linux上安装NVIDIA显卡驱动以及深度学习需要的cudn、cudnn、pytorch

1.安装显卡驱动必要的依赖

yum -y install pciutils
yum install -y epel-release  
yum install -y kernel-headers kernel-devel dkms

2.执行shell脚本禁用nouveau驱动

cat >>/lib/modprobe.d/dist-blacklist.conf<

3.安装驱动Official Advanced Driver Search | NVIDIA

chmod +x NVIDIA-Linux-x86_64-460.106.00.run
sh NVIDIA-Linux-x86_64-460.106.00.run

验证是否安装成功

4.安装cuda 

查看驱动对应的cuda版本Release Notes :: CUDA Toolkit Documentation

CUDA 工具包	CUDA 次要版本兼容性所需的最低驱动程序版本*
	Linux x86_64 驱动版本	Windows x86_64 驱动程序版本
CUDA 11.6.x	>=450.80.02	>=452.39
CUDA 11.5.x	>=450.80.02	>=452.39
CUDA 11.4.x	>=450.80.02	>=452.39
CUDA 11.3.x	>=450.80.02	>=452.39
CUDA 11.2.x	>=450.80.02	>=452.39
CUDA 11.1 (11.1.0)	>=450.80.02	>=452.39
CUDA 11.0 (11.0.3)	>=450.36.06**	>=451.22**

wget https://developer.download.nvidia.com/compute/cuda/11.3.0/local_installers/cuda-repo-rhel7-11-3-local-11.3.0_465.19.01-1.x86_64.rpm
sudo rpm -i cuda-repo-rhel7-11-3-local-11.3.0_465.19.01-1.x86_64.rpm
sudo yum clean all
sudo yum -y install nvidia-driver-latest-dkms cuda
sudo yum -y install cuda-drivers

配置cuda环境变量

vi /etc/profile

文件末尾添加
export CUDA_HOME=/usr/local/cuda-11.3
export PATH=/usr/local/cuda-11.3/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-11.3/lib64:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=/usr/local/cuda-11.3/lib:$LD_LIBRARY_PATH

刷新环境变量使其生效

source /etc/profile

验证是否安装成功

5.安装cudnn https://developer.nvidia.com/rdp/cudnn-archive

tar -zxvf cudnn-11.3-linux-x64-v8.2.1.32.tgz
sudo cp cuda/include/cudnn.h /usr/local/cuda/include/
sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64/
sudo chmod a+r /usr/local/cuda/include/cudnn.h
sudo chmod a+r /usr/local/cuda/lib64/libcudnn*

 6.安装conda

wget https://repo.anaconda.com/archive/Anaconda3-2021.11-Linux-x86_64.sh

chmod +x Anaconda3-2021.11-Linux-x86_64.sh

 ./Anaconda3-2021.11-Linux-x86_64.sh
 
source ~/.bashrc

验证是否安装成功

 7.安装pytorch

conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch

查看是否安装成功

 返回true就是安装成功了