Ubuntu14.0 + CUDA9.0 + cudnn7.0 + TensorFlow-gpu1.7.0

在安装好nvidia驱动的基础上安装 CUDA9.0 + cudnn7.0 + TensorFlow-gpu1.7.0 这三个是匹配的版本

别的匹配（CUDA8.0 + cudnn6.0 + TensorFlow-gpu1.4.0），更高版本没有了解，日后补充

1.安装CUDA9.0

下载CUDA9.0 runfile 文件，执行

sudo sh cuda_9..run

一路accept,yes，设置cuda_toolkit 和cuda_samples路径

然后设置环境变量

sudo gedit ~/.bash_profile

export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64"
export CUDA_HOME=/usr/local/cuda
export PATH=$PATH:/usr/local/cuda/bin

source ~/.bash_profile

2.安装cudnn

这里的路径是自己解压cudnn压缩包后的路径

tar xvzf cudnn-8.0-linux-x64-v6.-tgz
sudo cp cuda/include/cudnn.h /usr/local/cuda/include
sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64
sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*

在 ~/.bashrc文件中配置环境变量

export CUDNN_HOME=/home/xxx/local/cudnn/cuda
export LD_LIBRARY_PATH=${CUDNN_HOME}/lib64:$LD_LIBRARY_PATH
export CPLUS_INCLUDE_PATH=${CUDNN_HOME}/include:$CPLUS_INCLUDE_PATH

source ~/.bashrc

3.安装TensorFlow 1.7GPU版

sudo pip install tensorflow-gpu==1.7.

4.查看cuda版本和cudnn版本

cat /usr/local/cuda/version.txt

cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 

查看cuda_toolkit是否正确配置

nvcc --version