Ubuntu22.04.4安装配置CUDA12.5，Cdnn官方详细版本

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 安装需求如下图

 

 

 

机器raid配置

两块磁盘做raid1，参见官方raid，配置手册

 

https://www.supermicro.com/support/manuals/

系统下载

https://old-releases.ubuntu.com/releases/22.04/

制作U盘

 

使用rufus制作,

 

U盘系统安装

 

重启按F11,选择U盘，操作步骤截图如下：

 

 

 

 

 

获取到ip地址，点击下一步

 

 

 

 

 

 

 

 

ubuntu远程登录

 

后续使用远程登录，远程登录界面如下

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安装docker

可在选择系统安装部分，选择勾选docker即可

 

 

安装GCC

GCC版本要求参考官方

CUDA Toolkit Documentation 12.5

System Requirements 配置如下

 

To use NVIDIA CUDA on your system, you will need the following installed:

• CUDA-capable GPU
• A supported version of Linux with a gcc compiler and toolchain
• CUDA Toolkit (available at https://developer.nvidia.com/cuda-downloads)

The CUDA development environment relies on tight integration with the host development environment, including the host compiler and C runtime libraries, and is therefore only supported on distribution versions that have been qualified for this CUDA Toolkit release.

The following table lists the supported Linux distributions. Please review the footnotes associated with the table.

 

 

# 安装gcc12

sudo apt  install gcc-12

sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-12 12

admin1@admin1:~$ gcc --version

gcc (Ubuntu 12.3.0-1ubuntu1~22.04) 12.3.0

Copyright (C) 2022 Free Software Foundation, Inc.

This is free software; see the source for copying conditions.  There is NO

warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

 

安装Cuda12.5

Cuda官方下载链接

https://developer.nvidia.com/cuda-downloads

安装文档参考官方

 

CUDA 12.6 Update 1 Release Notes

wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-ubuntu2204.pin

sudo mv cuda-ubuntu2204.pin /etc/apt/preferences.d/cuda-repository-pin-600

wget https://developer.download.nvidia.com/compute/cuda/12.5.0/local_installers/cuda-repo-ubuntu2204-12-5-local_12.5.0-555.42.02-1_amd64.deb

sudo dpkg -i cuda-repo-ubuntu2204-12-5-local_12.5.0-555.42.02-1_amd64.deb

sudo cp /var/cuda-repo-ubuntu2204-12-5-local/cuda-*-keyring.gpg /usr/share/keyrings/

sudo apt-get update

sudo apt-get -y install cuda-toolkit-12-5

 安装GPU驱动

 

 

安装命令,最新版

 

sudo apt-get install -y cuda-drivers

 

如果你想要安装指定版本，2选1即可

 

sudo apt-get install -y cuda-drivers-555

 

安装完毕执行nvidia-smi

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安装cudnn

 

官方下载链接

https://developer.nvidia.com/cudnn-archive

 

安装文档参考(新版cdnn9.x.x)

 

NVIDIA cuDNN — NVIDIA cuDNN v9.4.0 documentation

 

安装文档参考(新版cdnn8.x.x)

 

Installation Guide :: NVIDIA cuDNN Documentation

 

 

deb包安装(适用于新版cdnn9.x.x)

 

wget https://developer.download.nvidia.com/compute/cudnn/9.4.0/local_installers/cudnn-local-repo-ubuntu2204-9.4.0_1.0-1_amd64.deb

sudo dpkg -i cudnn-local-repo-ubuntu2204-9.4.0_1.0-1_amd64.deb

sudo cp /var/cudnn-local-repo-ubuntu2204-9.4.0/cudnn-*-keyring.gpg /usr/share/keyrings/

sudo apt-get updatesudo apt-get -y install cudnn

 

1. To install for CUDA 11, perform the above configuration but install the CUDA 11 specific package:

sudo apt-get -y install cudnn-cuda-11

 

1. To install for CUDA 12, perform the above configuration but install the CUDA 12 specific package:

 

sudo apt-get -y install cudnn-cuda-12

 

 

deb包安装（适用于8.x.x）

 

Download the Debian local repository installation package. Before issuing the following commands, you must replace X.Y and 8.x.x.x with your specific CUDA and cuDNN versions.

1. Navigate to your downloads directory containing the cuDNN Debian local installer file.
2. Enable the local repository.

sudo dpkg -i cudnn-local-repo-${distro}-8.x.x.x_1.0-1_amd64.deb

or

sudo dpkg -i cudnn-local-repo-${distro}-8.x.x.x_1.0-1_arm64.deb

Import the CUDA GPG key.

sudo cp /var/cudnn-local-repo-*/cudnn-local-*-keyring.gpg /usr/share/keyrings/

Refresh the repository metadata.

sudo apt-get update

Install the runtime library.

sudo apt-get install libcudnn8=8.x.x.x-1+cudaX.Y

Install the developer library.

sudo apt-get install libcudnn8-dev=8.x.x.x-1+cudaX.Y

Install the code samples.

sudo apt-get install libcudnn8-samples=8.x.x.x-1+cudaX.Y

 

 

tar包安装

 

Before issuing the following commands, you must replace X.Y and v8.x.x.x with your specific CUDA and cuDNN versions and package date.

1. Navigate to your <cudnnpath> directory containing the cuDNN tar file.
2. Unzip the cuDNN package.

 tar -xvf cudnn-linux-x86_64-8.x.x.x_cudaX.Y-archive.tar.xz

Copy the following files into the CUDA toolkit directory.

sudo cp cudnn-*-archive/include/cudnn*.h /usr/local/cuda/include

sudo cp -P cudnn-*-archive/lib/libcudnn* /usr/local/cuda/lib64

sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn*

 

 

安装fabricmanager

官方文档链接如下

1. Overview — Fabric Manager for NVIDIA NVSwitch Systems r560 documentation

Note  In the following commands, <driver-branch> should be substituted with the required NVIDIA driver branch number for qualified data center drivers (for example, 560).

• For Debian and Ubuntu based OS distributions:

sudo apt-get install -V nvidia-open-<driver-branch>

sudo apt-get install -V nvidia-fabricmanager-<driver-branch> nvidia-fabricmanager-dev-<driver-branch>

 

 

安装nvidia- container-toolkit

 

官方安装文档

Installing the NVIDIA Container Toolkit — NVIDIA Container Toolkit 1.16.2 documentation

 

Installing with AptConfigure the production repository:

curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \

&& curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \

sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \

sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list

Optionally, configure the repository to use experimental packages:

sed -i -e '/experimental/ s/^#//g' /etc/apt/sources.list.d/nvidia-container-toolkit.list

Update the packages list from the repository:

sudo apt-get update

Install the NVIDIA Container Toolkit packages:

sudo apt-get install -y nvidia-container-toolkit

 

 

 

 

 

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