[Feature] Add multi machine dist_train

docs/en/1_exist_data_model.md

@@ -584,10 +584,23 @@ CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29500 ./tools/dist_train.sh ${CONFIG_FILE} 4
 CUDA_VISIBLE_DEVICES=4,5,6,7 PORT=29501 ./tools/dist_train.sh ${CONFIG_FILE} 4
 ```
 
-### Training on multiple nodes
+### Train with multiple machines
 
-MMDetection relies on `torch.distributed` package for distributed training.
-Thus, as a basic usage, one can launch distributed training via PyTorch's [launch utility](https://pytorch.org/docs/stable/distributed.html#launch-utility).
+If you launch with multiple machines simply connected with ethernet, you can simply run following commands:
+
+On the first machine:
+
+```shell
+NNODES=2 NODE_RANK=0 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR sh tools/dist_train.sh $CONFIG $GPUS
+```
+
+On the second machine:
+
+```shell
+NNODES=2 NODE_RANK=1 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR sh tools/dist_train.sh $CONFIG $GPUS
+```
+
+Usually it is slow if you do not have high speed networking like InfiniBand.
 
 ### Manage jobs with Slurm
 

docs/zh_cn/1_exist_data_model.md

@@ -566,11 +566,25 @@ CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29500 ./tools/dist_train.sh ${CONFIG_FILE} 4
 CUDA_VISIBLE_DEVICES=4,5,6,7 PORT=29501 ./tools/dist_train.sh ${CONFIG_FILE} 4
 ```
 
-#### 在多个节点上训练
+### 使用多台机器训练
 
-MMDetection 是依赖 `torch.distributed` 包进行分布式训练的。因此，我们可以通过 PyTorch 的 [启动工具](https://pytorch.org/docs/stable/distributed.html#launch-utility) 来进行基本地使用。
+如果您想使用由 ethernet 连接起来的多台机器， 您可以使用以下命令:
 
-#### 使用 Slurm 来管理任务
+在第一台机器上:
+
+```shell
+NNODES=2 NODE_RANK=0 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR sh tools/dist_train.sh $CONFIG $GPUS
+```
+
+在第二台机器上:
+
+```shell
+NNODES=2 NODE_RANK=1 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR sh tools/dist_train.sh $CONFIG $GPUS
+```
+
+但是，如果您不使用高速网路连接这几台机器的话，训练将会非常慢。
+
+### 使用 Slurm 来管理任务
 
 Slurm 是一个常见的计算集群调度系统。在 Slurm 管理的集群上，你可以使用 `slurm.sh` 来开启训练任务。它既支持单节点训练也支持多节点训练。
 

tools/dist_test.sh

@@ -3,8 +3,20 @@
 CONFIG=$1
 CHECKPOINT=$2
 GPUS=$3
+NNODES=${NNODES:-1}
+NODE_RANK=${NODE_RANK:-0}
 PORT=${PORT:-29500}
+MASTER_ADDR=${MASTER_ADDR:-"127.0.0.1"}
 
 PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
-python -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT \
-    $(dirname "$0")/test.py $CONFIG $CHECKPOINT --launcher pytorch ${@:4}
+python -m torch.distributed.launch \
+    --nnodes=$NNODES \
+    --node_rank=$NODE_RANK \
+    --master_addr=$MASTER_ADDR \
+    --nproc_per_node=$GPUS \
+    --master_port=$PORT \
+    $(dirname "$0")/test.py \
+    $CONFIG \
+    $CHECKPOINT \
+    --launcher pytorch \
+    ${@:4}

tools/dist_train.sh

@@ -2,8 +2,19 @@
 
 CONFIG=$1
 GPUS=$2
+NNODES=${NNODES:-1}
+NODE_RANK=${NODE_RANK:-0}
 PORT=${PORT:-29500}
+MASTER_ADDR=${MASTER_ADDR:-"127.0.0.1"}
 
 PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
-python -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT \
-    $(dirname "$0")/train.py $CONFIG --launcher pytorch ${@:3}
+python -m torch.distributed.launch \
+    --nnodes=$NNODES \
+    --node_rank=$NODE_RANK \
+    --master_addr=$MASTER_ADDR \
+    --nproc_per_node=$GPUS \
+    --master_port=$PORT \
+    $(dirname "$0")/train.py \
+    $CONFIG \
+    --seed 0 \
+    --launcher pytorch ${@:3}