My servers crash and automatically restart while training with tensorflow using more than one GPUs.

My task is like:

```
CUDA_VISIBLE_DEVICES=0 python train.py [LSTM training]
CUDA_VISIBLE_DEVICES=1 python train.py [Another LSTM training]

```

When the tasks are both running, the server crashes and automatically restarts. I tried with different servers, but all servers behave the same.

My hardware configuration:

Dell T7610 Server  

2\* E5-2609 CPU  

1\* Nvidia GTX 1080 (Inno 3D Foundation Edition)  

1\* Nvidia GTX 1080Ti (Gigabyte Foundation Edition)  

64G memory 

My software configuration:

Ubuntu 14.04 LTS (Linux 3.13.0-87-generic)  

Nvidia Driver version 378.13 (nvidia-378 from ppa:graphics-drivers)  

CUDA 8.0 (CuDNN 5.1.5)  

Python 2.7.13 |Anaconda 4.3.0 (64-bit)  

tensorflow-gpu-1.0.1 (from pip install) 

The PSU has enough capacity for the graphics cards. And from memtest results, it's not a memory-related problem. There are not any `syslog` and `kern.log` log that helps to locate the problem. 

I am aware of [a similar question](https://stackoverflow.com/questions/41157215/training-tensorflow-on-multiple-gpu-crashes-the-computer?rq=1). However, a workaround that downgrades the Linux kernel is unacceptable since I share the machines with lab folks.