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.

(*) Only the Tesla P100 GPU is supported for CUDA 9.0 on POWER8.

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.

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

$ lspci | grep -i nvidia

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 http://developer.nvidia.com/cuda-gpus, your GPU is CUDA-capable.

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

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:

$ uname -m && cat /etc/*release

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

x86_64
Red Hat Enterprise Linux Workstation release 6.0 (Santiago)

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

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:

$ gcc --version

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

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.

$ uname -r

$ sudo yum install kernel-devel-$(uname -r) kernel-headers-$(uname -r)

$ sudo dnf install kernel-devel-$(uname -r) kernel-headers-$(uname -r)

$ uname -r
3.16.6-2-default

$ sudo zypper install kernel-<variant>-devel=<version>

$ sudo apt-get install linux-headers-$(uname -r)

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.

The NVIDIA CUDA Toolkit is available at http://developer.nvidia.com/cuda-downloads.

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.

$ md5sum <file>

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.

$ sudo /usr/local/cuda-X.Y/bin/uninstall_cuda_X.Y.pl

$ sudo /usr/bin/nvidia-uninstall

$ sudo yum remove <package_name>                      # Redhat/CentOS
$ sudo dnf remove <package_name>                      # Fedora
$ sudo zypper remove <package_name>                   # OpenSUSE/SLES
$ sudo apt-get --purge remove <package_name>          # Ubuntu

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.

1. Perform the pre-installation actions.
2. 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.

3. Address custom xorg.conf, if applicable

   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.

4. Install repository meta-data

   $ sudo rpm --install cuda-repo-<distro>-<version>.<architecture>.rpm

5. Clean Yum repository cache

   $ sudo yum clean expire-cache

6. Install CUDA

   $ sudo yum install cuda

   If the i686 libvdpau package dependency fails to install, try using the following steps to fix the issue:

   $ yumdownloader libvdpau.i686
   $ sudo rpm -U --oldpackage libvdpau*.rpm

7. Add libcuda.so symbolic link, if necessary

   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.

8. Perform the post-installation actions.

1. Perform the pre-installation actions.
2. Address custom xorg.conf, if applicable

   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.

3. 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.

4. Install repository meta-data

   $ sudo rpm --install cuda-repo-<distro>-<version>.<architecture>.rpm

5. Clean DNF repository cache

   $ sudo dnf clean expire-cache

6. Install CUDA

   $ sudo dnf install cuda

   The CUDA driver installation may fail if the RPMFusion non-free repository is enabled. In this case, CUDA installations should temporarily disable the RPMFusion non-free repository:

   $ sudo dnf --disablerepo="rpmfusion-nonfree*" install cuda

   If a system has installed both packages with the same instance of dnf, some driver components may be missing. Such an installation can be corrected by running:

   $ sudo dnf install cuda-drivers

   If the i686 libvdpau package dependency fails to install, try using the following steps to fix the issue:

   $ dnf download libvdpau.i686
   $ sudo rpm -U --oldpackage libvdpau*.rpm

7. Add libcuda.so symbolic link, if necessary

   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.

8. Perform the post-installation actions.

1. Perform the pre-installation actions.
2. Install repository meta-data

   $ sudo rpm --install cuda-repo-<distro>-<version>.<architecture>.rpm

3. Refresh Zypper repository cache

   $ sudo zypper refresh

4. Install CUDA

   $ sudo zypper install cuda

   The driver is provided in multiple packages, nvidia-gfxG04-kmp-desktop, nvidia-gfxG04-kmp-default, nvidia-gfxG04-kmp-trace, and their Unified Memory variants. When installing cuda, the correct driver packages should also be specified. Without doing this, zypper will select packages that may not work on the system. Run the following to detect the flavor of kernel and install cuda with the appropriate driver packages:

   $ uname -r
     3.4.6-2.10-<flavor>
   $ sudo zypper install cuda nvidia-gfxG04-kmp-<flavor>

5. Add the user to the video group

   $ sudo usermod -a -G video <username>

6. Install CUDA Samples GL dependencies

   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.

7. Perform the post-installation actions.

1. Perform the pre-installation actions.
2. Install repository meta-data

   $ sudo rpm --install cuda-repo-<distro>-<version>.<architecture>.rpm

3. Refresh Zypper repository cache

   $ sudo zypper refresh

4. Install CUDA

   $ sudo zypper install cuda

   The driver is provided in multiple packages, nvidia-gfxG04-kmp-desktop, nvidia-gfxG04-kmp-default, nvidia-gfxG04-kmp-trace, and their Unified Memory variants. When installing cuda, the correct driver packages should also be specified. Without doing this, zypper will select packages that may not work on the system. Run the following to detect the flavor of kernel and install cuda with the appropriate driver packages:

   $ uname -r
     3.4.6-2.10-<flavor>
   $ sudo zypper install cuda nvidia-gfxG04-kmp-<flavor>

5. Add the user to the video group

   $ sudo usermod -a -G video <username>

6. Perform the post-installation actions.

1. Perform the pre-installation actions.
2. Install repository meta-data

   $ sudo dpkg -i cuda-repo-<distro>_<version>_<architecture>.deb

3. Installing the CUDA public GPG key

   When installing using the local repo:

   $ sudo apt-key add /var/cuda-repo-<version>/7fa2af80.pub

   When installing using network repo:

   $ sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/<distro>/<architecture>/7fa2af80.pub

4. Update the Apt repository cache

   $ sudo apt-get update

5. Install CUDA

   $ sudo apt-get install cuda

6. Perform the post-installation actions.

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

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

1. Perform the pre-installation actions.

2. Disable the Nouveau drivers.

3. 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.

4. 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.

5. Run the installer and follow the on-screen prompts:

   $ sudo sh cuda_<version>_linux.run

   The installer will prompt for the following:

   • EULA Acceptance
   • CUDA Driver installation
   • CUDA Toolkit installation, location, and /usr/local/cuda symbolic link
   • CUDA Samples installation and location
   The default installation locations for the toolkit and samples are:

   Component	Default Installation Directory
   CUDA Toolkit	/usr/local/cuda-9.0
   CUDA Samples	$(HOME)/NVIDIA_CUDA-9.0_Samples

   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.

   The installer must be executed with sufficient privileges to perform some actions. When the current privileges are insufficient to perform an action, the installer will ask for the user's password to attempt to install with root privileges. Actions that cause the installer to attempt to install with root privileges are:

   • installing the CUDA Driver
   • installing the CUDA Toolkit to a location the user does not have permission to write to
   • installing the CUDA Samples to a location the user does not have permission to write to
   • creating the /usr/local/cuda symbolic link

   Running the installer with sudo, as shown above, will give permission to install to directories that require root permissions. Directories and files created while running the installer with sudo will have root ownership.

   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.

   Note: Installing Mesa may overwrite the /usr/lib/libGL.so that was previously installed by the NVIDIA driver, so a reinstallation of the NVIDIA driver might be required after installing these libraries.

6. Reboot the system to reload the graphical interface.

7. Verify the device nodes are created properly.

8. Perform the post-installation actions.

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

$ lsmod | grep nouveau

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 setuidnvidia-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:

#!/bin/bash

/sbin/modprobe nvidia

if [ "$?" -eq 0 ]; then
  # Count the number of NVIDIA controllers found.
  NVDEVS=`lspci | grep -i NVIDIA`
  N3D=`echo "$NVDEVS" | grep "3D controller" | wc -l`
  NVGA=`echo "$NVDEVS" | grep "VGA compatible controller" | wc -l`

  N=`expr $N3D + $NVGA - 1`
  for i in `seq 0 $N`; do
    mknod -m 666 /dev/nvidia$i c 195 $i
  done

  mknod -m 666 /dev/nvidiactl c 195 255

else
  exit 1
fi

/sbin/modprobe nvidia-uvm

if [ "$?" -eq 0 ]; then
  # Find out the major device number used by the nvidia-uvm driver
  D=`grep nvidia-uvm /proc/devices | awk '{print $1}'`

  mknod -m 666 /dev/nvidia-uvm c $D 0
else
  exit 1
fi

Cluster management packages are provided as an alternative set of RPM and Deb packages intended to be used by deployment management tools as standalone packages. These packages are available for RHEL 6, RHEL 7, Ubuntu 14.04, and Ubuntu 16.04 on the x86_64 architecture. There are three parts to the cluster management packages: the CUDA toolkit packages, the NVIDIA driver packages, and the README.

The cluster management toolkit packages are split into a runtime package, cuda-cluster-runtime-9-0, and a development package, cuda-cluster-devel-9-0. The development package depends on the runtime package. The driver packages are the same as what is provided in the general RPM and Deb solution used in the Package Management section.

The README describes the package and kernel source dependencies of the cluster management packages. The README also describes the order of installation of the standalone cluster management packages.

Some of the following steps may have already been performed as part of the native Ubuntu installation. Such steps can safely be skipped.

These steps should be performed on the x86_64 host system, rather than the target system. To install the native CUDA Toolkit on the target system, refer to the native Ubuntu installation section.

1. Perform the pre-installation actions.
2. Enable the foreign architecture

   The foreign architecture must be enabled in order to install the cross-platform toolkit. To enable armhf, arm64 (aarch64), or ppc64el as a foreign architecture, the following commands must be executed, replacing <arch> with the desired architecture:

   $ sudo dpkg --add-architecture <arch>
   $ sudo apt-get update

3. Install repository meta-data
   Note: When using a proxy server with aptitude, ensure that wget is set up to use the same proxy settings before installing the cuda-repo package.

   $ sudo dpkg -i cuda-repo-<distro>_<version>_amd64.deb

4. Update the Apt repository cache

   $ sudo apt-get update

5. Install the cross-platform CUDA Toolkit
   To install the cross-platform toolkit for armhf, aarch64, or ppc64el, the following command must be executed, replacing <arch> with the desired architecture:

   $ sudo apt-get install cuda-cross-<arch>

6. Perform the post-installation actions.

This section describes the options used to build cross-platform samples. TARGET_ARCH=<arch> and TARGET_OS=<os> should be chosen based on the supported targets shown below. TARGET_FS=<path> can be used to point nvcc to libraries and headers used by the sample.

Nsight Eclipse Edition supports cross-platform development. See the Nsight Eclipse Edition Getting Started Guide for more details.

Some actions must be taken after the installation before the CUDA Toolkit and Driver can be used.

Other actions are recommended to verify the integrity of the installation.

Other options are not necessary to use the CUDA Toolkit, but are available to provide additional features.

$ ./runfile.run --silent \
                --toolkit --toolkitpath=/my/new/toolkit \
                --samples --samplespath=/my/new/samples

The RPM and Deb packages cannot be installed to a custom install location directly using the package managers. See the "Install CUDA to a specific directory using the Package Manager installation method" scenario in the Advanced Setup section for more information.

These errors occur after adding a foreign architecture because apt is attempting to query for each architecture within each repository listed in the system's sources.list file. Repositories that do not host packages for the newly added architecture will present this error. While noisy, the error itself does no harm. Please see the Advanced Setup section for details on how to modify your sources.list file to prevent these errors.

To make sure X doesn't use a certain GPU for display, you need to specify which other GPU to use for display. For more information, please refer to the "Use a specific GPU for rendering the display" scenario in the Advanced Setup section.

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.

See the Package Manager Installation section for more details.

System updates may include an updated Linux kernel. In many cases, a new Linux kernel will be installed without properly updating the required Linux kernel headers and development packages. To ensure the CUDA driver continues to work when performing a system update, rerun the commands in the Kernel Headers and Development Packages section.

$ sudo akmods --force
$ sudo depmod

To install a CUDA driver at a version earlier than 367 using a network repo, the required packages will need to be explicitly installed at the desired version. For example, to install 352.99, instead of installing the cuda-drivers metapackage at version 352.99, you will need to install all required packages of cuda-drivers at version 352.99.

Depending on your system configuration, you may not be able to install old versions of CUDA using the cuda metapackage. In order to install a specific version of CUDA, you may need to specify all of the packages that would normally be installed by the cuda metapackage at the version you want to install.