Source code for lightning_fabric.plugins.environments.lightning

# Copyright The Lightning AI 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
# 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 os
import socket

from lightning_fabric.plugins.environments.cluster_environment import ClusterEnvironment
from lightning_fabric.utilities.rank_zero import rank_zero_only

[docs]class LightningEnvironment(ClusterEnvironment): """The default environment used by Lightning for a single node or free cluster (not managed). There are two modes the Lightning environment can operate with: 1. The user only launches the main process by :code:`python ...` with no additional environment variables set. Lightning will spawn new worker processes for distributed training in the current node. 2. The user launches all processes manually or with utilities like :code:`torch.distributed.launch`. The appropriate environment variables need to be set, and at minimum :code:`LOCAL_RANK`. If the main address and port are not provided, the default environment will choose them automatically. It is recommended to use this default environment for single-node distributed training as it provides a convenient way to launch the training script. """ def __init__(self) -> None: super().__init__() self._main_port: int = -1 self._global_rank: int = 0 self._world_size: int = 1 @property def creates_processes_externally(self) -> bool: """Returns whether the cluster creates the processes or not. If at least :code:`LOCAL_RANK` is available as environment variable, Lightning assumes the user acts as the process launcher/job scheduler and Lightning will not launch new processes. """ return "LOCAL_RANK" in os.environ @property def main_address(self) -> str: return os.environ.get("MASTER_ADDR", "") @property def main_port(self) -> int: if self._main_port == -1: self._main_port = int(os.environ.get("MASTER_PORT", find_free_network_port())) return self._main_port
[docs] @staticmethod def detect() -> bool: return True
[docs] def world_size(self) -> int: return self._world_size
def set_world_size(self, size: int) -> None: self._world_size = size
[docs] def global_rank(self) -> int: return self._global_rank
def set_global_rank(self, rank: int) -> None: self._global_rank = rank rank_zero_only.rank = rank
[docs] def local_rank(self) -> int: return int(os.environ.get("LOCAL_RANK", 0))
[docs] def node_rank(self) -> int: group_rank = os.environ.get("GROUP_RANK", 0) return int(os.environ.get("NODE_RANK", group_rank))
[docs] def teardown(self) -> None: if "WORLD_SIZE" in os.environ: del os.environ["WORLD_SIZE"]
def find_free_network_port() -> int: """Finds a free port on localhost. It is useful in single-node training when we don't want to connect to a real main node but have to set the `MASTER_PORT` environment variable. """ s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) s.bind(("", 0)) port = s.getsockname()[1] s.close() return port

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