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一、下载安装包

此处的安装环境为离线环境，需要先下载cuda安装文件，安装文件可以去官网地址下载对应的系统版本。官网下载地址：https://developer.nvidia.com/cuda-toolkit-archive

1143511.png

驱动和cuda版本对应：
https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html

问题 centos7

Using built-in stream user interface
-> Detected 32 CPUs online; setting concurrency level to 32.
-> The file '/tmp/.X0-lock' exists and appears to contain the process ID '2647' of a running X server.
ERROR: You appear to be running an X server; please exit X before installing.  For further details, please see the section INSTALLING THE NVIDIA DRIVER in the README available on the Linux driver download page at www.nvidia.com.
ERROR: Installation has failed.  Please see the file '/var/log/nvidia-installer.log' for details.  You may find suggestions on fixing installation problems in the README available on the Linux driver download page at www.nvidia.com.

解决：
systemctl stop gdm.service

问题 centos8

-> Detected 128 CPUs online; setting concurrency level to 32.
-> Tagging shared libraries with chcon -t textrel_shlib_t.
ERROR: An NVIDIA kernel module 'nvidia-uvm' appears to already be loaded in your kernel.  This may be because it is in use (for example, by an X server, a CUDA program, or the NVIDIA Persistence Daemon), but this may also happen if your kernel was configured without support for module unloading.  Please be sure to exit any programs that may be using the GPU(s) before attempting to upgrade your driver.  If no GPU-based programs are running, you know that your kernel supports module unloading, and you still receive this message, then an error may have occurred that has corrupted an NVIDIA kernel module's usage count, for which the simplest remedy is to reboot your computer.
ERROR: Installation has failed.  Please see the file '/var/log/nvidia-installer.log' for details.  You may find suggestions on fixing installation problems in the README available on the Linux driver download page at www.nvidia.com.
~

解决：
GPU正在使用，关闭正在使用的GPU

问题 centos 8

Using built-in stream user interface
-> Detected 128 CPUs online; setting concurrency level to 32.
-> Tagging shared libraries with chcon -t textrel_shlib_t.
ERROR: An NVIDIA kernel module 'nvidia' appears to already be loaded in your kernel.  This may be because it is in use (for example, by an X server, a CUDA program, or the NVIDIA Persistence Daemon), but this may also happen if your kernel was configured without support for module unloading.  Please be sure to exit any programs that may be using the GPU(s) before attempting to upgrade your driver.  If no GPU-based programs are running, you know that your kernel supports module unloading, and you still receive this message, then an error may have occurred that has corrupted an NVIDIA kernel module's usage count, for which the simplest remedy is to reboot your computer.
ERROR: Installation has failed.  Please see the file '/var/log/nvidia-installer.log' for details.  You may find suggestions on fixing installation problems in the README available on the Linux driver download page at www.nvidia.com.

解决：
GPU正在使用，关闭正在使用的GPU,通过命令：
sudo lsof /dev/nvidia*
kill -9 pid

问题：

sh ./cuda_11.6.0_510.39.01_linux.run
Extraction failed.
Ensure there is enough space in /tmp and that the installation package is not corrupt
Signal caught, cleaning up

没有安装解压软件
yum install tar

问题 centos

nvidia-smi 中可以显示GPU，但是torch.cuda.is_available() 出现如下错误：

UserWarning: CUDA initialization: Unexpected error from cudaGetDeviceCount(). Did you run some cuda functions before calling

Error 802: system not yet initialized

centos8中解决办法

注意 驱动版本要对应上

wget https://developer.download.nvidia.cn/compute/cuda/repos/rhel8/x86_64/nvidia-fabric-manager-515.65.01-1.x86_64.rpm

sudo yum install nvidia-fabric-manager-515.65.01-1.x86_64.rpm

systemctl enable nvidia-fabricmanager

systemctl restart nvidia-fabricmanager

systemctl status nvidia-fabricmanager

二、cuda 版本切换

一般情况下从官网下载：https://developer.nvidia.com/cuda-toolkit-archive

注意安装的时候：不要安装cuda driver

安装完成后切换软连接：

rm -rf /usr/local/cuda  #删除之前创建的软链接 
sudo ln -s /usr/local/cuda-11.3/  /usr/local/cuda/ 
nvcc --version #查看当前 cuda 版本

如果还不行直接在环境变量中修改：

vim ~/.bashrc

#然后添加

export CUDA_HOME=/usr/local/cuda
export PATH=$PATH:/usr/local/cuda/bin
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-11.3/lib64

遇见过一种情况还不行：

查看 which nvcc 发现指向没有变 可能就是环境变量没有改过来
查看环境变量：

echo $PATH
## 打印：/home/centos/anaconda3/bin:/home/centos/anaconda3/condabin:/home/centos/.local/bin:/home/centos/bin:/usr/local/cuda-12.2/bin:/usr/local/bin:/usr/bin:/usr/local/sbin:/usr/sbin:/usr/local/cuda/bin

##发现环境变量没有变，需要将固定指向的环境变量修改：

export PATH=/home/centos/anaconda3/bin:/home/centos/anaconda3/condabin:/home/centos/.local/bin:/home/centos/bin:/usr/local/bin:/usr/bin:/usr/local/sbin:/usr/sbin:/usr/local/cuda/bin

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