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- gcc--multilib
sudo apt-get install g++- g++--multilib
sudo apt-get install gcc- gcc--multilib
sudo apt-get install g++- g++--multilib
sudo apt-get install gcc- gcc--multilib
sudo apt-get install g++- g++--multilib
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-4.8
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-
sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-4.8
sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-
sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-
sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++- 

　　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=
# 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- 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-
# 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..61_375.26_linux.run --noexec --target
  # 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..61_375.26_linux.run
  chmod u+x cuda_8.0.61.2_linux.run
  # 运行
  sudo ./chmod u+x cuda_8..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.-+cuda8.0_amd64.deb
  sudo dpkg -i libcudnn7-dev_7.1.4.-+cuda8.0_amd64.deb
  sudo dpkg -i libcudnn7-doc_7.1.4.-+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 

  • 编译

• # 不用编译全部 只编译deviceQuery
  cd /home/deep/NVIDIA_CUDA-.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  CUDA Capable device(s)
  
  Device : "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:                  MBytes ( bytes)
    () Multiprocessors, () CUDA Cores/MP:      CUDA Cores
    GPU Max Clock rate:                             MHz (1.73 GHz)
    Memory Clock rate:                              Mhz
    Memory Bus Width:                              -bit
    L2 Cache Size:                                  bytes
    Maximum Texture Dimension Size (x,y,z)         1D=(), 2D=(, ), 3D=(, , )
    Maximum Layered 1D Texture Size, (num) layers  1D=(),  layers
    Maximum Layered 2D Texture Size, (num) layers  2D=(, ),  layers
    Total amount of constant memory:                bytes
    Total amount of shared memory per block:        bytes
    Total number of registers available per block:
    Warp size:
    Maximum number of threads per multiprocessor:
    Maximum number of threads per block:
    Max dimension size of a thread block (x,y,z): (, , )
    Max dimension size of a grid size    (x,y,z): (, , )
    Maximum memory pitch:                           bytes
    Texture alignment:                              bytes
    Concurrent copy and kernel execution:          Yes with  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:    /  /
    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 = , Device0 = GeForce GTX
  Result = PASS

如果出现相应的显卡信息表示安装成功了。