我是 TensorFlow 的新手。
我刚刚安装了 TensorFlow 并测试安装，我尝试了以下代码，一旦我启动 TF 会话，我就会收到分段错误（核心转储）错误。
bafhf@remote-server:~$ python
Python 3.6.5 |Anaconda, Inc.| (default, Apr 29 2018, 16:14:56)
[GCC 7.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
/home/bafhf/anaconda3/envs/ismll/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
from ._conv import register_converters as _register_converters
>>> tf.Session()
2018-05-15 12:04:15.461361: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1349] Found device 0 with properties:
name: Tesla K80 major: 3 minor: 7 memoryClockRate(GHz): 0.8235
pciBusID: 0000:04:00.0
totalMemory: 11.17GiB freeMemory: 11.10GiB
Segmentation fault (core dumped)
我的nvidia-smi是：
Tue May 15 12:12:26 2018
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 390.30 Driver Version: 390.30 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 Tesla K80 On | 00000000:04:00.0 Off | 0 |
| N/A 38C P8 26W / 149W | 0MiB / 11441MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 1 Tesla K80 On | 00000000:05:00.0 Off | 2 |
| N/A 31C P8 29W / 149W | 0MiB / 11441MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
而nvcc --version是：
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2017 NVIDIA Corporation
Built on Fri_Sep__1_21:08:03_CDT_2017
Cuda compilation tools, release 9.0, V9.0.176
还有gcc --version是：
gcc (Ubuntu 5.4.0-6ubuntu1~16.04.9) 5.4.0 20160609
Copyright (C) 2015 Free Software Foundation, Inc.
This is free software; see the source for copying conditions. There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
以下是我的PATH：
/home/bafhf/bin:/home/bafhf/.local/bin:/usr/local/cuda/bin:/usr/local/cuda/lib:/usr/local/cuda/extras/CUPTI/lib:/home/bafhf/anaconda3/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin
和LD_LIBRARY_PATH：
/usr/local/cuda/bin:/usr/local/cuda/lib:/usr/local/cuda/extras/CUPTI/lib
我在服务器上运行它，我没有 root 权限。我仍然按照官方网站上的说明安装了所有东西。
编辑：新观察：
似乎 GPU 正在为该进程分配内存一秒钟，然后抛出核心分段转储错误：
Edit2：更改了 tensorflow 版本
我将我的 tensorflow 版本从 v1.8 降级到 v1.5。问题仍然存在。
有没有办法解决或调试这个问题？