Install cuda for linux

Install cuda for linux

The installation instructions for the CUDA Toolkit on Linux.

1. Introduction

CUDA В® is a parallel computing platform and programming model invented by NVIDIA В® . It enables dramatic increases in computing performance by harnessing the power of the graphics processing unit (GPU).

This guide will show you how to install and check the correct operation of the CUDA development tools.

1.1. System Requirements

The CUDA development environment relies on tight integration with the host development environment, including the host compiler and C runtime libraries, and is therefore only supported on distribution versions that have been qualified for this CUDA Toolkit release.

The following table lists the supported Linux distributions. Please review the footnotes associated with the table.

(2) Note that starting with CUDA 11.0, the minimum recommended GCC compiler is at least GCC 5 due to C++11 requirements in CUDA libraries e.g. cuFFT and CUB. On distributions such as RHEL 7 or CentOS 7 that may use an older GCC toolchain by default, it is recommended to use a newer GCC toolchain with CUDA 11.0. Newer GCC toolchains are available with the Red Hat Developer Toolset.

(3) Minor versions of the following compilers listed: of GCC, ICC, PGI and XLC, as host compilers for nvcc are supported.

(4) Only Tesla V100 and T4 GPUs are supported for CUDA 11.0 on Arm64 ( aarch64 ) POWER9 ( ppc64le ).

1.2. About This Document

This document is intended for readers familiar with the Linux environment and the compilation of C programs from the command line. You do not need previous experience with CUDA or experience with parallel computation. Note: This guide covers installation only on systems with X Windows installed.

2. Pre-installation Actions

2.1. Verify You Have a CUDA-Capable GPU

To verify that your GPU is CUDA-capable, go to your distribution’s equivalent of System Properties, or, from the command line, enter:

If you do not see any settings, update the PCI hardware database that Linux maintains by entering update-pciids (generally found in /sbin ) at the command line and rerun the previous lspci command.

If your graphics card is from NVIDIA and it is listed in https://developer.nvidia.com/cuda-gpus, your GPU is CUDA-capable.

The Release Notes for the CUDA Toolkit also contain a list of supported products.

2.2. Verify You Have a Supported Version of Linux

The CUDA Development Tools are only supported on some specific distributions of Linux. These are listed in the CUDA Toolkit release notes.

To determine which distribution and release number you’re running, type the following at the command line:

You should see output similar to the following, modified for your particular system:

The x86_64 line indicates you are running on a 64-bit system. The remainder gives information about your distribution.

2.3. Verify the System Has gcc Installed

The gcc compiler is required for development using the CUDA Toolkit. It is not required for running CUDA applications. It is generally installed as part of the Linux installation, and in most cases the version of gcc installed with a supported version of Linux will work correctly.

To verify the version of gcc installed on your system, type the following on the command line:

If an error message displays, you need to install the from your Linux distribution or obtain a version of gcc and its accompanying toolchain from the Web.

2.4. Verify the System has the Correct Kernel Headers and Development Packages Installed

The CUDA Driver requires that the kernel headers and development packages for the running version of the kernel be installed at the time of the driver installation, as well whenever the driver is rebuilt. For example, if your system is running kernel version 3.17.4-301, the 3.17.4-301 kernel headers and development packages must also be installed.

While the Runfile installation performs no package validation, the RPM and Deb installations of the driver will make an attempt to install the kernel header and development packages if no version of these packages is currently installed. However, it will install the latest version of these packages, which may or may not match the version of the kernel your system is using. Therefore, it is best to manually ensure the correct version of the kernel headers and development packages are installed prior to installing the CUDA Drivers, as well as whenever you change the kernel version.

RHEL7/CentOS7

Fedora/RHEL8/CentOS8

OpenSUSE/SLES

The kernel development packages for the currently running kernel can be installed with:

$ sudo zypper install -v kernel- -devel=

Ubuntu

2.5. Choose an Installation Method

The CUDA Toolkit can be installed using either of two different installation mechanisms: distribution-specific packages (RPM and Deb packages), or a distribution-independent package (runfile packages). The distribution-independent package has the advantage of working across a wider set of Linux distributions, but does not update the distribution’s native package management system. The distribution-specific packages interface with the distribution’s native package management system. It is recommended to use the distribution-specific packages, where possible.

2.6. Download the NVIDIA CUDA Toolkit

Choose the platform you are using and download the NVIDIA CUDA Toolkit

The CUDA Toolkit contains the CUDA driver and tools needed to create, build and run a CUDA application as well as libraries, header files, CUDA samples source code, and other resources.

Download Verification

The download can be verified by comparing the MD5 checksum posted at https://developer.nvidia.com/cuda-downloads/checksums with that of the downloaded file. If either of the checksums differ, the downloaded file is corrupt and needs to be downloaded again.

2.7. Handle Conflicting Installation Methods

Before installing CUDA, any previously installations that could conflict should be uninstalled. This will not affect systems which have not had CUDA installed previously, or systems where the installation method has been preserved (RPM/Deb vs. Runfile). See the following charts for specifics.

3. Package Manager Installation

Basic instructions can be found in the Quick Start Guide. Read on for more detailed instructions.

3.1. Overview

The Package Manager installation interfaces with your system’s package management system. When using RPM or Deb, the downloaded package is a repository package. Such a package only informs the package manager where to find the actual installation packages, but will not install them.

If those packages are available in an online repository, they will be automatically downloaded in a later step. Otherwise, the repository package also installs a local repository containing the installation packages on the system. Whether the repository is available online or installed locally, the installation procedure is identical and made of several steps.

Finally, some helpful package manager capabilities are detailed.

These instructions are for native development only. For cross-platform development, see the CUDA Cross-Platform Environment section.

3.2. RHEL7/CentOS7

Satisfy DKMS dependency: The NVIDIA driver RPM packages depend on other external packages, such as DKMS and libvdpau . Those packages are only available on third-party repositories, such as EPEL. Any such third-party repositories must be added to the package manager repository database before installing the NVIDIA driver RPM packages, or missing dependencies will prevent the installation from proceeding.

On RHEL 7 Linux only, execute the following steps to enable optional repositories.

The driver relies on an automatically generated xorg.conf file at /etc/X11/xorg.conf . If a custom-built xorg.conf file is present, this functionality will be disabled and the driver may not work. You can try removing the existing xorg.conf file, or adding the contents of /etc/X11/xorg.conf.d/00-nvidia.conf to the xorg.conf file. The xorg.conf file will most likely need manual tweaking for systems with a non-trivial GPU configuration.

Install repository meta-data

The libcuda.so library is installed in the /usr/lib<,64>/nvidia directory. For pre-existing projects which use libcuda.so, it may be useful to add a symbolic link from libcuda.so in the /usr/lib <,64>directory.

3.3. RHEL8/CentOS8

Satisfy DKMS dependency: The NVIDIA driver RPM packages depend on other external packages, such as DKMS and libvdpau . Those packages are only available on third-party repositories, such as EPEL. Any such third-party repositories must be added to the package manager repository database before installing the NVIDIA driver RPM packages, or missing dependencies will prevent the installation from proceeding.

On RHEL 8 Linux only, execute the following steps to enable optional repositories.

The driver relies on an automatically generated xorg.conf file at /etc/X11/xorg.conf . If a custom-built xorg.conf file is present, this functionality will be disabled and the driver may not work. You can try removing the existing xorg.conf file, or adding the contents of /etc/X11/xorg.conf.d/00-nvidia.conf to the xorg.conf file. The xorg.conf file will most likely need manual tweaking for systems with a non-trivial GPU configuration.

Install repository meta-data

The libcuda.so library is installed in the /usr/lib<,64>/nvidia directory. For pre-existing projects which use libcuda.so, it may be useful to add a symbolic link from libcuda.so in the /usr/lib <,64>directory.

3.4. Fedora

The driver relies on an automatically generated xorg.conf file at /etc/X11/xorg.conf. If a custom-built xorg.conf file is present, this functionality will be disabled and the driver may not work. You can try removing the existing xorg.conf file, or adding the contents of /etc/X11/xorg.conf.d/00-nvidia.conf to the xorg.conf file. The xorg.conf file will most likely need manual tweaking for systems with a non-trivial GPU configuration.

Satisfy Akmods dependency

The NVIDIA driver RPM packages depend on the Akmods framework which is provided by the RPMFusion free repository. The RPMFusion free repository must be added to the package manager repository database before installing the NVIDIA driver RPM packages, or missing dependencies will prevent the installation from proceeding.

Install repository meta-data

The libcuda.so library is installed in the /usr/lib<,64>/nvidia directory. For pre-existing projects which use libcuda.so, it may be useful to add a symbolic link from libcuda.so in the /usr/lib <,64>directory.

3.5. SLES

1. Perform the pre-installation actions.
2. On SLES12 SP4, install the Mesa-libgl-devel Linux packages before proceeding. See Mesa-libGL-devel.
3. Install repository meta-data

The CUDA Samples package on SLES does not include dependencies on GL and X11 libraries as these are provided in the SLES SDK. These packages must be installed separately, depending on which samples you want to use.

3.6. OpenSUSE

3.7. Ubuntu

When installing using the local repo:

When installing using network repo on Ubuntu 20.04/18.04:

When installing using network repo on Ubuntu 16.04:

Pin file to prioritize CUDA repository:

3.8. Additional Package Manager Capabilities

Below are some additional capabilities of the package manager that users can take advantage of.

3.8.1. Available Packages

The recommended installation package is the cuda package. This package will install the full set of other CUDA packages required for native development and should cover most scenarios.

The cuda package installs all the available packages for native developments. That includes the compiler, the debugger, the profiler, the math libraries, and so on. For x86_64 patforms, this also include Nsight Eclipse Edition and the visual profilers. It also includes the NVIDIA driver package.

On supported platforms, the cuda-cross-aarch64 and cuda-cross-ppc64el packages install all the packages required for cross-platform development to ARMv8 and POWER8, respectively. The libraries and header files of the target architecture’s display driver package are also installed to enable the cross compilation of driver applications. The cuda-cross- packages do not install the native display driver.

The packages installed by the packages above can also be installed individually by specifying their names explicitly. The list of available packages be can obtained with:

3.8.2. Package Upgrades

The cuda package points to the latest stable release of the CUDA Toolkit. When a new version is available, use the following commands to upgrade the toolkit and driver:

The cuda-drivers package points to the latest driver release available in the CUDA repository. When a new version is available, use the following commands to upgrade the driver:

Some desktop environments, such as GNOME or KDE, will display an notification alert when new packages are available.

To avoid any automatic upgrade, and lock down the toolkit installation to the X.Y release, install the cuda-X-Y or cuda-cross—X-Y package.

Side-by-side installations are supported. For instance, to install both the X.Y CUDA Toolkit and the X.Y+1 CUDA Toolkit, install the cuda-X.Y and cuda-X.Y+1 packages.

3.8.3. Meta Packages

Meta packages are RPM/Deb packages which contain no (or few) files but have multiple dependencies. They are used to install many CUDA packages when you may not know the details of the packages you want. Below is the list of meta packages.

4. Runfile Installation

Basic instructions can be found in the Quick Start Guide. Read on for more detailed instructions.

This section describes the installation and configuration of CUDA when using the standalone installer. The standalone installer is a «.run» file and is completely self-contained.

4.1. Overview

The Runfile installation installs the NVIDIA Driver, CUDA Toolkit, and CUDA Samples via an interactive ncurses-based interface.

The installation steps are listed below. Distribution-specific instructions on disabling the Nouveau drivers as well as steps for verifying device node creation are also provided.

Finally, advanced options for the installer and uninstallation steps are detailed below.

The Runfile installation does not include support for cross-platform development. For cross-platform development, see the CUDA Cross-Platform Environment section.

4.2. Installation

Reboot into text mode (runlevel 3).

This can usually be accomplished by adding the number «3» to the end of the system’s kernel boot parameters.

Since the NVIDIA drivers are not yet installed, the text terminals may not display correctly. Temporarily adding «nomodeset» to the system’s kernel boot parameters may fix this issue.

Consult your system’s bootloader documentation for information on how to make the above boot parameter changes.

The reboot is required to completely unload the Nouveau drivers and prevent the graphical interface from loading. The CUDA driver cannot be installed while the Nouveau drivers are loaded or while the graphical interface is active.

Verify that the Nouveau drivers are not loaded. If the Nouveau drivers are still loaded, consult your distribution’s documentation to see if further steps are needed to disable Nouveau.

The /usr/local/cuda symbolic link points to the location where the CUDA Toolkit was installed. This link allows projects to use the latest CUDA Toolkit without any configuration file update.

If installing the driver, the installer will also ask if the openGL libraries should be installed. If the GPU used for display is not an NVIDIA GPU, the NVIDIA openGL libraries should not be installed. Otherwise, the openGL libraries used by the graphics driver of the non-NVIDIA GPU will be overwritten and the GUI will not work. If performing a silent installation, the —no-opengl-libs option should be used to prevent the openGL libraries from being installed. See the Advanced Options section for more details.

If the GPU used for display is an NVIDIA GPU, the X server configuration file, /etc/X11/xorg.conf , may need to be modified. In some cases, nvidia-xconfig can be used to automatically generate a xorg.conf file that works for the system. For non-standard systems, such as those with more than one GPU, it is recommended to manually edit the xorg.conf file. Consult the xorg.conf documentation for more information.

Reboot the system to reload the graphical interface.

Verify the device nodes are created properly.

4.3. Disabling Nouveau

To install the Display Driver, the Nouveau drivers must first be disabled. Each distribution of Linux has a different method for disabling Nouveau.

4.3.1. Fedora

1. Create a file at /usr/lib/modprobe.d/blacklist-nouveau.conf with the following contents:
2. Regenerate the kernel initramfs:
3. Run the below command:
4. Reboot the system.

4.3.2. RHEL/CentOS

1. Create a file at /etc/modprobe.d/blacklist-nouveau.conf with the following contents:
2. Regenerate the kernel initramfs:

4.3.3. OpenSUSE

1. Create a file at /etc/modprobe.d/blacklist-nouveau.conf with the following contents:
2. Regenerate the kernel initrd:

4.3.4. SLES

No actions to disable Nouveau are required as Nouveau is not installed on SLES.

4.3.5. Ubuntu

1. Create a file at /etc/modprobe.d/blacklist-nouveau.conf with the following contents:
2. Regenerate the kernel initramfs:

4.4. Device Node Verification

Check that the device files /dev/nvidia* exist and have the correct (0666) file permissions. These files are used by the CUDA Driver to communicate with the kernel-mode portion of the NVIDIA Driver. Applications that use the NVIDIA driver, such as a CUDA application or the X server (if any), will normally automatically create these files if they are missing using the nvidia-modprobe tool that is bundled with the NVIDIA Driver. However, some systems disallow setuid binaries, so if these files do not exist, you can create them manually by using a startup script such as the one below:

4.5. Advanced Options

directory. If not provided, the default path of /usr/local/cuda- 11.0 is used. —samples Install the CUDA Samples. —samplespath=

directory. If not provided, the default path of $(HOME)/NVIDIA_CUDA- 11.0 _Samples is used. —defaultroot=

is not provided, then the default path of your distribution is used. This only applies to the libraries installed outside of the CUDA Toolkit path. Extraction —extract=

the following: the driver runfile, the raw files of the toolkit and samples to

This is especially useful when one wants to install the driver using one or more of the command-line options provided by the driver installer which are not exposed in this installer.

as the kernel source directory when building the NVIDIA kernel module. Required for systems where the kernel source is installed to a non-standard location. Running nvidia-xconfig —run-nvidia-xconfig Tells the driver installation to run nvidia-xconfig to update the system X configuration file so that the NVIDIA X driver is used. The pre-existing X configuration file will be backed up. No nvidia-drm kernel module —no-drm Do not install the nvidia-drm kernel module. This option should only be used to work around failures to build or install the nvidia-drm kernel module on systems that do not need the provided features. Custom Temporary Directory Selection —tmpdir=

instead of /tmp. Useful in cases where /tmp cannot be used (doesn’t exist, is full, is mounted with ‘noexec’, etc.). Show Installer Options —help Prints the list of command-line options to stdout.