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Source code for pytorch_lightning.plugins.environments.torchelastic_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,
# See the License for the specific language governing permissions and
# limitations under the License.

import logging
import os

import torch.distributed

from pytorch_lightning.plugins.environments.cluster_environment import ClusterEnvironment
from pytorch_lightning.utilities.imports import _TORCH_GREATER_EQUAL_1_9_1
from pytorch_lightning.utilities.rank_zero import rank_zero_warn

log = logging.getLogger(__name__)

[docs]class TorchElasticEnvironment(ClusterEnvironment): """Environment for fault-tolerant and elastic training with `torchelastic <https://pytorch.org/elastic/>`_""" @property def creates_processes_externally(self) -> bool: return True @property def main_address(self) -> str: if "MASTER_ADDR" not in os.environ: rank_zero_warn("MASTER_ADDR environment variable is not defined. Set as localhost") os.environ["MASTER_ADDR"] = "" log.debug(f"MASTER_ADDR: {os.environ['MASTER_ADDR']}") return os.environ["MASTER_ADDR"] @property def main_port(self) -> int: if "MASTER_PORT" not in os.environ: rank_zero_warn("MASTER_PORT environment variable is not defined. Set as 12910") os.environ["MASTER_PORT"] = "12910" log.debug(f"MASTER_PORT: {os.environ['MASTER_PORT']}") return int(os.environ["MASTER_PORT"])
[docs] @staticmethod def detect() -> bool: """Returns ``True`` if the current process was launched using the torchelastic command.""" if _TORCH_GREATER_EQUAL_1_9_1: # if not available (for example on MacOS), `is_torchelastic_launched` is not defined return torch.distributed.is_available() and torch.distributed.is_torchelastic_launched() required_env_vars = {"RANK", "GROUP_RANK", "LOCAL_RANK", "LOCAL_WORLD_SIZE"} return required_env_vars.issubset(os.environ.keys())
[docs] def world_size(self) -> int: return int(os.environ["WORLD_SIZE"])
def set_world_size(self, size: int) -> None: log.debug("TorchElasticEnvironment.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( "TorchElasticEnvironment.set_global_rank was called, but setting global rank is not allowed. Ignored." )
[docs] def local_rank(self) -> int: return int(os.environ["LOCAL_RANK"])
[docs] def node_rank(self) -> int: return int(os.environ.get("GROUP_RANK", 0))

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