File size: 2,233 Bytes
5e2c4f1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 | import argparse
import datetime
import importlib
import os
from shutil import copyfile
import torch
import torch.distributed as dist
from omegaconf import OmegaConf
from utils.dist_utils import (
get_world_size,
)
from utils.utils import seed_all
parser = argparse.ArgumentParser(description="VFI")
parser.add_argument("-c", "--config", type=str)
parser.add_argument("-p", "--port", default="23455", type=str)
parser.add_argument("--local_rank", default="0")
args = parser.parse_args()
def main_worker(rank, config):
if "local_rank" not in config:
config["local_rank"] = config["global_rank"] = rank
if torch.cuda.is_available():
print(f"Rank {rank} is available")
config["device"] = f"cuda:{rank}"
if config["distributed"]:
dist.init_process_group(backend="nccl", timeout=datetime.timedelta(seconds=5400))
else:
config["device"] = "cpu"
cfg_name = os.path.basename(args.config).split(".")[0]
config["exp_name"] = cfg_name + "_" + config["exp_name"]
config["save_dir"] = os.path.join(config["save_dir"], config["exp_name"])
if (not config["distributed"]) or rank == 0:
os.makedirs(config["save_dir"], exist_ok=True)
os.makedirs(f"{config['save_dir']}/ckpts", exist_ok=True)
config_path = os.path.join(config["save_dir"], args.config.split("/")[-1])
if not os.path.isfile(config_path):
copyfile(args.config, config_path)
print("[**] create folder {}".format(config["save_dir"]))
trainer_name = config.get("trainer_type", "base_trainer")
print(f"using GPU {rank} for training")
if rank == 0:
print(trainer_name)
trainer_pack = importlib.import_module("trainers." + trainer_name)
trainer = trainer_pack.Trainer(config)
trainer.train()
if __name__ == "__main__":
torch.backends.cudnn.benchmark = True
cfg = OmegaConf.load(args.config)
seed_all(cfg.seed)
rank = int(args.local_rank)
torch.cuda.set_device(torch.device(f"cuda:{rank}"))
# setting distributed cfgurations
cfg["world_size"] = get_world_size()
cfg["local_rank"] = rank
if rank == 0:
print("world_size: ", cfg["world_size"])
main_worker(rank, cfg)
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