Reference for ultralytics/utils/dist.py

def find_free_network_port() -> int:
    """
    Find 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.

    Returns:
        (int): The available network port number.
    """
    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)
    trainer.args.model = "{getattr(trainer.hub_session, "model_url", trainer.args.model)}"
    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):
    """
    Generate command for distributed training.

    Args:
        world_size (int): Number of processes to spawn for distributed training.
        trainer (object): The trainer object containing configuration for distributed training.

    Returns:
        cmd (List[str]): The command to execute for distributed training.
        file (str): Path to the temporary file created for DDP 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)