Multi Node Distribute Training

I want to know if apex supports multi-node GPU distribute training , I follow pytorch Document to use distributed.initilize(). In my case, I have two nodes, each node has 4 GPUs. I use the following command:
In node 0: python -m torch.distributed.launch --nproc_per_node=$NGPUS --master_port=2345 --nnodes=2 --node_rank=0 --master_addr="192.168.0.1" tools/train_net.py
In node 1: python -m torch.distributed.launch --nproc_per_node=$NGPUS --master_port=2345 --nnodes=2 --node_rank=1 --master_addr="192.168.0.1" tools/train_net.py
The torch.distributed.initialize is **torch.distributed.init_process_group(backend="nccl",init_method="env://" ) **
When I try to run code, in node 0 it print a little training info, but in node 1 it provided some bug info like *python3.7/site-packages/apex/parallel/distributed.py", line 239, in init
call(coalesced, extra_args) RuntimeError: NCCL error in: /opt/conda/conda-bld/pytorch-nightly_1553663942394/work/torch/lib/c10d/ProcessGroupNCCL.cpp:260, unhandled system error

Start with the two-node launch commands and the call to torch.distributed.init_process_group in the issue, then inspect apex/parallel/distributed.py around line 239 and the entry point tools/train_net.py. Reproduce the NCCL error on both nodes and determine whether the reported configuration is supported; done means the cause is identified and the supported behavior or required configuration is documented.