When I execution the example within nvidia-docker, it cann't achieve the result

(base) root@e9f21ccb6520:/workspace/apex/examples/simple/distributed# bash run.sh
Selected optimization level O1: Insert automatic casts around Pytorch functions and Tensor methods.

Defaults for this optimization level are:
enabled : True
opt_level : O1
cast_model_type : None
patch_torch_functions : True
keep_batchnorm_fp32 : None
master_weights : None
loss_scale : dynamic
Processing user overrides (additional kwargs that are not None)...
After processing overrides, optimization options are:
enabled : True
opt_level : O1
cast_model_type : None
patch_torch_functions : True
keep_batchnorm_fp32 : None
master_weights : None
loss_scale : dynamic

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The cursor is waiting for the results always...
But when I set “--nproc_per_node=1“ within run.sh , then run it, it can works fine.
There are 6 GPUs in my computer.
CUDA Version 9.0.176
pytorch 1.1.0
python 3.7.3

Start with examples/simple/distributed/run.sh and reproduce the hang with its default multi-GPU settings, then compare it with --nproc_per_node=1. Use the reported CUDA 9.0.176, PyTorch 1.1.0, Python 3.7.3, and six-GPU setup when investigating; done means the distributed example completes and produces its expected result.