UBUNTU22.04安装AMD/NVIDIA驱动+CUDA12.2+CUDNN

本文安装基于AMD显卡WX3100以及nvidia TESLA P40进行驱动安装

首先更新软件并安装依赖

sudo apt-get update
sudo apt-get upgrade -y
sudo apt-get install g++
sudo apt-get install gcc
sudo apt-get install make

1.安装AMD显卡驱动

首先下载UNUNTU对应版本的显卡驱动：AMD驱动

来到驱动包对应路径下安装驱动包

sudo dpkg -i amdgpu-install_5.5.50503-1_all.deb 

安装驱动：

sudo amdgpu-install --no-dkms
sudo apt install rocm-dev

配置AMD驱动环境变量

ls -l /dev/dri/render*
sudo usermod -a -G render $LOGNAME
sudo usermod -a -G video $LOGNAME

重启电脑后在终端输入rocm-smi,若出现类似以下字符则说明安装成功

======================= ROCm System Management Interface =======================
================================= Concise Info =================================
GPU  Temp (DieEdge)  AvgPwr  SCLK    MCLK    Fan     Perf  PwrCap  VRAM%  GPU%  
0    34.0c           4.211W  734Mhz  300Mhz  19.22%  auto  35.0W    11%   0%    
================================================================================
============================= End of ROCm SMI Log ==============================

2.安装计算卡显卡驱动

首先在终端输入以下字符检查NVIDIA显卡型号以及可用驱动版本

ubuntu-drivers devices

tiger@EPIC-7302-S8030GM4NE-2T:~$ ubuntu-drivers devices
== /sys/devices/pci0000:00/0000:00:03.7/0000:06:00.0 ==
modalias : pci:v000010DEd00001B38sv000010DEsd000011D9bc03sc02i00
vendor   : NVIDIA Corporation
model    : GP102GL [Tesla P40]
driver   : nvidia-driver-535 - distro non-free recommended
driver   : nvidia-driver-450-server - distro non-free
driver   : nvidia-driver-470-server - distro non-free
driver   : nvidia-driver-390 - distro non-free
driver   : nvidia-driver-418-server - distro non-free
driver   : nvidia-driver-535-server - distro non-free
driver   : nvidia-driver-545 - distro non-free
driver   : nvidia-driver-470 - distro non-free
driver   : xserver-xorg-video-nouveau - distro free builtin

选择推荐版本驱动进行安装：

sudo apt install nvidia-driver-535

也可以在查看完驱动版本后，使用UBUNTU自带的软件和更新进行驱动安装

安装完成后在终端输入nvidia-smi，出现类似以下字符则说明安装成功

tiger@EPIC-7302-S8030GM4NE-2T:~$ nvidia-smi
Wed Aug  7 23:37:11 2024       
+---------------------------------------------------------------------------------+
| NVIDIA-SMI 535.183.01             Driver Version: 535.183.01   CUDA Version: 12.2     |
|-----------------------------------+----------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |
|                                         |                      |               MIG M. |
|====================================+======================+======================|
|   0  Tesla P40                      Off | 00000000:06:00.0 Off |                  Off |
| N/A   26C    P8              10W / 250W |      4MiB / 24576MiB |      0%      Default |
|                                         |                      |                  N/A |
+-----------------------------------+----------------------+----------------------+
                                                                                         
+---------------------------------------------------------------------------------+
| Processes:                                                                            |
|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
|        ID   ID                                                             Usage      |
|==================================================================================|
|    0   N/A  N/A      1271      G   /usr/lib/xorg/Xorg                            4MiB |
+----------------------------------------------------------------------------------+

3.安装CUDA

首先在官网下载对应驱动支持的版本的CUDA安装包：CUDA官网

此处以535驱动支持的CUDA-12.2为例

运行runfile代码安装CUDA

wget https://developer.download.nvidia.com/compute/cuda/12.2.2/local_installers/cuda_12.2.2_535.104.05_linux.run
sudo sh cuda_12.2.2_535.104.05_linux.run

安装时取消勾选显卡驱动即可

│ CUDA Installer                                                               │
│ - [ ] Driver                                                                 │
│      [ ] 535.104.05                                                          │
│ + [X] CUDA Toolkit 12.2                                                      │
│   [X] CUDA Demo Suite 12.2                                                   │
│   [X] CUDA Documentation 12.2                                                │
│ - [ ] Kernel Objects                                                         │
│      [ ] nvidia-fs                                                           │
│   Options                                                                    │
│   Install                                                                    │
│                

出现以下内容则说明安装完成

tiger@EPIC-7302-S8030GM4NE-2T:~$ sudo sh cuda_12.2.2_535.104.05_linux.run
[sudo] tiger 的密码： 
===========
= Summary =
===========

Driver:   Not Selected
Toolkit:  Installed in /usr/local/cuda-12.2/

Please make sure that
 -   PATH includes /usr/local/cuda-12.2/bin
 -   LD_LIBRARY_PATH includes /usr/local/cuda-12.2/lib64, or, add /usr/local/cuda-12.2/lib64 to /etc/ld.so.conf and run ldconfig as root

To uninstall the CUDA Toolkit, run cuda-uninstaller in /usr/local/cuda-12.2/bin
***WARNING: Incomplete installation! This installation did not install the CUDA Driver. A driver of version at least 535.00 is required for CUDA 12.2 functionality to work.
To install the driver using this installer, run the following command, replacing <CudaInstaller> with the name of this run file:
    sudo <CudaInstaller>.run --silent --driver

Logfile is /var/log/cuda-installer.log

配置配置CUDA环境变量

sudo gedit ~/.bashrc

在最末尾添加地址，需将以下地址中的cuda-xx.x替换为对应的版本。例：cuda-12.2

export PATH=$PATH:/usr/local/cuda-xx.x/bin
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-xx.x/lib64
export CUDA_HOME=$CUDA_HOME:/usr/local/cuda-xx.x

更新环境变量

source ~/.bashrc

检测是否安装成功

nvcc -V

显示以下内容则说明安装成功

epyc-7302@epyc7302-S8030GM4NE-2T:~$ nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2023 NVIDIA Corporation
Built on Tue_Aug_15_22:02:13_PDT_2023
Cuda compilation tools, release 12.2, V12.2.140
Build cuda_12.2.r12.2/compiler.33191640_0

4.安装CUDNN工具包

前往官网下载CUDA对应的工具包

安装CUDNN软件包

sudo dpkg -i cudnn-local-repo-ubuntu2204-8.9.7.29_1.0-1_amd64.deb 

安装完成会显示需要执行的操作命令，执行对应的命令sudo cp /var/cudnn-local-repo-ubuntu2204-8.9.7.29/cudnn-local-08A7D361-keyring.gpg /usr/share/keyrings/

tiger@EPIC-7302-S8030GM4NE-2T:~$ sudo dpkg -i cudnn-local-repo-ubuntu2204-8.9.7.29_1.0-1_amd64.deb 
[sudo] tiger 的密码： 
正在选中未选择的软件包 cudnn-local-repo-ubuntu2204-8.9.7.29。
(正在读取数据库 ... 系统当前共安装有 224517 个文件和目录。)
准备解压 cudnn-local-repo-ubuntu2204-8.9.7.29_1.0-1_amd64.deb  ...
正在解压 cudnn-local-repo-ubuntu2204-8.9.7.29 (1.0-1) ...
正在设置 cudnn-local-repo-ubuntu2204-8.9.7.29 (1.0-1) ...

The public cudnn-local-repo-ubuntu2204-8.9.7.29 GPG key does not appear to be installed.
To install the key, run this command:
sudo cp /var/cudnn-local-repo-ubuntu2204-8.9.7.29/cudnn-local-08A7D361-keyring.gpg /usr/share/keyrings/

之后进入文件夹

cd /var/cudnn-local-repo-ubuntu2204-8.9.7.29/

，安装对应的依赖包，这些依赖包是安装时生存的对应的deb文件，只能进入该目录使用dpkg安装

sudo dpkg -i libcudnn8_8.9.7.29-1+cuda12.2_amd64.deb
sudo dpkg -i libcudnn8-dev_8.9.7.29-1+cuda12.2_amd64.deb 
sudo dpkg -i libcudnn8-samples_8.9.7.29-1+cuda12.2_amd64.deb 

验证安装是否可用

进入文件夹

cd /usr/src/cudnn_samples_v8

，将示例复制到主目录

cp -r /usr/src/cudnn_samples_v8/ $HOME

进入mnistCUDNN文件夹并在终端打开，然后编译

make clean && make

若产生报错则安装依赖

sudo apt-get install libfreeimage-dev

运行mnistCUDNN

./mnistCUDNN

显示以下内容则说明安装可用

Loading image data/five_28x28.pgm
Performing forward propagation ...
Resulting weights from Softmax:
0.0000000 0.0000008 0.0000000 0.0000002 0.0000000 1.0000000 0.0000154 0.0000000 0.0000012 0.0000006 

Result of classification: 1 3 5

Test passed!