diff --git a/train/main.py b/train/main.py
new file mode 100644
index 0000000..e59bf3c
--- /dev/null
+++ b/train/main.py
@@ -0,0 +1,28 @@
+# python3 -m torch.distributed.run --nproc_per_node=1 --nnodes=1 --node_rank=0 --rdzv_id=21046 --rdzv_backend=c10d --rdzv_endpoint=127.0.0.1:21046 main.py
+from torch import optim
+from torch import nn
+
+from model import Network
+from dataset import Cifar10Dataset
+from trainer import ddp_init, Trainer
+from model import Network
+
+BATCH_SIZE = 64
+EPOCH_NUM = 30
+
+def main(batch_size, epoch_num):
+    print(f'training config: batch_size={batch_size}, epoch={epoch_num}')
+    ddp_init()
+
+    model = Network()
+    dataset = Cifar10Dataset('./dataset_dir/cifar-10-batches-py')
+    optimizer = optim.Adam(model.parameters(), lr=0.001)
+    criterion = nn.CrossEntropyLoss()
+
+    trainer = Trainer(model, dataset, batch_size, optimizer, criterion)
+    trainer.train(epoch_num)
+
+
+if __name__ == '__main__':
+    main(BATCH_SIZE, EPOCH_NUM)
+
diff --git a/train/train.py b/train/trainer.py
similarity index 61%
rename from train/train.py
rename to train/trainer.py
index fb3be73..bf14df8 100644
--- a/train/train.py
+++ b/train/trainer.py
@@ -55,35 +55,3 @@ class Trainer():
             print(f"[DEVICE {self.global_rank}] EPOCH {epoch} loss={train_loss_sum/len(self.loader)} acc={(train_correct_sum/train_item_counter).item()}")
 
 
-
-def main(batch_size, epoch_num):
-    print(f'training with {world_size} GPUs')
-    print(f'training config: batch_size={batch_size}, epoch={epoch_num}')
-    print(f'LOCAL_RANK={os.environ["LOCAL_RANK"]}')
-    print(f'RANK={os.environ["RANK"]}')
-    print(f'LOCAL_WORLD_SIZE={os.environ["LOCAL_WORLD_SIZE"]}')
-    print(f'WORLD_SIZE={os.environ["WORLD_SIZE"]}')
-    print(f'MASTER_ADDR={os.environ["MASTER_ADDR"]}')
-    print(f'MASTER_PORT={os.environ["MASTER_PORT"]}')
-    ddp_init()
-
-    model = Network()
-    dataset = Cifar10Dataset('./dataset_dir/cifar-10-batches-py')
-    optimizer = optim.Adam(model.parameters(), lr=0.001)
-    criterion = nn.CrossEntropyLoss()
-
-    trainer = Trainer(model, dataset, batch_size, optimizer, criterion)
-    trainer.train(epoch_num)
-
-
-if __name__ == '__main__':
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--batch_size', type=int, default=32, help="batch size for training")
-    parser.add_argument('--epoch_num', type=int, default=50, help="training epoch")
-    args = parser.parse_args()
-    
-    world_size = torch.cuda.device_count()
-    main(args.batch_size, args.epoch_num)
-    # mp.spawn(main, args=(world_size, args.batch_size, args.epoch_num), nprocs=world_size)
-