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
#!/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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