[Feature] Add multi machine dist_train (#734)

* add dist_train with multi machines

* add dist_train with multi machines

docs/en/getting_started.md

@@ -130,7 +130,7 @@ And then run the script [above](#train-with-a-single-gpu).
 The process of training on the CPU is consistent with single GPU training. We just need to disable GPUs before the training process.
 ```
 
-### Train with multiple GPUs
+### Train with multiple GPUs in single machine
 
 ```shell
 ./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [optional arguments]
@@ -148,6 +148,22 @@ Difference between `resume-from` and `load-from`:
 
 ### Train with multiple machines
 
+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.
+
 If you run MMClassification on a cluster managed with [slurm](https://slurm.schedmd.com/), you can use the script `slurm_train.sh`. (This script also supports single machine training.)
 
 ```shell

docs/zh_CN/getting_started.md

@@ -128,7 +128,7 @@ export CUDA_VISIBLE_DEVICES=-1
 我们不推荐用户使用 CPU 进行训练，这太过缓慢。我们支持这个功能是为了方便用户在没有 GPU 的机器上进行调试。
 ```
 
-### 使用多个 GPU 进行训练
+### 使用单台机器多个 GPU 进行训练
 
 ```shell
 ./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [optional arguments]
@@ -146,6 +146,22 @@ export CUDA_VISIBLE_DEVICES=-1
 
 ### 使用多台机器进行训练
 
+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:
+
+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。
+
 如果用户在 [slurm](https://slurm.schedmd.com/) 集群上运行 MMClassification，可使用 `slurm_train.sh` 脚本。（该脚本也支持单台机器上进行训练）
 
 ```shell

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 +2,18 @@
 
 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 \
+    --launcher pytorch ${@:3}