init

main.py

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+from pathlib import Path
+import torch
+import pickle
+import argparse
+import logging
+import torch.distributed as dist
+from config import MyParser
+from steps import trainer
+
+
+if __name__ == "__main__":
+    formatter = (
+        "%(asctime)s [%(levelname)s] %(filename)s:%(lineno)d || %(message)s"
+    )
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    
+    torch.cuda.empty_cache()
+    args = MyParser().parse_args()
+    logging.info(args)
+    exp_dir = Path(args.exp_dir)
+    exp_dir.mkdir(exist_ok=True, parents=True)
+    logging.info(f"exp_dir: {str(exp_dir)}")
+
+    if args.resume:
+        resume = args.resume
+        assert(bool(args.exp_dir))
+        with open("%s/args.pkl" % args.exp_dir, "rb") as f:
+            old_args = pickle.load(f)
+        new_args = vars(args)
+        old_args = vars(old_args)
+        for key in new_args:
+            if key not in old_args or old_args[key] != new_args[key]:
+                old_args[key] = new_args[key]
+        args = argparse.Namespace(**old_args)
+        args.resume = resume
+    else:
+        with open("%s/args.pkl" % args.exp_dir, "wb") as f:
+            pickle.dump(args, f)
+
+    dist.init_process_group(backend='nccl', init_method='env://')
+    rank = dist.get_rank()
+    world_size = dist.get_world_size()
+    torch.cuda.set_device(rank)
+    my_trainer = trainer.Trainer(args, world_size, rank)
+    my_trainer.train()