ubuntu14.04 cuda8.0 cudnn caffe tensorflow opencv

Basic

1、首选装好系统运行下面的代码:(如果已经安装了nvidia显卡容易出现问题，我出错后，重装了系统)

sudo apt-get update    
sudo apt-get upgrade    
sudo apt-get install build-essential    
sudo apt-get autoremove 

2、安装git

sudo apt-get install git   

NVIDIA 驱动安装

下载NVIDIA-Linux-x86_64-367.27.run，下载地址为：

将其与cuda_8.0.27_linux.run（下载地址为：https://developer.nvidia.com/cuda-toolkit 不过需要注册）拷到home/zhou下。

1、将nouveau添加到黑名单，防止它启动

cd /etc/modprobe.d

sudo gedit nvidia-graphics-drivers.conf

检查：

cat nvidia-graphics-drivers.conf

sudo gedit /etc/default/grub

rdblacklist=nouveau nouveau.modeset=0

检查：

cat /etc/default/grub

sudo service lightdm stop

3、安装nvidia driver

//获取权限

sudo chmod 777 NVIDIA-Linux-x86_64-367.27.run

//安装驱动

sudo ./NVIDIA-Linux-x86_64-367.27.run

Accept
Continue installation
安装完成之后

sudo service lightdm start

图形界面出现，然后关机，切换到1080

4、nvidia检查

cat /proc/driver/nvidia/version

cuda8.0
1、 在终端运行指令

 sudo sh cuda_8.0.27_linux.run

Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 361.62?
(y)es/(n)o/(q)uit: n

Install the CUDA 8.0 Toolkit?
(y)es/(n)o/(q)uit: y

Enter Toolkit Location
[ default is /usr/local/cuda-8.0 ]:

Do you want to install a symbolic link at /usr/local/cuda?
(y)es/(n)o/(q)uit: y

Install the CUDA 8.0 Samples?
(y)es/(n)o/(q)uit: y

Enter CUDA Samples Location
[ default is /home/zhou ]:

Installing the CUDA Toolkit in /usr/local/cuda-8.0 …
Missing recommended library: libglu.so
Missing recommended library: libX11.so
Missing recommended library: libXi.so
Missing recommended library: libXmu.so

Installing the CUDA Samples in /home/zhou …
copying samples to /home/zhou/NVIDIA_CUDA-8.0_Samples Now…
Finished copying samples.

===========
= Summary=
===========
Driver: Not Selected
Toolkit: Installed in /usr/local/cuda-8.0
Samples: Installed in /home/zhou,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_2961.log
安装完成，但是缺少一些库。

2、安装所缺少的库

sudo apt-get install freeglut3-dev build-essential libx11-dev libxmu-dev libxi-dev libgl1-mesa-glx libglu1-mesa libglu1-mesa-dev

这个因为网络问题，可能要安装很久。
还是有线好点。

3、

sudo apt-get install vim

安装完成。

4、设置环境变量
在终端输入这两句：

然后修改文件中环境变量设置

sudo gedit ~/.bashrc

export PATH=/usr/local/cuda/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH

然后更新环境变量

source ~/.bashrc

cudnn

1、安装cudnn_v5

    cd ~/Downloads/  
    tar xvf cudnn*.tgz  
    cd cuda  
    sudo cp */*.h /usr/local/cuda/include/ 
    sudo cp */libcudnn* /usr/local/cuda/lib64/  
    sudo chmod a+r /usr/local/cuda/lib64/libcudnn*  

Check

nvidia-smi  

OpenCV

unzip opencv-2.4.13.zip

cd opencv-2.4.13
mkdir release  

sudo apt-get install build-essential cmake libgtk2.0-dev pkg-config Python-dev python-numpy libavcodec-dev libavformat-dev libswscale-dev  

cd release  

cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local ..  

make
sudo make install  

#include <cv.h>  
#include <highgui.h>  
 
using namespace cv;  
 
int main(int argc,char* argv[])  
{  
    Mat image;  
    image = imread(argv[1],1);  
 
    if (argc != 2 || !image.data)   
    {  
        printf("No image data\n");  
        return -1;  
    }  
 
    namedWindow("display Image",CV_WINDOW_AUTOSIZE);  
    imshow("display Image",image);  
    waitKey(0);  
    return 0;  
}

cmake .  

make  

Tensorflow

1、先下载v0.8版的GPU支持

现在的方法：参考https://www.tensorflow.org/versions/r0.12/get_started/os_setup.html#pip-installation

sudo apt-get install python-pip python-dev   

2、下载安装Tensorflow

sudo pip install --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-0.12.0rc0-cp27-none-linux_x86_64.whl  

3、测试Tensorflow

    python  
    >>> import tensorflow as tf  
    >>> exit()  

成功的话会出现以下信息：

>>> import tensorflow as tf
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcublas.so locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcudnn.so locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcufft.so locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcurand.so locally

4、我碰到的一个问题"AttributeError: NewBase is_abstract,ImportError: libcudart.so.7.5"

解决：是因为six版本问题。

sudo pip install six --upgrade --target="/usr/lib/python2.7/dist-packages" 

5、推荐IDE调试工具

pyCharm免费的社区版（community release）不支持远程调试，百度下载然后到bin里面，运行pycharm安装文件就可以了。

安装atlas(如果装了这个就不需要装下面的OpenBLAS了)

sudo apt-get install libatlas-base-dev

OpenBLAS

1、先下载git，然后安装OpenBLAS

2、添加lib库的变量路径

Common Tools

1、安装来自Scipy的普通tools

2、如果安装matplotlib时无法安装，按照下面方法：

先下载:here

然后减压matplotlib-1.5.0，并进入matplotlib-1.5.0里面

最后运行

Caffe

1、caffe相信大家都很熟悉了，下面是一些基础依赖库

2、克隆caffe

3、如果安装了cuDNN然后把Makefile文件的USE_CUDNN := 1注释去掉

4、如果安装了OpenBLAS，修改BLAS参数，如果没有装openblas就不用修改

sed -i 's/BLAS := atlas/BLAS := open/' Makefile.config

5、开启python接口

      WITH_PYTHON_LAYER := 1

MATLAB_DIR := /usr/local/MATLAB/MATLAB_Production_Server/R2015a

8、如果你装了3.0的opencv需要开启这个接口

OPENCV_VERSION := 3

9、安装需求build和测试caffe，编译PyCaffe

sudo pip install -r python/requirements.txt  
make all -j $(($(nproc) + 1))  
make test -j $(($(nproc) + 1))  
make runtest -j $(($(nproc) + 1))  
make pycaffe -j $(($(nproc) + 1))

10、添加caffe的环境变量

    echo 'export CAFFE_ROOT=$(pwd)' >> ~/.bashrc  
    echo 'export PYTHONPATH=$CAFFE_ROOT/python:$PYTHONPATH' >> ~/.bashrc  
    source ~/.bashrc  

    ipython  
    >>> import caffe  
    >>> exit()  

Theano

1、安装Theano

    sudo apt-get install python-numpy python-scipy python-dev python-pip python-nose g++ python-pygments python-sphinx python-nose  
    sudo pip install Theano  

2、测试接口

    python  
    >>> import theano  
    >>> exit()  

Keras

1、这个编译器(关于Theano和Tensorflow)不是很熟悉，顺便安装一下试试。默认是Theano，如果想换Tensorflow，可以按照here.

Torch

1、这个是facebook的深度框架

2、添加环境变量

3、推荐IDE工具eclipse，安装相应的插件(Lua Development Tools)

首先安装eclipse c/c++的开发版，然后在官网搜索Lua，看到LDT就点进去，找到Existing Eclipse installation，按照官网指示安装插件，就ok了。

Mxnet

mxnet是cxxnet的下一代，目前实现了cxxnet所有功能，但借鉴了minerva/torch7/theano，加入更多新的功能。

1、X2GO是一个远程控制桌面，下面是安装教程

    sudo apt-get install software-properties-common  
    sudo add-apt-repository ppa:x2go/stable  
    sudo apt-get update  
    sudo apt-get install x2goserver x2goserver-xsession  

sudo apt-get update  
sudo apt-get install -y xfce4 xfce4-goodies xubuntu-desktop

hostname -I