Ubuntu18.04安装完应该做的一些事显卡驱动安装和cuda8.0

来源：cnblogs　　作者：Piilu　　时间：2019/3/22 8:49:21　　对本文有异议

　　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