Ubuntu18.04安装完应该做的一些事 显卡驱动安装和cuda8.0

　　博主装ubuntu18.04主要是为了用于跑深度学习，所以我们先来搞搞gcc环境

第一步：安装多版本gcc、g++可切换

sudo apt-get install gcc-4.8 gcc-4.8-multilib
sudo apt-get install g++-4.8 g++-4.8-multilib
sudo apt-get install gcc-5 gcc-5-multilib
sudo apt-get install g++-5 g++-5-multilib
sudo apt-get install gcc-6 gcc-6-multilib
sudo apt-get install g++-6 g++-6-multilib
sudo apt-get install gcc-7 gcc-7-multilib
sudo apt-get install g++-7 g++-7-multilib
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-4.8 48
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-5 50
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-6 60
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-7 70
sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-4.8 48
sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-5 50
sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-6 60
sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-7 70

　　切换版本命令

sudo update-alternatives --config gcc 
sudo update-alternatives --config g++

　　根据自己想要的环境选择

第二步：准备安装显卡驱动和cuda8.0等相关文件

　　最新cuda8.0 及其补丁

　　cuda_8.0.61.2_linux.run 

　　cuda_8.0.61_375.26_linux.run

　　最新支持cuda8.0的cudnn

　　libcudnn7_7.1.4.18-1+cuda8.0_amd64.deb

　　libcudnn7-dev_7.1.4.18-1+cuda8.0_amd64.deb

　　libcudnn7-doc_7.1.4.18-1+cuda8.0_amd64.deb

　　cuda8.0 安装包解压文件

　　/001/installutils.pm（从cuda_8.0.61.2_linux.run中解压出来的文件，后面会讲到）
第三步：安装显卡驱动

• 1、开机 nomodeset 进入系统
• 开机进引导界面 第一项 按e 进入配置启动
• 在quiet splash - - -后加上 nomodeset
• 按f10 保存 进入系统

quiet splash - - -
quiet splash nomodeset

• 2、禁用系统自带nvidia驱动

sudo vim /etc/modprobe.d/blacklist.conf
# 在文件尾加入
blacklist nouveau
options nouveau modeset=0
# 保存并退出 执行下面命令 更新引导
sudo update-initramfs –u

• 3、安装 nvidia 驱动

# 切换gcc 版本 到gcc-5 以上 (使用高版本感觉会好一点)
# 查看支持的驱动版本
ubuntu-drivers devices
# 安装驱动
sudo ubuntu-drivers autoinstall
# 根据查询的版本安装比较保险 例如
sudo apt-get install nvidia-driver-390
# 装驱动 需要关闭 安全启动

• 5、重启系统

sudo reboot
# 查看nvidia驱动 使用情况
nvidia-smi

• 6、安装cuda8.0
  • 安装依赖

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

  • 切换gcc版本到 4.8

• sudo update-alternatives --config gcc

  • 解压cuda8.0

• sh cuda_8.0.61_375.26_linux.run --noexec --target 001 
  # 将runfile文件解压并且放到001文件夹中
  # 将instalutil.pm 拷贝到 /etc/perl/
  sudo cp instalutil.pm /etc/perl/

  • 安装cuda8.0及补丁

• # 可选 加运行权限 
  chmod u+x cuda_8.0.61_375.26_linux.run
  chmod u+x cuda_8.0.61.2_linux.run
  # 运行
  sudo ./chmod u+x cuda_8.0.61_375.26_linux.run
  
  do you accept the previously read eula?
  accept/decline/quit: accept
  
  you are attempting to install on an unsupported configuration. do you wish to continue?
  (y)es/(n)o [ default is no ]: y
  
  install nvidia accelerated graphics driver for linux-x86_64 375.26?
  (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/deep ]:
  
  # 安装补丁
  sudo ./cuda_8.0.61.2_linux.run

  • 添加环境变量

• cd 
  vim .bashrc
  # 添加到文件尾部
  export path=/usr/local/cuda-8.0/bin:$path 
  export ld_library_path=/usr/local/cuda-8.0/lib64$ld_library_path
  # 保存 退出
  sudo su
  source .bashrc

  • 重启系统

• sudo reboot

• 安装cudnn

• sudo dpkg -i libcudnn7_7.1.4.18-1+cuda8.0_amd64.deb
  sudo dpkg -i libcudnn7-dev_7.1.4.18-1+cuda8.0_amd64.deb
  sudo dpkg -i libcudnn7-doc_7.1.4.18-1+cuda8.0_amd64.deb

  • 查看cuda版本和cudnn版本

• # cuda 版本
  cat /usr/local/cuda/version.txt
  # cudnn 版本
  cat /usr/include/x86_64-linux-gnu/cudnn_v7.h | grep cudnn_major -a 2

  • 编译

• # 不用编译全部 只编译devicequery
  cd /home/deep/nvidia_cuda-8.0_samples/1_utilities/devicequery
  make

  • 测试

• ./devicequery
  
  # 出现显卡信息
  ./devicequery starting...
  
  cuda device query (runtime api) version (cudart static linking)
  
  detected 1 cuda capable device(s)
  
  device 0: "geforce gtx 1080"
    cuda driver version / runtime version          9.1 / 8.0
    cuda capability major/minor version number:    6.1
    total amount of global memory:                 8116 mbytes (8510701568 bytes)
    (20) multiprocessors, (128) cuda cores/mp:     2560 cuda cores
    gpu max clock rate:                            1734 mhz (1.73 ghz)
    memory clock rate:                             5005 mhz
    memory bus width:                              256-bit
    l2 cache size:                                 2097152 bytes
    maximum texture dimension size (x,y,z)         1d=(131072), 2d=(131072, 65536), 3d=(16384, 16384, 16384)
    maximum layered 1d texture size, (num) layers  1d=(32768), 2048 layers
    maximum layered 2d texture size, (num) layers  2d=(32768, 32768), 2048 layers
    total amount of constant memory:               65536 bytes
    total amount of shared memory per block:       49152 bytes
    total number of registers available per block: 65536
    warp size:                                     32
    maximum number of threads per multiprocessor:  2048
    maximum number of threads per block:           1024
    max dimension size of a thread block (x,y,z): (1024, 1024, 64)
    max dimension size of a grid size    (x,y,z): (2147483647, 65535, 65535)
    maximum memory pitch:                          2147483647 bytes
    texture alignment:                             512 bytes
    concurrent copy and kernel execution:          yes with 2 copy engine(s)
    run time limit on kernels:                     yes
    integrated gpu sharing host memory:            no
    support host page-locked memory mapping:       yes
    alignment requirement for surfaces:            yes
    device has ecc support:                        disabled
    device supports unified addressing (uva):      yes
    device pci domain id / bus id / location id:   0 / 1 / 0
    compute mode:
       < default (multiple host threads can use ::cudasetdevice() with device simultaneously) >
  
  devicequery, cuda driver = cudart, cuda driver version = 9.1, cuda runtime version = 8.0, numdevs = 1, device0 = geforce gtx 1080
  result = pass

   

如果出现相应的显卡信息表示安装成功了。