In this article, I will share some of my experience on installing NVIDIA driver and CUDA on Linux OS. Here I mainly use Ubuntu as example. Comments for CentOS/Fedora are also provided as much as I can.
Today I failed to connect VS Code to a CentOS 7 server via SSH and found the following error message from vscode's log output.
wget: unrecognized option '--no-config' Usage: wget [OPTION]... [URL]... Try `wget --help' for more options.
The reason is that VScode needs to download an update package via wget
and supply such an argument but wget
on CentOS 7 is outdated to support this option.
Fortunately, VS Code respects the PATH variable of the user.
So a viable solution is to create a wrapper script of wget
(important: chmod +x
to make it executable), and put it under ~/.local/bin
which is configured in PATH
in my .bashrc
(important: in front of system paths).
This guide will show you how to use Intel graphics for rendering display and NVIDIA graphics for CUDA computing on Ubuntu 18.04 / 20.04 desktop.
I made this work on an ordinary gaming PC with two graphics devices, an Intel UHD Graphics 630 plus an NVIDIA GeForce GTX 1080 Ti.
Both of them can be shown via lspci | grep VGA
.
00:02.0 VGA compatible controller: Intel Corporation Device 3e92
01:00.0 VGA compatible controller: NVIDIA Corporation GP102 [GeForce GTX 1080 Ti] (rev a1)
Ubuntu 15.10 have been released for a couple of days. It is a bleeding-edge system coming with Linux kernel 4.2 and GCC 5. However, compiling and running Caffe on this new system is no longer as smooth as on earlier versions. I have done some research related to this issue and finally find a way out. I summarize it here in this short tutorial and I hope more people and enjoy this new system without breaking their works.
The latest NVIDIA driver is officially included in Ubuntu 15.10 repositories. One can install it directly via apt-get
.
sudo apt-get install nvidia-352-updates nvidia-modprobe
The nvidia-modprobe
utility is used to load NVIDIA kernel modules and create NVIDIA character device files automatically everytime your machine boots up.
Reboot your machine and verify everything works by issuing nvidia-smi
or running deviceQuery
in CUDA samples.