ubuntu22.04安装显卡、CUDA(含多个CUDA切换)、CUDNN、pytorch

imolly 2024-10-02 12:07:03 阅读 91

一、显卡

「必须」更新软件列表和安装必要软件、依赖

<code>sudo apt-get update #更新软件列表

sudo apt-get install g++

sudo apt-get install gcc

sudo apt-get install make

禁用 nouveau 驱动

ouveau是Ubuntu自带的显卡驱动,但他是核显,我这里想安装独显,就得把他禁掉

命令(cmd)

sudo gedit /etc/modprobe.d/blacklist.conf

(输入密码)

(自动打开的)文本,在末尾

# added

blacklist nouveau

blacklist lbm-nouveau

options nouveau modeset=0

alias nouveau off

alias lbm-nouveau off

ctrl+s 保存

更新重启

sudo update-initramfs –u

sudo reboot # 重启电脑

lsmod | grep nouveau # 输出内容为空,则表示成功禁用

安装驱动

选择合适版本:官网查询,记住版本号xxx.yy卸载之前的

sudo apt-get remove nvidia-* # 卸载之前的

add-apt-repository ppa:graphics-drivers/ppa # 更新显卡驱动的源

apt-get install nvidia-driver-xxx # xxx是显卡版本,需要修改

检查

nvidia-smi

 安装成功。

CUDA version :12.4 ——最大CUDA版本

二、CUDA

CUDA-显卡 对应

pytorch 官网:CUDA11.8   12.1

安装依赖

<code>sudo apt-get install freeglut3-dev build-essential libx11-dev libxmu-dev libxi-dev libgl1-mesa-glx libglu1-mesa libglu1-mesa-dev

安装CUDA

CUDA 11.8CUDA 12.1一路选择下来,我最后选了runfile(local),因为命令行少

第一行加sudo,能防止权限不够

# 11.8

sudo wget https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_520.61.05_linux.run

sudo sh cuda_11.8.0_520.61.05_linux.run

# 12.1

sudo wget https://developer.download.nvidia.com/compute/cuda/12.1.0/local_installers/cuda_12.1.0_530.30.02_linux.run

sudo sh cuda_12.1.0_530.30.02_linux.run

安装时的选择

continue写 accept不要driver:在driver处按enterinstall(如果有多个CUDA,会问你symlink改不改): Yes

检查

nvcc -V

成功!

安装CUDA失败,重装该版本(希望用不上)

环境配置之cuda的卸载(ubuntu)_ubuntu卸载cuda-CSDN博客

切换CUDA(选读)

切换脚本

<code>sudo vim switch-cuda.sh

#!/usr/bin/env bash

# Copyright (c) 2018 Patrick Hohenecker

#

# Permission is hereby granted, free of charge, to any person obtaining a copy

# of this software and associated documentation files (the "Software"), to deal

# in the Software without restriction, including without limitation the rights

# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell

# copies of the Software, and to permit persons to whom the Software is

# furnished to do so, subject to the following conditions:

#

# The above copyright notice and this permission notice shall be included in all

# copies or substantial portions of the Software.

#

# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR

# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,

# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE

# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER

# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,

# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE

# SOFTWARE.

# author: Patrick Hohenecker <mail@paho.at>

# version: 2018.1

# date: May 15, 2018

set -e

# ensure that the script has been sourced rather than just executed

if [[ "${BASH_SOURCE[0]}" = "${0}" ]]; then

echo "Please use 'source' to execute switch-cuda.sh!"

exit 1

fi

INSTALL_FOLDER="/usr/local" # the location to look for CUDA installations atcode>

TARGET_VERSION=${1} # the target CUDA version to switch to (if provided)

# if no version to switch to has been provided, then just print all available CUDA installations

if [[ -z ${TARGET_VERSION} ]]; then

echo "The following CUDA installations have been found (in '${INSTALL_FOLDER}'):"

ls -l "${INSTALL_FOLDER}" | egrep -o "cuda-[0-9]+\\.[0-9]+$" | while read -r line; do

echo "* ${line}"

done

set +e

return

# otherwise, check whether there is an installation of the requested CUDA version

elif [[ ! -d "${INSTALL_FOLDER}/cuda-${TARGET_VERSION}" ]]; then

echo "No installation of CUDA ${TARGET_VERSION} has been found!"

set +e

return

fi

# the path of the installation to use

cuda_path="${INSTALL_FOLDER}/cuda-${TARGET_VERSION}"code>

# filter out those CUDA entries from the PATH that are not needed anymore

path_elements=(${PATH//:/ })

new_path="${cuda_path}/bin"code>

for p in "${path_elements[@]}"; do

if [[ ! ${p} =~ ^${INSTALL_FOLDER}/cuda ]]; then

new_path="${new_path}:${p}"code>

fi

done

# filter out those CUDA entries from the LD_LIBRARY_PATH that are not needed anymore

ld_path_elements=(${LD_LIBRARY_PATH//:/ })

new_ld_path="${cuda_path}/lib64:${cuda_path}/extras/CUPTI/lib64"code>

for p in "${ld_path_elements[@]}"; do

if [[ ! ${p} =~ ^${INSTALL_FOLDER}/cuda ]]; then

new_ld_path="${new_ld_path}:${p}"code>

fi

done

# update environment variables

export CUDA_HOME="${cuda_path}"code>

export CUDA_ROOT="${cuda_path}"code>

export LD_LIBRARY_PATH="${new_ld_path}"code>

export PATH="${new_path}"code>

echo "Switched to CUDA ${TARGET_VERSION}."

set +e

return

# 保存,优先选1

:w !sudo tee %

:wq!

source switch-cuda.sh # 查看拥有的CUDA版本

source switch-cuda.sh XX.X # XX.X为版本号

三、CUDNN

选择合适版本:官网,下载tar(或者三个包)

单个包跳到3,

三个包点链接:ubuntu下的cudnn安装_cudnn安装 ubuntu-CSDN博客

解压

<code>tar -xvf cudnn-linux-x86_64-8.9.6.50_cuda11-archive.tar.xz

# 改文件名称

# 或者可以删掉文件名,然后把想解压的文件拖到命令窗口里,自动获取文件路径。都可以

进入文件夹,复制

cd cudnn-linux-x86_64-8.9.6.50_cuda11-archive/

sudo cp -d -r ./lib/* /usr/local/cuda-11.8/lib64/

sudo cp -r ./include/* /usr/local/cuda-11.8/include/

测试

sudo chmod a+r /usr/local/cuda-11.8/include/cudnn.h /usr/local/cuda-11.8/lib64/libcudnn*

cat /usr/local/cuda-11.8/include/cudnn_version.h | grep CUDNN_MAJOR -A 2

出现的三个数字==版本号

四、pytorch

官网:Start Locally | PyTorch

官网命令

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

我用的conda xxx,没有conda用pip3 也行,据说pip3快

检查(依然在CMD)

python # 如果报错,试python3

import torch

from torch.backends import cudnn

print(cudnn.is_available())

print(torch.backends.cudnn.version())

print(torch.cuda.is_available()) # True,则gpu版本的pytorch安装成功

print(torch.zeros(1).cuda())

如果有python编译器,如pycharm、vscode,在那里面import print 更简单,直接复制。这里是照顾没装这些编译器的人

可能报错

#error -- unsupported GNU version! gcc versions later than 8 are not supported!-CSDN博客

Gcc多版本安装和切换_安装gcc新版本,并能切换-CSDN博客

「解决」ubuntu CUDA版本什么都对,但torch.cuda.is_available()是false-CSDN博客

参考

ubuntu下,安装配置CUDA_cuda安装教程 ubuntu-CSDN博客

 ubuntu20.04安装多版本cuda,切换版本_ubuntu 多版本nvcc-CSDN博客

【深度学习环境配置】ubuntu 20.04+4060 Ti+CUDA 11.8+pytorch(装机、显卡驱动、CUDA、cudnn、pytorch)_4060ti cuda cudnn-CSDN博客



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