README.md

@@ -25,18 +25,6 @@ python3 -m torch.distributed.run \
 
 ## Testint (in the docker container)
 
-### Build overlay network in Docker
-```
-# Master: Init the cluster
-docker swarm init --advertise-addr=192.168.1.46 --listen-addr=192.168.1.46:2377
-
-# Worker: Join the cluster
-docker swarm join --token TOKEN_FROM_MASTER 192.168.1.46:2377
-
-# Master: Create a overlay network in Docker
-docker network create --driver overlay --attachable train-net
-```
-
 ### Start Downloading Image
 ```
 mkdir ./dataset_dir
@@ -46,18 +34,16 @@ docker run -it --rm -v ./dataset_dir:/dataset YOUR_IMAGE
 ### Start Training Image
 ```
 docker run -it \
-    --rm \
-    --network train-net \
+    --net=host \
     --runtime=nvidia \
     --gpus all \
-    --name train-0 \
     -v ./dataset_dir:/dataset \
     -v ./output:/output \
     -e GPU_NUM=1 \
     -e NODE_NUM=2 \
     -e NODE_RANK=0 \
-    -e MASTER_IP=train-0 \
+    -e MASTER_IP=192.168.1.46 \
     -e MASTER_PORT=21046  \
-    snsd0805/cifar100-train:v3 bash
+    snsd0805/cifar100-train:v2
 ```
 

train/Dockerfile

@@ -1,26 +0,0 @@
-# You can change what base image you want
-# Here, i use torch2.2.2+118
-FROM pytorch/pytorch:2.2.2-cuda11.8-cudnn8-runtime
-
-# You should open 21046 port for communication between multiple machins
-EXPOSE 21046
-
-# Now, I create the directory '/train' for saving the training codes.
-RUN mkdir /train
-
-# You should get your training code here.
-# I use COPY to copy my codes into the image.
-# Or, you can `apt update && apt install git` and use git to pull your code
-# from GitHub, Gitea, or GitLab.
-COPY ./*.py /train
-
-# Training setting
-ENV GPU_NUM 1
-ENV NODE_NUM 1
-ENV NODE_RANK 0
-ENV MASTER_IP "127.0.0.1"
-ENV MASTER_PORT 21046
-
-WORKDIR /train
-CMD export NCCL_SOCKET_IFNAME=eth0 && python3 -m torch.distributed.run --nproc_per_node=$GPU_NUM --nnodes=$NODE_NUM --node_rank=$NODE_RANK --rdzv_id=$MASTER_PORT --rdzv_backend=c10d --rdzv_endpoint=$MASTER_IP:$MASTER_PORT main.py
-

train/trainer.py

@@ -31,7 +31,6 @@ class Trainer():
         self.criterion = criterion
 
     def train(self, epoch_num):
-        print("Start traininig...")
         for epoch in range(epoch_num):
             self.model.train()
             train_loss_sum = 0
@@ -53,7 +52,7 @@ class Trainer():
 
                 self.optimizer.step()
                 self.optimizer.zero_grad()
-            print(f"[RANK {self.global_rank}] EPOCH {epoch} loss={train_loss_sum/len(self.loader)} acc={(train_correct_sum/train_item_counter).item()}")
+            print(f"[DEVICE {self.global_rank}] EPOCH {epoch} loss={train_loss_sum/len(self.loader)} acc={(train_correct_sum/train_item_counter).item()}")
 
     def save(self, model_path):
         torch.save(self.model.state_dict(), model_path)