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Ubunto16.04+1070ti 安装Cuda8.0 和 t

Ubunto16.04+1070ti 安装Cuda8.0 和 t

作者: PROoshio | 来源:发表于2018-05-05 22:07 被阅读0次

      最近老板突然要说做项目,双手一挥就申请了张显卡,因此记录下这篇文档;

    系统 / Ubunto16.04
    显卡 / Nvidia GTX 1070ti

    • NVIDIA显卡驱动

      • 安装准备

        • 屏蔽nouveau开源驱动
        touch /etc/modprobe.d/blacklist-nouveau.conf 
        echo "blacklist nouveau" >>blacklist-nouveau.conf 
        echo "options nouveau modeset = 0" >>blacklist-nouveau.conf
        
        • 更新前可以去blacklist-nouveau.conf查看命令是否添加成功,之后执行更新:
        sudo update-initramfs -u
        
        • Nvidia官网下载和显卡对应的驱动,我的是GTX1070ti,对应的最新的驱动是NVIDIA-Linux-x86_64-390.48.run
      • 安装NVIDIA显卡驱动:

        • 进入字符界面Ctrl+alt+F1之后,输入同户名和密码,登陆成功后执行:
        sudo service lightdm stop
        
        • 安装:其中–no-opengl-files很重要,不然安装后重启会出现循环登录的问题。
         sudo chmod 777 NVIDIA-Linux-x86_64-390.48.run   //执行权限
         sudo sh NVIDIA-Linux-x86_64-390.48.run –no-opengl-files   //执行
         sudo service lightdm start
         sudo reboot
        
        • 重启如果能够顺利登录,恭喜,之后测试是否安装成功:
        nvidia-smi
        
        打印出gpu相关信息表示安装成功。
      • 安装CUDA-8.0

        • 安装依赖:
          • 设置源:
          # deb cdrom:[Ubuntu 16.04 LTS _Xenial Xerus_ - Release amd64 (20160420.1)]/ xenial main restricted
          deb-src http://archive.ubuntu.com/ubuntu xenial main restricted #Added by software-properties
          deb http://mirrors.aliyun.com/ubuntu/ xenial main restricted
          deb-src http://mirrors.aliyun.com/ubuntu/ xenial main restricted multiverse universe #Added by software-properties
          deb http://mirrors.aliyun.com/ubuntu/ xenial-updates main restricted
          deb-src http://mirrors.aliyun.com/ubuntu/ xenial-updates main restricted multiverse universe #Added by software-properties
          deb http://mirrors.aliyun.com/ubuntu/ xenial universe
          deb http://mirrors.aliyun.com/ubuntu/ xenial-updates universe
          deb http://mirrors.aliyun.com/ubuntu/ xenial multiverse
          deb http://mirrors.aliyun.com/ubuntu/ xenial-updates multiverse
          deb http://mirrors.aliyun.com/ubuntu/ xenial-backports main restricted universe multiverse
          deb-src http://mirrors.aliyun.com/ubuntu/ xenial-backports main restricted universe multiverse #Added by software-properties
          deb http://archive.canonical.com/ubuntu xenial partner
          deb-src http://archive.canonical.com/ubuntu xenial partner
          deb http://mirrors.aliyun.com/ubuntu/ xenial-security main restricted
          deb-src http://mirrors.aliyun.com/ubuntu/ xenial-security main restricted multiverse universe #Added by software-properties
          deb http://mirrors.aliyun.com/ubuntu/ xenial-security universe
          deb http://mirrors.aliyun.com/ubuntu/ xenial-security multiverse
          
          将aliyun的源添加到/etc/apt/source.list中;
        • 安装相关依赖库:
          sudo apt-get install freeglut3-dev build-essential libx11-dev
          sudo apt-get install libxmu-dev libxi-dev libgl1-mesa-glx libglu1-mesa
          sudo apt-get install libglu1-mesa-dev
          ```
        
      • 安装:ubunto16系统默认的gcc-5.4.0就支持cuda-8.0,我的cuda-runfile文件是cuda_8.0.61_375.26_linux-run

         sudo sh cuda_8.0.44_linux.run --no-opengl-libs
        

        这里没有安装opengl,不会出现循环登录的bug;

        • 添加环境变量
        vim ~/.bashrc
        export PATH=/usr/local/cuda-8.0/bin:$PATH
        export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64:$LD_LIBRARY_PATH
        sudo vim /etc/profile
        export CUDA_HOME=/usr/local/cuda-8.0
        
        • 设置动态链接库
        sudo vim /etc/profile
        

        写入

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

        创建cuda.conf文件

        sudo gedit /etc/ld.so.conf.d/cuda.conf
        

        添加以下路径

        /usr/local/cuda/lib64
        

        执行链接生效

        sudo ldconfig
        sudo reboot
        
        • 测试cuda是否安装成功
        cd /usr/local/cuda-8.0/samples/1_Utilities/deviceQuery 
        sudo make 
        ./deviceQuery
        
        得到以下结果表示安装成功。
      • 安装cuDNN-5.1

        • Cuda8.0对应的cnDNN版本是5.1,去官网注册下载;
        • 下载之后解压,将cuDNN里的文件copy到CUDA目录;
        sudo cp cudnn.h /usr/local/cuda/include/ 
        sudo cp lib* /usr/local/cuda/lib64/
        cd /usr/local/cuda/lib64/ 
        sudo rm -rf libcudnn.so libcudnn.so.5
        sudo ln -s libcudnn.so.5.1.5 libcudnn.so.5 
        sudo ln -s libcudnn.so.5 libcudnn.so 
        
      • 安装python

        • 安装setuptools依赖的zlib库;
        download:http://www.zlib.net/
        ./configure --prefix=/usr/local/zlib/
        make
        make install
        

        添加链接;

        //将--prefix目录添加到zlib.conf中
        sudo vim /etc/ld.so.conf.d/zlib.conf
        ldconfig
        
        • 安装setuptools;
        download:https://pypi.org/project/setuptools/
        sudo python setup.py install
        
        • 安装pip;
        sudo python setup.py install
        
      • 安装tensorflow

        pip安装:

        //gpu-python2
        sudo pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0-cp27-none-linux_x86_64.whl
        //cpu-python2
        sudo pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp27-none-linux_x86_64.whl
        //gpu-python3
        sudo pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.2.0-cp34-cp34m-linux_x86_64.whl
        //cpu-python3
        sudo pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0-cp34-cp34m-linux_x86_64.whl
        
      • 总结

        这样环境就搭好了,可以愉快的烧GPU啦~

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