add multi-machine dist_train (#1303)

docs/en/1_exist_data_model.md

@@ -201,30 +201,23 @@ GPUS=16 ./tools/slurm_train.sh dev pp_kitti_3class hv_pointpillars_secfpn_6x8_16
 
 You can check [slurm_train.sh](https://github.com/open-mmlab/mmdetection/blob/master/tools/slurm_train.sh) for full arguments and environment variables.
 
-You can also use pytorch original DDP with script `multinode_train.sh`. (This script also supports single machine training.)
+If you launch with multiple machines simply connected with ethernet, you can simply run following commands:
 
-For each machine, run
-```shell
-./tools/sh_train.sh ${CONFIG_FILE} ${NODE_NUM} ${NODE_RANK} ${MASTER_NODE_IP}
-```
-
-Here is an example of using 16 GPUs (2 nodes), the IP=10.10.10.10:
+On the first machine:
 
-run in node0: 
 ```shell
-./tools/sh_train.sh hv_pointpillars_secfpn_6x8_160e_kitti-3d-3class.py 2 0 10.10.10.10
+NNODES=2 NODE_RANK=0 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR ./tools/dist_train.sh $CONFIG $GPUS
 ```
 
-run in node1: 
+On the second machine:
+
 ```shell
-./tools/sh_train.sh hv_pointpillars_secfpn_6x8_160e_kitti-3d-3class.py 2 1 10.10.10.10
+NNODES=2 NODE_RANK=1 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR ./tools/dist_train.sh $CONFIG $GPUS
 ```
 
-
-If you have just multiple machines connected within ethernet, you can refer to
-PyTorch [launch utility](https://pytorch.org/docs/stable/distributed.html).
 Usually it is slow if you do not have high speed networking like InfiniBand.
 
+
 ### Launch multiple jobs on a single machine
 
 If you launch multiple jobs on a single machine, e.g., 2 jobs of 4-GPU training on a machine with 8 GPUs,

docs/zh_cn/1_exist_data_model.md

@@ -198,7 +198,21 @@ GPUS=16 ./tools/slurm_train.sh dev pp_kitti_3class hv_pointpillars_secfpn_6x8_16
 
 你可以查看 [slurm_train.sh](https://github.com/open-mmlab/mmdetection/blob/master/tools/slurm_train.sh) 来获取所有的参数和环境变量。
 
-如果你有多个机器连接到以太网，可以参考 PyTorch 的 [launch utility](https://pytorch.org/docs/stable/distributed.html)，如果你没有像 InfiniBand 一样的高速率网络，通常会很慢。
+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 ./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 ./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。
 
 ### 在单个机器上启动多个任务
 

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}