubeflow: https://www.kubeflow.org
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"""
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def __init__(self) -> None:
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super().__init__()
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# TODO: remove in 1.7
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if hasattr(self, "is_using_kubeflow") and callable(self.is_using_kubeflow):
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rank_zero_deprecation(
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f"`{self.__class__.__name__}.is_using_kubeflow` has been deprecated in v1.6 and will be removed in"
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f" v1.7. Implement the static method `detect()` instead (do not forget to add the `@staticmethod`"
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f" decorator)."
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)
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@property
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def creates_processes_externally(self) -> bool:
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return True
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@property
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def main_address(self) -> str:
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return os.environ["MASTER_ADDR"]
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@property
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def main_port(self) -> int:
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return int(os.environ["MASTER_PORT"])
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@staticmethod
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def detect() -> bool:
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"""Returns ``True`` if the current process was launched using Kubeflow PyTorchJob."""
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required_env_vars = {"KUBERNETES_PORT", "MASTER_ADDR", "MASTER_PORT", "WORLD_SIZE", "RANK"}
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# torchelastic sets these. Make sure we're not in torchelastic
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excluded_env_vars = {"GROUP_RANK", "LOCAL_RANK", "LOCAL_WORLD_SIZE"}
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env_vars = os.environ.keys()
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return required_env_vars.issubset(env_vars) and excluded_env_vars.isdisjoint(env_vars)
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def world_size(self) -> int:
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return int(os.environ["WORLD_SIZE"])
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def set_world_size(self, size: int) -> None:
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log.debug("KubeflowEnvironment.set_world_size was called, but setting world size is not allowed. Ignored.")
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def global_rank(self) -> int:
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return int(os.environ["RANK"])
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def set_global_rank(self, rank: int) -> None:
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log.debug("KubeflowEnvironment.set_global_rank was called, but setting global rank is not allowed. Ignored.")
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def local_rank(self) -> int:
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return 0
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def node_rank(self) -> int:
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return self.global_rank()
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