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import os |
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import shutil |
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import socket |
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import sys |
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import tempfile |
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from . import USER_CONFIG_DIR |
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from .torch_utils import TORCH_1_9 |
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def find_free_network_port() -> int: |
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""" |
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Finds a free port on localhost. |
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It is useful in single-node training when we don't want to connect to a real main node but have to set the |
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`MASTER_PORT` environment variable. |
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""" |
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with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: |
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s.bind(("127.0.0.1", 0)) |
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return s.getsockname()[1] |
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def generate_ddp_file(trainer): |
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"""Generates a DDP file and returns its file name.""" |
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module, name = f"{trainer.__class__.__module__}.{trainer.__class__.__name__}".rsplit(".", 1) |
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content = f""" |
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# Ultralytics Multi-GPU training temp file (should be automatically deleted after use) |
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overrides = {vars(trainer.args)} |
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if __name__ == "__main__": |
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from {module} import {name} |
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from ultralytics.utils import DEFAULT_CFG_DICT |
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cfg = DEFAULT_CFG_DICT.copy() |
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cfg.update(save_dir='') # handle the extra key 'save_dir' |
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trainer = {name}(cfg=cfg, overrides=overrides) |
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results = trainer.train() |
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""" |
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(USER_CONFIG_DIR / "DDP").mkdir(exist_ok=True) |
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with tempfile.NamedTemporaryFile( |
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prefix="_temp_", |
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suffix=f"{id(trainer)}.py", |
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mode="w+", |
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encoding="utf-8", |
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dir=USER_CONFIG_DIR / "DDP", |
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delete=False, |
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) as file: |
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file.write(content) |
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return file.name |
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def generate_ddp_command(world_size, trainer): |
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"""Generates and returns command for distributed training.""" |
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import __main__ |
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if not trainer.resume: |
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shutil.rmtree(trainer.save_dir) |
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file = generate_ddp_file(trainer) |
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dist_cmd = "torch.distributed.run" if TORCH_1_9 else "torch.distributed.launch" |
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port = find_free_network_port() |
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cmd = [sys.executable, "-m", dist_cmd, "--nproc_per_node", f"{world_size}", "--master_port", f"{port}", file] |
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return cmd, file |
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def ddp_cleanup(trainer, file): |
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"""Delete temp file if created.""" |
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if f"{id(trainer)}.py" in file: |
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os.remove(file) |
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