我在ubuntu 16.04上安装了CUDA和cuDnn。
CUDA版本：9.0 //驱动程序版本390.87
cuDNN版本：7.2 for CUDA9.0
import tensorflow as tf
工作正常，但是
tf.Session()
呈现以下错误。
2018-09-15 16:43:23.281375: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1484] Adding visible gpu devices: 0
2018-09-15 16:43:23.281431: E tensorflow/core/common_runtime/direct_session.cc:158] Internal: cudaGetDevice() failed. Status: CUDA driver version is insufficient for CUDA runtime version
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/imhgchoi/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1494, in __init__
super(Session, self).__init__(target, graph, config=config)
File "/home/imhgchoi/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 626, in __init__
self._session = tf_session.TF_NewSession(self._graph._c_graph, opts)
tensorflow.python.framework.errors_impl.InternalError: Failed to create session.
错误消息暗示我安装了错误版本的CUDA驱动程序，但我迷路了。我不确定采取什么措施来纠正这种情况。
添加环境变量后
那只是增加了新的错误..
2018-09-15 17:13:39.684390: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2018-09-15 17:13:39.767963: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:897] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2018-09-15 17:13:39.768481: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1405] Found device 0 with properties:
name: GeForce GTX 1050 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.506
pciBusID: 0000:09:00.0
totalMemory: 3.94GiB freeMemory: 3.41GiB
2018-09-15 17:13:39.768502: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1484] Adding visible gpu devices: 0
2018-09-15 17:13:39.768635: E tensorflow/core/common_runtime/direct_session.cc:158] Internal: cudaGetDevice() failed. Status: CUDA driver version is insufficient for CUDA runtime version
也许是你的环境变量导致了这个问题。试试这个：
在〜/ .bashrc文件的末尾添加这些行并打开一个终端，然后在那里启动一个python会话然后导入tensorflow（你应该通过apt安装tensporflow-gpu）并查看它是否有效：
sudo vim ~/.bashrc
并在文件的末尾添加这些并重新启动您的终端：
export CUDA_HOME="/usr/local/cuda-9.0"
export LD_LIBRARY_PATH="${CUDA_HOME}/lib64"
export PATH="${CUDA_HOME}/bin:${PATH}"
export DYLD_LIBRARY_PATH="${CUDA_HOME}/lib"
Edit.1
请确保“usr / local / cuda-9.0”是您安装cuda的目录。