1、 设备环境
1.1 软件环境:
(1) 执行命令:cat /etc/redhat-release 查看CentOS版本
CentOS Linux release 7.3.1611 (Core)
(2)执行命令:cat /proc/version 查看内核版本
内核:4.8.5 (后面会用到)
2、 配置环境
2.1 方法及步骤:
(1)安装对应版本内核源码包、gcc编译器
执行命令:
#sudo yum install -y gcc (或者:sudo yum install -y gcc-c++ 或者 yum install http://mirror.centos.org/centos/7/updates/x86_64/Packages/gcc-4.8.5-36.el7_6.2.x86_64.rpm [版本要按照内核的版本] )
# sudo yum install -y kernel
# sudo yum install -y kernel-devel
# sudo yum install -y kernel-header
查看相应版本,确认已经完成下载并安装gcc,kernel, kernel-devel和kernel-header包
查看相应版本执行命令:
#rpm -qa|grep gcc
#rpm -qa|grep kernel
从上面看到kernel 有两个版本,卸载一个,并确保kernel与kernel-devel和kernel-header包的版本号一致
卸载执行命令:
#rpm -e --nodeps kernel-3.10.0-514.el7.x86_64 ( 不检查依赖关系直接删除)
查看确认卸载成功内核版本,执行命令:
#rpm -qa|grep kernel
(2)查看nouveau是否被禁用
执行命令:lsmod | grep nouveau
有输出说明没有被禁用。
(3)禁用系统nouveau
执行命令:
#su
#echo -e "blacklist nouveau\noptions nouveau modeset=0" > /etc/modprobe.d/blacklist.conf
(4)重启系统
(5)验证nouveau是否被禁用
执行命令: lsmod | grep nouveau
没有输出说明禁用成功,如果有输出说明禁用失败。
(6)下载NVIDIA显卡驱动
到英伟达官网(https://www.nvidia.cn/Download/index.aspx?lang=cn)选择显卡和系统版本进行驱动下载
点击搜索
点击DOWNLOAD
右键【AGREE&DOWNLOAD】,菜单里复制链接地址。此处为:http://us.download.nvidia.com/tesla/418.67/NVIDIA-Linux-x86_64-418.67.run
执行命令:wget http://us.download.nvidia.com/tesla/418.67/NVIDIA-Linux-x86_64-418.67.run 对驱动进行下载y
(7)安装NVIDIA显卡驱动
Ctrl+alt+f2然后切换到运行级别3
执行命令:
# init 3
# chmod +x NVIDIA-Linux-x86_64-384.59.run
# sudo ./NVIDIA-Linux-x86_64-418.67.run -no-x-check -no-nouveau-check -no-opengl-files
点击 ok
点击 yes
点击ok ,显卡驱动安装成功。
(8)验证安装NVIDIA显卡驱动是否成功
执行命令:nvidia-smi ,如果输出下图,说明安装成功。
3、 问题处理
错误1:
ERROR: The Nouveau kernel driver is currently in use by your system. This driver is incompatible with the NVIDIA driver, and must be disabled before proceeding.
Please consult the NVIDIA driver README and your Linux distribution's documentation for details on how to correctly disable the Nouveau kernel driver.
解释:如果没有执行屏蔽nouveau操作,报以上错误。
错误2:
unable to find the development too 'cc' in you path; please make sure that you have the package 'gcc
解决办法:
yum install gcc
错误3:
ERROR: Unable to find the kernel source tree for the currently running kernel. Please make sure you have installed the kernel source files for your kernel
and that they are properly configured; on Red Hat Linux systems, for example, be sure you have the 'kernel-source' or 'kernel-devel' RPM installed.
If you know the correct kernel source files are installed, you may specify the kernel source path with the '--kernel-source-path' command line option.
解决办法:
yum install kernel-delve
错误4:
ERROR: Unable to find the kernel source tree for the currently running kernel. Please make sure you have installed the kernel source files for your kernel
and that they are properly configured; on Red Hat Linux systems, for example, be sure you have the 'kernel-source' or 'kernel-devel' RPM installed.
If you know the correct kernel source files are installed, you may specify the kernel source path with the '--kernel-source-path' command line option.
解决方法:
./NVIDIA-Linux-x86_64-390.67.run --kernel-source-path=/usr/src/kernels/3.10.0-862.3.2.el7.x86_64/
错误5:
ERROR: Unable to load the kernel module 'nvidia.ko'. This happens most frequently when this kernel module was built against the wrong
or improperly configured kernel sources, with a version of gcc that differs from the one used to build the target kernel,
or if another driver, such as nouveau, is present and prevents the NVIDIA kernel module from obtaining ownership of the NVIDIA GPU(s),
or no NVIDIA GPU installed in this system is supported by this NVIDIA Linux graphics driver release.
Please see the log entries 'Kernel module load error' and 'Kernel messages' at the end of the file '/var/log/nvidia-installer.log' for more information.
解决办法:
- 可以通过以下方式查看内核版本和源码包版本:
ls /boot | grep vmlinuz
- 如果上面的命令输出中有多个内核,则按grub.conf中指定的文件为准。
rpm -aq | grep kernel-devel kernel-devel-2.6.35.13-92.fc14.i686
- 从上面的输出中可以看出内核版本号和内核源码版本。为了解决这个错误,需要从FC官方网站上下载与内核版本对应的源码包进行安装。
可以在以下网站下载并安装:
http://rpmfind.net/linux/rpm2html/search.php?query=kernel-devel
备注:执行更新内核操作好需要重新执行屏蔽nouveau,及重建initramfs image步骤。
警告:
WARNING: nvidia-installer was forced to guess the X library path '/usr/lib64' and X module path '/usr/lib64/xorg/modules';
these paths were not queryable from the system. If X fails to find the NVIDIA X driver module, please install the `pkg-config` utility
and the X.Org SDK/development package for your distribution and reinstall the driver.
字符模式安装警告信息,可忽略。
参考:https://www.cnblogs.com/2012blog/p/9431432.html
相关推荐
GPU显卡驱动 适用于: GeForce RTX2080Ti linux-x86_64 版本: 470.103.01
kuang,需要的拿去吧
NVIDIA显卡驱动440.82Linux版,可搭配cuda9.0、cuda10.0和cuda10.1使用。
NVIDIA-GRID-vSphere-7.0 最新版本14.0显卡驱动
jetson agx xavier (jetpack4.6.1) paddlepaddle_gpu-2.4.1-cp36-cp36m-linux_aarch64.whl
lsmod|grep nvidia , 没有nvidia模块,可以开始安装。 5)sudo bash NVIDIA-Linux-x86_64-418.56.run ,一路点yes。 6)查看是否安装成功。nvidia-smi 如果是其它的内核版本,可以在...
onnxruntime_gpu-1.4----1.11.0-cp36-cp36m-linux_aarch64.whl
VMware ESXi安装NVIDIA GPU显卡硬件驱动和配置vGPU.doc
Ubuntu安装nvidia驱动步骤 1、ctrl+alt+F1进入命令行界面,输入用户名:xxxx,用户密码:xxxx。 2、关闭所有使用 GPU 的进程:sudo systemctl isolate multi-user.target 3、卸载原始驱动:sudo modprobe -r ...
tf_nightly_gpu-1.5.0.dev20171116-cp36-cp36m-win_amd64.whl 最新版tensorflow,windows系统 python3.6
ESXi_7.0 vGPU 主机显卡驱动 460版本适合点亮vGPU显卡,对于部分软件需要显卡驱动在470以上版本的可以试试510 。 NVIDIA_bootbank_NVIDIA-VMware_ESXi_7.0_Host_Driver_460.32.04-1OEM.700.0.0.15525992.vib ...
官方离线安装包,测试可用。请使用rpm -ivh [rpm完整包名] 进行安装
这些 NVIDIA 提供的可再发行组件是 PyTorch 的 Python pip 轮安装程序,具有 GPU 加速和对 cuDNN 的支持。这些软件包旨在安装在指定版本的 JetPack 之上。 在安装 PyTorch for Jetson 之前,请确保:在您的 Jetson ...
自己在jetpack5.0.1版本的nx上build的onnx版本,不是正式版本,可能会在一些环境下出问题。理论上说jetson系列应该都可以安装运行,前提是cuda11.4(使用tensorrt的话需要tensorrt8.4)
2021.03.06-GPU研究框架-方正证券-111页.pdf
japierdole co za chujostwo zajebane kurwa mac szał chuja
NVIDIA-Linux-x86_64显卡驱动,适用于RTX2060显卡的设备。只需要将改软件copy到Ubuntu16.04LTS系统下./NVIDIA-Linux-x86_64-418.88.run然后一步步操作即可;安装完毕后可通过nvidia-smi 查看是否安装成功;
python人像抠图利器,paddlepaddle 1.8.5 GPU版 whl格式:众人周知的外网环境,需要垫脚否则下载巨慢,这里帮各位搬一下砖,pip install XXX安装即可,望笑纳;)
NVIDIA 15.0 vGPU显卡驱动,适用于版本VMware esxi 8.0,支持RTX6000/RTX8000/M60/A10/A16/A40等显卡。版本号:NVIDIA-GRID-vSphere-8.0-525.60.12-525.60.13-527.41
tensorflow GPU DLL libtensorflow-gpu-windows-x86_64-2.3.0.zip