# Ultralytics YOLO 🚀, GPL-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): import_path = '.'.join(str(trainer.__class__).split(".")[1:-1]) if not trainer.resume: shutil.rmtree(trainer.save_dir) # remove the save_dir content = f'''cfg = {vars(trainer.args)} \nif __name__ == "__main__": from ultralytics.{import_path} import {trainer.__class__.__name__} trainer = {trainer.__class__.__name__}(cfg=cfg) 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): import __main__ # noqa local import to avoid https://github.com/Lightning-AI/lightning/issues/15218 # Get file and args (do not use sys.argv due to security vulnerability) exclude_args = ['save_dir'] args = [f"{k}={v}" for k, v in vars(trainer.args).items() if k not in exclude_args] file = generate_ddp_file(trainer) # if argv[0].endswith('yolo') else os.path.abspath(argv[0]) # Build command torch_distributed_cmd = "torch.distributed.run" if TORCH_1_9 else "torch.distributed.launch" cmd = [ sys.executable, "-m", torch_distributed_cmd, "--nproc_per_node", f"{world_size}", "--master_port", f"{find_free_network_port()}", file] + args 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)