use torchrun instead for multigpu

src/train.py

@@ -18,12 +18,9 @@ if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_vit_latent.yml', nargs='+') # ugh
     parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
-    parser.add_argument('--local_rank', type=int, help='Rank Number') 
     args = parser.parse_args()
     args.opt = " ".join(args.opt) # absolutely disgusting
 
-    os.environ['LOCAL_RANK'] = str(args.local_rank)
-
     with open(args.opt, 'r') as file:
         opt_config = yaml.safe_load(file)
 

train.sh

@@ -6,7 +6,7 @@ CONFIG=$2
 PORT=1234
 
 if (( $GPUS > 1 )); then
-	python3 -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT ./src/train.py -opt "$CONFIG" --launcher=pytorch
+	torchrun --nproc_per_node=$GPUS --master_port=$PORT ./src/train.py -opt "$CONFIG" --launcher=pytorch
 else
 	python3 ./src/train.py -opt "$CONFIG"
 fi