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
第三步：安装显卡驱动
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1、开机 nomodeset 进入系统
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开机进引导界面 第一项 按e 进入配置启动
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在quiet splash - - -后加上 nomodeset
- 按F10 保存 进入系统
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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
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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
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sudo update-alternatives --config gcc
- 解压cuda8.0
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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
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# 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
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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
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sudo reboot- 安装cudnn
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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
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# 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
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# 不用编译全部 只编译deviceQuery cd /home/deep/NVIDIA_CUDA-8.0_Samples/1_Utilities/deviceQuery make
- 测试
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./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。