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# 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)
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