# Ultralytics YOLO 🚀, AGPL-3.0 license import os import shutil import socket import sys import tempfile from . import USER_CONFIG_DIR from .torch_utils import TORCH_1_9 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. """ with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: s.bind(("127.0.0.1", 0)) return s.getsockname()[1] # port def generate_ddp_file(trainer): """Generates a DDP file and returns its file name.""" module, name = f"{trainer.__class__.__module__}.{trainer.__class__.__name__}".rsplit(".", 1) content = f""" # Ultralytics Multi-GPU training temp file (should be automatically deleted after use) overrides = {vars(trainer.args)} if __name__ == "__main__": from {module} import {name} from ultralytics.utils import DEFAULT_CFG_DICT cfg = DEFAULT_CFG_DICT.copy() cfg.update(save_dir='') # handle the extra key 'save_dir' trainer = {name}(cfg=cfg, overrides=overrides) results = trainer.train() """ (USER_CONFIG_DIR / "DDP").mkdir(exist_ok=True) with tempfile.NamedTemporaryFile( prefix="_temp_", suffix=f"{id(trainer)}.py", mode="w+", encoding="utf-8", dir=USER_CONFIG_DIR / "DDP", delete=False, ) as file: file.write(content) return file.name def generate_ddp_command(world_size, trainer): """Generates and returns command for distributed training.""" import __main__ # noqa local import to avoid https://github.com/Lightning-AI/lightning/issues/15218 if not trainer.resume: shutil.rmtree(trainer.save_dir) # remove the save_dir file = generate_ddp_file(trainer) dist_cmd = "torch.distributed.run" if TORCH_1_9 else "torch.distributed.launch" port = find_free_network_port() cmd = [sys.executable, "-m", dist_cmd, "--nproc_per_node", f"{world_size}", "--master_port", f"{port}", file] return cmd, file def ddp_cleanup(trainer, file): """Delete temp file if created.""" if f"{id(trainer)}.py" in file: # if temp_file suffix in file os.remove(file)