Installation
This Howto provides a way to install the official CUDA packages from NVIDIA along our packaged NVIDIA driver at RPM Fusion.
While this repository contains both the NVIDIA driver and CUDA toolkit, We recommend to use our packaged driver instead, in order to receive fixes needed by Fedora kernel update.
NVIDIA official repositories
These repositories contain versions of CUDA that are parallel installable along with another version.
CUDA Toolkit
Please use the Official link: https://developer.nvidia.com/cuda-downloads
Fedora 39 and later (if using a compatible compiler, see also
sudo dnf config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/fedora39/x86_64/cuda-fedora39.repo sudo dnf clean all sudo dnf module disable nvidia-driver sudo dnf -y install cuda
RHEL/Rocky/Alma 9
sudo dnf config-manager --add-repo http://developer.download.nvidia.com/compute/cuda/repos/rhel9/x86_64/cuda-rhel9.repo sudo dnf clean all sudo dnf module disable nvidia-driver sudo dnf -y install cuda
RHEL/Rocky/Alma 8
sudo dnf config-manager --add-repo http://developer.download.nvidia.com/compute/cuda/repos/rhel8/x86_64/cuda-rhel8.repo sudo dnf clean all sudo dnf module disable nvidia-driver sudo dnf -y install cuda
RHEL/CentOS 7
sudo yum-config-manager --add-repo http://developer.download.nvidia.com/compute/cuda/repos/rhel7/x86_64/cuda-rhel7.repo sudo yum clean all sudo yum install cuda
Machine Learning repository
Please use the official link: https://developer.nvidia.com/nccl/nccl-download
- RHEL 9/Fedora
Merged with the regular CUDA repository
RHEL/CentOS 8
sudo dnf install https://developer.download.nvidia.com/compute/machine-learning/repos/rhel8/x86_64/nvidia-machine-learning-repo-rhel8-1.0.0-1.x86_64.rpm sudo dnf install libcudnn7 libcudnn7-devel libnccl libnccl-devel
RHEL/CentOS 7
sudo yum install https://developer.download.nvidia.com/compute/machine-learning/repos/rhel7/x86_64/nvidia-machine-learning-repo-rhel7-1.0.0-1.x86_64.rpm sudo yum install libcudnn7 libcudnn7-devel libnccl libnccl-devel
TensorRT repository
You can download the TensorRT component using the appropriate version from https://developer.nvidia.com/nvidia-tensorrt-download
This requires to login with the NVIDIA CUDA program subscription.
Legacy NVIDIA 340xx/CUDA 6.5
This repository contains a legacy version of CUDA 6.5 that will works with the NVIDIA 340xx series
Please use the Official link: https://developer.nvidia.com/cuda-toolkit-65
RHEL/CentOS 6
sudo yum install http://developer.download.nvidia.com/compute/cuda/repos/rhel6/x86_64/cuda-repo-rhel6-6.5-14.x86_64.rpm sudo yum install cuda
Fedora 20 (and later)
sudo yum install install http://developer.download.nvidia.com/compute/cuda/repos/fedora20/x86_64/cuda-repo-fedora20-6.5-14.x86_64.rpm sudo yum install cuda
Please verify to have a compatible compiler.
Community repositories
RPM Fusion CUDA
This repository aims to receive content dedicated for CUDA and is built with the official cuda releases. Only available for Fedora (latest supported CUDA release) so far and is still a work in progress...
AI/ML Fedora nvidia-container-toolkit
With the AI-ML working group at fedora, there is this content allowing a fully built from source nvidia-container-toolkit that integrates well with fedora: See also https://copr.fedorainfracloud.org/coprs/g/ai-ml/nvidia-container-toolkit/
Known issues
Newer/Beta driver
Sometime with recent CUDA releases, a newer/beta driver version is required. We usually package the such driver in RPM Fusion for rawhide. To ease the installation in stable Fedora branches, you can follow this guideline: See also https://rpmfusion.org/Howto/NVIDIA#Latest.2FBeta_driver
It can be a good idea to keep using the rawhide drivers by default.
GCC version
When using a later version of Fedora than what is supported by the NVIDIA CUDA Official repository, you might be unable to compile. You can either:
Install an older gcc for dedicated for CUDA from COPR (Recommended on Fedora).
- GCC8 Works up to Fedora 32 for cuda-10.1 and later (up to CUDA 11)
dnf copr enable kwizart/cuda-gcc-10.1 -y dnf install cuda-gcc cuda-gcc-c++ -y
You will need to tell CUDA to use it instead of using the default g++ this can be done for the cuda-samples with:
export HOST_COMPILER=cuda-g++
Install the appropriate gcc version from developer toolset. It will install in parallel. Please see https://www.softwarecollections.org/en/scls/rhscl/devtoolset-8/
sudo dnf install https://rpmfind.net/linux/centos/7/extras/x86_64/Packages/centos-release-scl-rh-2-3.el7.centos.noarch.rpm sudo dnf install devtoolset-8-toolchain
You cannot install the whole devtoolset-8 collection, but the toolchain is enough , then each time you need to build using cuda, you start by
scl run devtoolset-8 bash gcc --version gcc (GCC) 8.3.1 20190311 (Red Hat 8.3.1-3) Copyright (C) 2018 Free Software Foundation, Inc. This is free software; see the source for copying conditions. There is NO warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. exit gcc --version gcc (GCC) 9.2.1 20190827 (Red Hat 9.2.1-1) Copyright (C) 2019 Free Software Foundation, Inc. This is free software; see the source for copying conditions. There is NO warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
- Tweak the /usr/local/cuda*/targets/x86_64-linux/include/crt/host_defines.h to accept the Fedora default compiler. (Not recommended - not always working).
Which driver Package
Both "CUDA" and "RPM Fusion" repositories provide the nvidia driver packages. Unfortunately, the packaging method is way too different and can conflicts. We recommends to use the publicly and community based packaging method (RPM Fusion) and avoid the NVIDIA packaged nvidia-driver. From time to time, NVIDIA uses non-publictly released driver, so you will have to wait for a public driver for the RPM Fusion counterpart...
With current RHEL8 repositories, the nvidia-driver is packaged as a module. So it's easy to disable with:
sudo dnf module disable nvidia-driver
NVIDIA driver higher in CUDA repo
Often when NVIDIA release a newer CUDA version or even in the case of pre-release software the NVIDIA driver is at a higher version than the driver provided by RPM Fusion. There is no way for us to provide a version that will match the newer CUDA requirement "ahead" of any NVIDIA public driver release. With that said, the dependencies can sometime be faked at the RPM level with:
dnf module enable nvidia-driver -y && dnf download cuda-drivers && dnf module disable nvidia-driver -y rpm -Uvh cuda-drivers*.rpm --nodeps dnf update
Please remind to remove the cuda-drivers package when the RPM Fusion provided driver is high enough. Complain to NVIDIA for this bad behaviour, not to us.
Once a newer version of the driver is available publicly, it will likely be available on the RPM Fusion rawhide repository in the first step, please follow this guide on how to upgrade to the newer driver (This is currently the case with CUDA 11 and 450xx driver serie) : https://rpmfusion.org/Howto/NVIDIA#Latest.2FBeta_driver
NVIDIA provided libOpenCL
NVIDIA only advertise OpenCL 1.2 with the binary driver at this time. As a consequence, they provide an old version of libOpenCL.so.1 which works fine with their binary driver. As most software in Fedora and RPM Fusion are built using a newer libOpenCL, the system linker detects that and issues the following message:
/usr/local/cuda-9.2/targets/x86_64-linux/lib/libOpenCL.so.1: no version information available (required by ffmpeg)
You can either ignore the message or manually delete the libOpenCL.so.1 provided by NVIDIA (run sudo ldconfig once deleted). Please verify to not have other OpenCL providers that might interfere with NVIDIA OpenCL usage. (looking at /etc/OpenCL/vendors ).
Running blender
Even when only running blender, you need a CUDA compatible compiler as described above. This is because blender will compile the "CUDA Kernels" optimized for your own GPU. You can run blender with:
scl run devtoolset-7 blender
Once the "CUDA kernels" are compiled, you can run blender normally
References
CUDA whatsnew : https://developer.nvidia.com/cuda-toolkit/whatsnew
CUDA documentation: https://docs.nvidia.com/cuda/index.html