为了装TensorFlow 1.10 下面升级一下系统的软件环境
NVIDIA DRIVER
去官网下载最新的linux驱动 http://www.nvidia.com/Download/index.aspx
直接运行会报错
sudo bash NVIDIA-Linux-x86_64-390.87.run
ERROR: You appear to be running an X server; please exit X before
installing. For further details, please see the section INSTALLING
THE NVIDIA DRIVER in the README available on the Linux driver
download page at www.nvidia.com.
需要先关闭图形界面，在另一台电脑上用ssh登录这台电脑然后运行
sudo init
sudo killall Xorg
然后再运行
sudo bash NVIDIA-Linux-x86_64-390.87.run
装好后运行
nvidia-smi
出现下图结果说明成功安装
再运行下面命令恢复图形界面
sudo init
可以重启一下确认显卡驱动是否正常
如果需要改gcc 或g++版本 请参考上一篇博文 https://www.cnblogs.com/jins-note/p/9597210.html
CUDA 9.2
由于TensorFlow 1.10 支持cuda 9.2
去官网下载最新版本
https://developer.nvidia.com/cuda-downloads?target_os=Linux&target_arch=x86_64&target_distro=Ubuntu&target_version=1710&target_type=runfilelocal
先安装一些推荐库
sudo apt-get install freeglut3-dev build-essential libx11-dev libxmu-dev libxi-dev libgl1-mesa-glx libglu1-mesa libglu1-mesa-dev libglfw3-dev libgles2-mesa-dev
这里注意：cuda里带的驱动比刚从官网下的新，那么用cuda里带的驱动（居然比官网下的显卡驱动新？？）
不然会报错
cudaerrorinsufficientdriver
然后安装
sudo init
sudo killall Xorg
sudo bash cuda_9..148_396.37_linux.run
安装过程如下
Description The NVIDIA CUDA Toolkit provides command-line and graphical
Do you accept the previously read EULA?
accept/decline/quit: accept Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 396.37?
(y)es/(n)o/(q)uit: y Install the CUDA 9.2 Toolkit?
(y)es/(n)o/(q)uit: y Enter Toolkit Location
[ default is /usr/local/cuda-9.2 ]: Do you want to install a symbolic link at /usr/local/cuda?
(y)es/(n)o/(q)uit: y Install the CUDA 9.2 Samples?
(y)es/(n)o/(q)uit: y Enter CUDA Samples Location
[ default is /home/whatever ]:
安装 补丁
sudo bash cuda_9.2.148.1_linux.run
装好后修改环境变量 ~/.bashrc 在末尾添加
export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64"
export CUDA_HOME=/usr/local/cuda
export PATH="$CUDA_HOME/bin:$PATH"
修改完毕之后执行一下使其生效：
source ~/.bashrc
恢复显示
sudo init
装好后去 CUDA Samples 目录编译一些例子看看能不能运行，能运行就ok
cd ~/NVIDIA_CUDA-.2_Samples/
make -j8
编译好后去下面目录里运行
cd bin/x86_64/linux/release
cuDNN
去官网下载对应版本 https://developer.nvidia.com/rdp/cudnn-download 需要登录才能下载
下载后的文件后缀名应该是 *.tgz 如果 是 .solitairetheme8 那就改成 .tgz
安装很简单，解压复制到对应目录就好
tar -zxvf cudnn-9.2-linux-x64-v7.2.1..tgz
sudo cp cuda/include/cudnn.h /usr/local/cuda/include/
sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64/ -d
sudo chmod a+r /usr/local/cuda/include/cudnn.h
sudo chmod a+r /usr/local/cuda/lib64/libcudnn*
TensorFlow 1.10
安装 Anaconda 从这里下载 https://www.anaconda.com/download/
更换为国内源 https://mirrors.ustc.edu.cn/help/anaconda.html
conda config --add channels https://mirrors.ustc.edu.cn/anaconda/pkgs/free/
conda config --add channels https://mirrors.ustc.edu.cn/anaconda/pkgs/main/
conda config --add channels https://mirrors.ustc.edu.cn/anaconda/cloud/conda-forge/
conda config --set show_channel_urls yes
然后安装
conda install tensorflow-gpu==1.10
装好后测试
import tensorflow as tf
tf.__version__
参考： https://cuiqingcai.com/5822.html