Segmentation fault (Core dump) when using GPU

Recently, the GPU driver on the server is updated to 390.12 by the IT support, then I also update the CUDA9 and cudnn library corresponding. However, since then I started to get segmentation error once I call .cuda() function. I attach the following stack traces. The example I use is the official mnist example:

gdb python
GNU gdb (GDB) Red Hat Enterprise Linux 7.6.1-100.el7_4.1
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License GPLv3+: GNU GPL version 3 or later <>
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Reading symbols from /home/zhe/anaconda3/envs/pytorch_cuda9/bin/python3.6...done.
(gdb) r 
Starting program: /home/zhe/anaconda3/envs/pytorch_cuda9/bin/python
[Thread debugging using libthread_db enabled]
Using host libthread_db library "/lib64/".
Missing separate debuginfo for /home/zhe/anaconda3/envs/pytorch_cuda9/lib/python3.6/site-packages/numpy/../../../
Detaching after fork from child process 34372.
Detaching after fork from child process 34373.
Detaching after fork from child process 34374.
[New Thread 0x7fffac683700 (LWP 34377)]
[New Thread 0x7fffa8a2a700 (LWP 34379)]

Program received signal SIGSEGV, Segmentation fault.
[Switching to Thread 0x7fffa8a2a700 (LWP 34379)]
0x00007fffeac828d5 in ?? () from /usr/lib64/nvidia/
Missing separate debuginfos, use: debuginfo-install glibc-2.17-196.el7_4.2.x86_64 libuuid-2.23.2-43.el7_4.2.x86_64
(gdb) bt
#0  0x00007fffeac828d5 in ?? () from /usr/lib64/nvidia/
#1  0x00007fffeadd2914 in ?? () from /usr/lib64/nvidia/
#2  0x00007fffead6ee80 in ?? () from /usr/lib64/nvidia/
#3  0x00007ffff7bc6e25 in start_thread () from /lib64/
#4  0x00007ffff78f434d in clone () from /lib64/

did you reinstall with the cuda 9 version?

Yes, I install a local anaconda 3 and install the cuda 9 version pytorch.

Did you manage to solve this issue?

I get exactly the same error when I try to use the GPU (Tesla K80) on Scientific Linux. Anaconda 3, Cuda 9.1, Nvidia Driver version 390.30.

EDIT: Apparently, for me it is a GPU related issue. When I run nvidia-smi, I detected that some of my GPUs have a “Volatile Uncorr. ECC”. When I set CUDA_VISIBLE_DEVICES to anything other than those, I can run the code on GPU. The issue should be resolved when I reset the GPUs and reboot.

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