linux从零开始安装nvidia驱动和tensorflow

安装nvidia驱动和CUDA

1. 下载驱动和CUDA安装包，在官网下载对应版本就行
2. sudo apt-get install linux-headers-$(uname -r) 或者 linux-headers-generic.否则直接安装会报错 kernel not found
3. 安装 nvidia 驱动，一路accept和yes
4. 安装 CUDA，一路yes。安装路径：/usr/local/cuda-8.0/。是否安装推荐的驱动, no
   最后显示类似下面Summary内容，表示安装成功。
5. 在/etc/profile中添加：

export PATH=/usr/local/cuda-8.0/lib64:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH

保存后

# source /etc/profile
# nvcc -V  检查CUDA
# apt-get install cmake 安装cmake
# cd  /usr/local/cuda-8.0/samples
# make  测试CUDA

测试时间较长，一段没有error即可中止

===========
= Summary =
===========

Driver:   Not Selected
Toolkit:  Installed in /usr/local/cuda-8.0
Samples:  Installed in /storage/installers/cuda_samples, but missing recommended libraries

Please make sure that
 -   PATH includes /usr/local/cuda-8.0/bin
 -   LD_LIBRARY_PATH includes /usr/local/cuda-8.0/lib64, or, add /usr/local/cuda-8.0/lib64 to /etc/ld.so.conf and run ldconfig as root

To uninstall the CUDA Toolkit, run the uninstall script in /usr/local/cuda-8.0/bin

Please see CUDA_Installation_Guide_Linux.pdf in /usr/local/cuda-8.0/doc/pdf for detailed information on setting up CUDA.

***WARNING: Incomplete installation! This installation did not install the CUDA Driver. A driver of version at least 361.00 is required for CUDA 8.0 functionality to work.
To install the driver using this installer, run the following command, replacing  with the name of this run file:
    sudo .run -silent -driver

Logfile is /tmp/cuda_install_16018.log

安装CUDNN

1. 下载cudnn-8.0-linux-x64-v5.1.tgz
2. tar -xvzf cudnn-8.0-linux-x64-v5.1.tgz 解压完会有一个cuda文件夹

# cd cuda
# cp  include/cudnn.h
 /usr/local/cuda/include
# cp  lib64/libcudnn.*   /usr/local/cuda/lib64

cuDNN安装完成！
有的博客说要建立软链接，但是我没有做，步骤如下：

# cd  /usr/local/cuda/lib64 
# rm  -rf  libcudnn.so  libcudnn.so.5
# ln  -s  libcudnn.5.1.3  libcudnn.so.5
# ln  -s  libcudnn.so.5  libcudnn.so

---

首先安装anaconda, 因为很多python库都包含在里面了,一次性安装很方便

安装anaconda

1. 从官网下载最新的anaconda安装包，我下的是Anaconda2-4.2.0
2. bash Anaconda2-4.2.0-Linux-x86_64.sh
3. PREFIX=/usr/share/anaconda2
4. # vim /etc/profile (添加环境变量)
   export PATH=$PATH:/usr/share/anaconda2/bin
   再source /etc/profile生效
   修改镜像文件，使得系统默认python为anaconda中的python

# mv /usr/bin/python  /usr/bin/python_bk
# ln -s /usr/share/anaconda2/bin/python  /usr/bin/python

安装h5py

conda install h5py #注意必须先安装anaconda2
这时会提示升级anaconda,yes即可

安装tensorflow

ln -s /usr/anaconda2/bin/pip /usr/bin/pip 建立软连接
github下载 tensorflow_gpu-0.12.0rc0-cp27-none-linux-x86_64.whl
pip install tensorflow_gpu-0.12.0rc0-cp27-none-linux-x86_64.whl
安装完成。测试：
python && import tensorflow 测试tensorflow
或者：

# cd  /usr/share/anaconda2/lib/python2.7/site-packages/tensorflow/models/image/mnist
# CUDA_VISIBLE_DEVICES = 0(选择显卡)  python convolutional.py

开头出现以下字样表示安装成功：

Python 2.7.12 |Anaconda 4.2.0 (64-bit)| (default, Jul  2 2016, 17:42:40) 
[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
Anaconda is brought to you by Continuum Analytics.
Please check out: http://continuum.io/thanks and https://anaconda.org
>>> import tensorflow as tf
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcublas.so locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcudnn.so locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcufft.so locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcurand.so locally