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Установка cuda astra linux

Содержание
  1. fromigorek
  2. понедельник, 3 марта 2014 г.
  3. CUDA install on Astra-linux
  4. Installation instructions¶
  5. Windows, binary¶
  6. Linux/Windows, using conda for python¶
  7. Linux, from source¶
  8. For Matlab¶
  9. For Python¶
  10. Windows, from source using Visual Studio 2015В¶
  11. Using the Visual Studio IDE¶
  12. Using .bat scripts in build\msvc¶
  13. Linux, building conda packages¶
  14. Электрический блогнот
  15. мои заметки на полях
  16. Linux как установить CUDA
  17. Предисловие
  18. Шаг 1 — проверяем nvidia драйвер
  19. Шаг 2 — качаем CUDA Toolkit
  20. Шаг 3 — устанавливаем CUDA Toolkit
  21. Шаг 4 — Тест
  22. Шаг 5 — устанавливаем cuDNN
  23. Выводы
  24. Установка cuda astra linux
  25. 1. Introduction
  26. 2. Windows
  27. 2.1. Network Installer
  28. 2.2. Local Installer
  29. 2.3. Pip Wheels — Windows
  30. 2.4. 3.1.6. 3.2.3. Conda
  31. 3. Linux
  32. 3.1. Linux x86_64
  33. 3.1.1. Redhat / CentOS
  34. 3.1.1.1. RPM Installer
  35. 3.1.1.2. Runfile Installer
  36. 3.1.2. Fedora
  37. 3.1.2.1. RPM Installer
  38. 3.1.2.2. Runfile Installer
  39. 3.1.3. SUSE Linux Enterprise Server
  40. 3.1.3.1. RPM Installer
  41. 3.1.3.2. Runfile Installer
  42. 3.1.4. OpenSUSE
  43. 3.1.4.1. RPM Installer
  44. 3.1.4.2. Runfile Installer
  45. 3.1.5. Pip Wheels — Linux
  46. Conda
  47. 3.1.7. WSL
  48. 3.1.8. Ubuntu
  49. 3.1.8.1. Debian Installer
  50. 3.1.8.2. Runfile Installer
  51. 3.1.9. Debian
  52. 3.1.9.1. Debian Installer
  53. 3.1.9.2. Runfile Installer
  54. 3.2. Linux POWER8
  55. 3.2.1. Ubuntu
  56. 3.2.1.1. Debian Installer
  57. 3.2.2. Redhat / CentOS
  58. 3.2.2.1. RPM Installer
  59. Conda
  60. Notices
  61. Notice

fromigorek

понедельник, 3 марта 2014 г.

CUDA install on Astra-linux

# Установлена Astra-linux-orel-1.9 по дефолту
# Качаю драйвера NVIDIA и CUDA
mkdir -p /root/src/cuda
cd /root/src/cuda
wget http://us.download.nvidia.com/XFree86/Linux-x86_64/331.49/NVIDIA-Linux-x86_64-331.49.run
wget http://developer.download.nvidia.com/compute/cuda/5_5/rel/installers/cuda_5.5.22_linux_64.run

# Для установки проприетарного драйвера NVIDIA, необходимо предварительно выгрузить модуль NOUVEAU
# Данный модуль подгружается еще на этапе загрузки initrd (так сделано в Astra-linux)
# Поэтому пришлось препарировать initrd, чтобы избавиться от NOUVEAU
mkdir /root/src/initrd
cd /root/src/initrd
cp -a /boot/initrd.img-3.2.0-27-generic .
mv initrd.img-3.2.0-27-generic initrd.img.gz
gunzip initrd.img.gz
cpio -i
mv initrd.img ../
rm ./lib/modules/3.2.0-27-generic/kernel/drivers/gpu/drm/nouveau/nouveau.ko
find . | cpio -o -H newc | gzip -9 > ../initrd.img-3.2.0-27-generic
mv /boot/initrd.img-3.2.0-27-generic /boot/initrd.img-3.2.0-27-generic.save
cp -a ../initrd.img-3.2.0-27-generic /boot

# Блэклист для NOUVEAU модуля
touch /etc/modprobe.d/nvidia-installer-disable-nouveau.conf
echo «blacklist nouveau» > /etc/modprobe.d/nvidia-installer-disable-nouveau.conf
echo «options nouveau modeset=0» >> /etc/modprobe.d/nvidia-installer-disable-nouveau.conf

# Необходимые пакеты для установки драйвера NVIDIA (пакеты ставились с DVD)
apt-get update
apt-get install linux-headers-`uname -r` binutils pkg-config build-essential xserver-xorg-dev
reboot

# После перезагрузки проверяю отсутствие модуля NOUVEAU
lsmod |grep nouveau

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Installation instructions¶

Windows, binary¶

Add the mex and tools subdirectories to your MATLAB path, or copy the Python astra module to your Python site-packages directory. We require the Microsoft Visual Studio 2015 redistributable package. If this is not already installed on your system, it is included as vc_redist.x64.exe in the ASTRA zip file.

Linux/Windows, using conda for python¶

Requirements: conda python environment, with 64 bit Python 2.7, 3.5 or 3.6.

There are packages available for the ASTRA Toolbox in the astra-toolbox channel for the conda package manager. To use these, run the following inside a conda environment.

Linux, from source¶

For Matlab¶

Requirements: g++, boost, CUDA (5.5 or higher), Matlab (R2012a or higher)

Add $HOME/astra/matlab and its subdirectories (tools, mex) to your matlab path.

If you want to build the Octave interface instead of the Matlab interface, specify –enable-octave instead of –with-matlab=… . The Octave files will be installed into $HOME/astra/octave .

NB: Each matlab version only supports a specific range of g++ versions. Despite this, if you have a newer g++ and if you get errors related to missing GLIBCXX_3.4.xx symbols, it is often possible to work around this requirement by deleting the version of libstdc++ supplied by matlab in MATLAB_PATH/bin/glnx86 or MATLAB_PATH/bin/glnxa64 (at your own risk), or setting LD_PRELOAD=/usr/lib64/libstdc++.so.6 (or similar) when starting matlab.

For Python¶

Requirements: g++, boost, CUDA (5.5 or higher), Python (2.7 or 3.x)

This will install ASTRA into your current Python environment.

Windows, from source using Visual Studio 2015В¶

Requirements: Visual Studio 2015 (full or community), boost (recent), CUDA 8.0, Matlab (R2012a or higher) and/or WinPython 2.7/3.x.

Using the Visual Studio IDE¶

Set the environment variable MATLAB_ROOT to your matlab install location.

Copy boost headers to lib\include\boost (i.e., copy the boost subdirectory from the boost source archive to lib\include), and boost libraries to lib\x64.

Open astra_vc14.sln in Visual Studio.

Select the appropriate solution configuration (typically Release_CUDA|x64).

Build the solution.

Install by copying AstraCuda64.dll and all .mexw64 files from bin\x64\Release_CUDA and the entire matlab\tools directory to a directory to be added to your matlab path.

Using .bat scripts in build\msvc¶

Edit build_env.bat and set up the correct directories.

Run build_setup.bat to automatically copy the boost headers and libraries.

For matlab: Run build_matlab.bat. The .dll and .mexw64 files will be in bin\x64\Release_Cuda.

For python 2.7/3.5: Run build_python27.bat or build_python35.bat. ASTRA will be directly installed into site-packages.

Linux, building conda packages¶

To build your own conda packages for the ASTRA toolbox, perform the following steps inside the conda environment:

© Copyright 2010-2019, imec-Vision Lab, University of Antwerp; 2014-2019, CWI, Amsterdam

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Электрический блогнот

мои заметки на полях

Linux как установить CUDA

Установить CUDA (Compute Unified Device Architecture) библиотеки в Linux очень легко и в тоже время сложно. Казалось бы, что тут сложного, сделай какой-нибудь apt-get install cuda или yum install cuda и система на автомате все сама установит. Действительно, во многих случаях этого достаточно, но, как говорится, есть нюансы.

Так вот, чтобы использовать всю мощь вашей графической карты необходимо выполнение следующих условий:

  1. Наличие карты Nvidia (будем считать, что она уже есть);
  2. Установленные в системе драйвера от Nvidia (будем исходить из того, что тоже установлены);
  3. CUDA Toolkit, те самые библиотеки и программы, которые чаще всего для простоты называют CUDA (без Toolkit)

Вот пунктом номер 3 мы и будем заниматься в этой статье.
Все последующие шаги будут приведены для Ubuntu 18.04 (самая популярная система), но они так же подойдут и для других дистрибутивов Linux.

Предисловие

Устанавливать CUDA будем от обычного пользователя, в домашнюю папку. Я не сторонник установки в /usr/local таких вещей, которые часто приходится обновлять. Лучше поставить куда-нибудь в безопасное место, чтобы не запороть работающую систему. Например, /home/username/cuda подойдет отлично. Надоест эксперементировать с CUDA, просто удалите эту папку и все. И не надо заботиться, что какие-то зависимости нарушились в системе.

Шаг 1 — проверяем nvidia драйвер

Исходим из того, что Nvidia карточка у ва есть и nvidia драйвер установлен в систему и запущен.

Проверяем:
lsmod | grep -i nvidia
вывод должен быть похожим на следующий:

Далее определяем версию nvidia драйвера с помощью команды modinfo:

Есть еще один способ определить версию драйвера. Для этого воспользуемся утилитой nvidia-smi:

Nvidia-smi так же выдала версию 435.21.
Если nvidia-smi не будет в вашей системе, то пользуйтесь способом с modinfo.

Шаг 2 — качаем CUDA Toolkit

Между весрией Nvidia драйвера и версией CUDA Toolkit существует связь. Для определенной версии Nvidia драйвера нужно скачивать и устанавливать строго соответствующий пакет CUDA Toolkit, иначе ничего не получится. Опять же есть два способа определить версию CUDA Toolkit.

Первый способ:
идем на страницу cuda toolkit release notes и в таблице «Table 1. CUDA Toolkit and Compatible Driver Versions» ищем нужное соотвествие между версией драйвера и версией CUDA Toolkit:

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Например, на моем ноуте установлен nvidia драйвер версии 435.21, значит мне подойдут все версии CUDA Toolkit кроме 10.2. Иными словами 10.1 включительно и ниже.
Если у вас драйвер версии 390, то CUDA Toolkit надо скачивать версии 9.1 и ниже.

Второй способ:
можно снова воспользоваться утилитой nvidia-smi:

здесь четко написано, для вашего драйвера нужна CUDA 10.1.

После того, как определились с версией CUDA Toolkit идем и скачиваем его со страницы:
https://developer.nvidia.com/cuda-toolkit-archive


Здесь выбираем:
Linux -> x86_64 -> Ubuntu -> 18.04 -> runfile (local)

После скачивания в директории для загрузок появится файл:
cuda_10.1.105_418.39_linux.run

Шаг 3 — устанавливаем CUDA Toolkit

Инсталлер скачан. Сделаем его исполняемым:

И сразу же запускаем:

Запускается долго (наверняка происходит самораспаковка).

После соглашения с EULA появляется экран:

Как видите здесь размечен драйвер, мы его устанавливать не будем, он уже в системе и запущен.

Далее наводи курсор на «CUDA Toolkit 10.1» и жмем букву «A», тем самым переходя к расширенным настройкам:

Здесь делаем неактивными все позиции, как на скриншоте и переходим в «Change Toolkit Installation Path» и вводим имя директории для установки:

в прцессе установки нужно будет еще ввести «Root install path» вводим туже саму директорию:

Когда установка завершится нужно будет дать системе знать куда установлена CUDA, для этого в файл

/.bashrc прописываем следующие строки:

На этом установка закончена.

Шаг 4 — Тест

Тестируем связку CUDA и драйвера Nvidia. Для этого воспользуемся примеры из устанвки CUDA.
Возьмем тест с частицами.

Как видно из рисунка, тест запустился и судя по выводу nvidia-smi на 24% нагружает видеокарту. Буковки C+G перед ./particles говорят о том, что задействованы и вычислительные (С) и графические (G) ресурсы видеокарты.

Шаг 5 — устанавливаем cuDNN

Если вы планируете использовать CUDA в машинном обучении, то просто необходимо устанвить библиотеку cuDNN. Этабиблиотека позволяет максимально эффективно использовать мощности графического ускорителя при работе с нейронными сетями. Ставится cuDNN элементрано:
1) регистрируетесь;
2) скачиваете нужную версию (для каждой CUDA своя cuDNN);
3) распаковываете архив в папку куда установлена CUDA.

Выводы

В данной статье приведено описание способа установки CUDA библиотек в Linux в случае, когда графический драйвер уже установлен, а у пользователя нет прав администратора.

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Установка cuda astra linux

Minimal first-steps instructions to get CUDA running on a standard system.

1. Introduction

This guide covers the basic instructions needed to install CUDA and verify that a CUDA application can run on each supported platform.

These instructions are intended to be used on a clean installation of a supported platform. For questions which are not answered in this document, please refer to the Windows Installation Guide and Linux Installation Guide.

The CUDA installation packages can be found on the CUDA Downloads Page.

2. Windows

When installing CUDA on Windows, you can choose between the Network Installer and the Local Installer. The Network Installer allows you to download only the files you need. The Local Installer is a stand-alone installer with a large initial download. For more details, refer to the Windows Installation Guide.

2.1. Network Installer

Perform the following steps to install CUDA and verify the installation.

  1. Launch the downloaded installer package.
  2. Read and accept the EULA.
  3. Select «next» to download and install all components.
  4. Once the download completes, the installation will begin automatically.
  5. Once the installation completes, click «next» to acknowledge the Nsight Visual Studio Edition installation summary.
  6. Click «close» to close the installer.
  7. Navigate to the CUDA Samples’ nbody directory.
  8. Open the nbody Visual Studio solution file for the version of Visual Studio you have installed.

2.2. Local Installer

Perform the following steps to install CUDA and verify the installation.

  1. Launch the downloaded installer package.
  2. Read and accept the EULA.
  3. Select «next» to install all components.
  4. Once the installation completes, click «next» to acknowledge the Nsight Visual Studio Edition installation summary.
  5. Click «close» to close the installer.
  6. Navigate to the CUDA Samples’ nbody directory.
  7. Open the nbody Visual Studio solution file for the version of Visual Studio you have installed.

2.3. Pip Wheels — Windows

NVIDIA provides Python Wheels for installing CUDA through pip, primarily for using CUDA with Python. These packages are intended for runtime use and do not currently include developer tools (these can be installed separately).

Please note that with this installation method, CUDA installation environment is managed via pip and additional care must be taken to set up your host environment to use CUDA outside the pip environment.

2.4. 3.1.6. 3.2.3. Conda

The Conda packages are available at https://anaconda.org/nvidia.

To perform a basic install of all CUDA Toolkit components using Conda, run the following command:

To uninstall the CUDA Toolkit using Conda, run the following command:

3. Linux

CUDA on Linux can be installed using an RPM, Debian, Runfile, or Conda package, depending on the platform being installed on.

3.1. Linux x86_64

For development on the x86_64 architecture. In some cases, x86_64 systems may act as host platforms targeting other architectures. See the Linux Installation Guide for more details.

3.1.1. Redhat / CentOS

When installing CUDA on Redhat or CentOS, you can choose between the Runfile Installer and the RPM Installer. The Runfile Installer is only available as a Local Installer. The RPM Installer is available as both a Local Installer and a Network Installer. The Network Installer allows you to download only the files you need. The Local Installer is a stand-alone installer with a large initial download. In the case of the RPM installers, the instructions for the Local and Network variants are the same. For more details, refer to the Linux Installation Guide.

3.1.1.1. RPM Installer

Perform the following steps to install CUDA and verify the installation.

  1. Install EPEL to satisfy the DKMS dependency by following the instructions at EPEL’s website.
  2. Enable optional repos:

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

  • On x86_64 workstation:
  • On POWER9 system:
  • On x86_64 server:
  • Install the repository meta-data, clean the yum cache, and install CUDA:
  • Reboot the system to load the NVIDIA drivers.
  • Set up the development environment by modifying the PATH and LD_LIBRARY_PATH variables:
  • Install a writable copy of the samples then build and run the nbody sample:

    3.1.1.2. Runfile Installer

    Perform the following steps to install CUDA and verify the installation.

    3.1.2. Fedora

    When installing CUDA on Fedora, you can choose between the Runfile Installer and the RPM Installer. The Runfile Installer is only available as a Local Installer. The RPM Installer is available as both a Local Installer and a Network Installer. The Network Installer allows you to download only the files you need. The Local Installer is a stand-alone installer with a large initial download. In the case of the RPM installers, the instructions for the Local and Network variants are the same. For more details, refer to the Linux Installation Guide.

    3.1.2.1. RPM Installer

    Perform the following steps to install CUDA and verify the installation.

    1. Install the RPMFusion free repository to satisfy the Akmods dependency:
    2. Install the repository meta-data, clean the dnf cache, and install CUDA:
    3. Reboot the system to load the NVIDIA drivers.
    4. Set up the development environment by modifying the PATH and LD_LIBRARY_PATH variables:
    5. Install a writable copy of the samples then build and run the nbody sample:

    3.1.2.2. Runfile Installer

    Perform the following steps to install CUDA and verify the installation.

    3.1.3. SUSE Linux Enterprise Server

    When installing CUDA on SUSE Linux Enterprise Server, you can choose between the Runfile Installer and the RPM Installer. The Runfile Installer is only available as a Local Installer. The RPM Installer is available as both a Local Installer and a Network Installer. The Network Installer allows you to download only the files you need. The Local Installer is a stand-alone installer with a large initial download. In the case of the RPM installers, the instructions for the Local and Network variants are the same. For more details, refer to the Linux Installation Guide.

    3.1.3.1. RPM Installer

    Perform the following steps to install CUDA and verify the installation.

    1. Install the repository meta-data, refresh the Zypper cache, and install CUDA:
    2. Add the user to the video group:
    3. Reboot the system to load the NVIDIA drivers.
    4. Set up the development environment by modifying the PATH and LD_LIBRARY_PATH variables:
    5. Install a writable copy of the samples then build and run the vectorAdd sample:

    3.1.3.2. Runfile Installer

    Perform the following steps to install CUDA and verify the installation.

    1. Reboot into runlevel 3 by temporarily adding the number «3» and the word «nomodeset» to the end of the system’s kernel boot parameters.
    2. Run the installer silently to install with the default selections (implies acceptance of the EULA):
    3. Create an xorg.conf file to use the NVIDIA GPU for display:
    4. Reboot the system to load the graphical interface.
    5. Set up the development environment by modifying the PATH and LD_LIBRARY_PATH variables:
    6. Install a writable copy of the samples then build and run the vectorAdd sample:

    3.1.4. OpenSUSE

    When installing CUDA on OpenSUSE, you can choose between the Runfile Installer and the RPM Installer. The Runfile Installer is only available as a Local Installer. The RPM Installer is available as both a Local Installer and a Network Installer. The Network Installer allows you to download only the files you need. The Local Installer is a stand-alone installer with a large initial download. In the case of the RPM installers, the instructions for the Local and Network variants are the same. For more details, refer to the Linux Installation Guide.

    3.1.4.1. RPM Installer

    Perform the following steps to install CUDA and verify the installation.

    1. Install the repository meta-data, refresh the Zypper cache, and install CUDA:
    2. Add the user to the video group:
    3. Reboot the system to load the NVIDIA drivers.
    4. Set up the development environment by modifying the PATH and LD_LIBRARY_PATH variables:
    5. Install a writable copy of the samples then build and run the nbody sample:

    3.1.4.2. Runfile Installer

    Perform the following steps to install CUDA and verify the installation.

    3.1.5. Pip Wheels — Linux

    NVIDIA provides Python Wheels for installing CUDA through pip, primarily for using CUDA with Python. These packages are intended for runtime use and do not currently include developer tools (these can be installed separately).

    Please note that with this installation method, CUDA installation environment is managed via pip and additional care must be taken to set up your host environment to use CUDA outside the pip environment.

    The following metapackages will install the latest version of the named component on Linux for the indicated CUDA version. «cu11» should be read as «cuda11».

    • nvidia-cuda-runtime-cu11
    • nvidia-cuda-cupti-cu11
    • nvidia-cuda-nvcc-cu11
    • nvidia-nvml-dev-cu11
    • nvidia-cuda-nvrtc-cu11
    • nvidia-nvtx-cu11
    • nvidia-cuda-sanitizer-api-cu11
    • nvidia-cublas-cu11
    • nvidia-cufft-cu11
    • nvidia-curand-cu11
    • nvidia-cusolver-cu11
    • nvidia-cusparse-cu11
    • nvidia-npp-cu11
    • nvidia-nvjpeg-cu11

    These metapackages install the following packages:

    • nvidia-nvml-dev-cu114
    • nvidia-cuda-nvcc-cu114
    • nvidia-cuda-runtime-cu114
    • nvidia-cuda-cupti-cu114
    • nvidia-cublas-cu114
    • nvidia-cuda-sanitizer-api-cu114
    • nvidia-nvtx-cu114
    • nvidia-cuda-nvrtc-cu114
    • nvidia-npp-cu114
    • nvidia-cusparse-cu114
    • nvidia-cusolver-cu114
    • nvidia-curand-cu114
    • nvidia-cufft-cu114
    • nvidia-nvjpeg-cu114

    Conda

    The Conda packages are available at https://anaconda.org/nvidia.

    To perform a basic install of all CUDA Toolkit components using Conda, run the following command:

    To uninstall the CUDA Toolkit using Conda, run the following command:

    3.1.7. WSL

    These instructions must be used if you are installing in a WSL environment. Do not use the Ubuntu instructions in this case.

      Install repository meta-data

    When installing using the local repo:

    When installing using the network repo:

    Pin file to prioritize CUDA repository:

    Update the Apt repository cache and install CUDA

    3.1.8. Ubuntu

    When installing CUDA on Ubuntu, you can choose between the Runfile Installer and the Debian Installer. The Runfile Installer is only available as a Local Installer. The Debian Installer is available as both a Local Installer and a Network Installer. The Network Installer allows you to download only the files you need. The Local Installer is a stand-alone installer with a large initial download. In the case of the Debian installers, the instructions for the Local and Network variants are the same. For more details, refer to the Linux Installation Guide.

    3.1.8.1. Debian Installer

    Perform the following steps to install CUDA and verify the installation.

    1. Install the repository meta-data, install GPG key, update the apt-get cache, and install CUDA:
    2. Reboot the system to load the NVIDIA drivers.
    3. Set up the development environment by modifying the PATH and LD_LIBRARY_PATH variables:
    4. Install a writable copy of the samples then build and run the nbody sample:

    3.1.8.2. Runfile Installer

    Perform the following steps to install CUDA and verify the installation.

    3.1.9. Debian

    When installing CUDA on Debian 10, you can choose between the Runfile Installer and the Debian Installer. The Runfile Installer is only available as a Local Installer. The Debian Installer is available as both a Local Installer and a Network Installer. The Network Installer allows you to download only the files you need. The Local Installer is a stand-alone installer with a large initial download. For more details, refer to the Linux Installation Guide.

    3.1.9.1. Debian Installer

    Perform the following steps to install CUDA and verify the installation.

    1. Install the repository meta-data, install GPG key, update the apt-get cache, and install CUDA:
    2. Reboot the system to load the NVIDIA drivers.
    3. Set up the development environment by modifying the PATH and LD_LIBRARY_PATH variables:
    4. Install a writable copy of the samples then build and run the nbody sample:

    3.1.9.2. Runfile Installer

    Perform the following steps to install CUDA and verify the installation.

    3.2. Linux POWER8

    For development on the POWER8 architecture.

    3.2.1. Ubuntu

    When installing CUDA on Ubuntu on POWER8, you must use the Debian Installer. The Debian Installer is available as both a Local Installer and a Network Installer. The Network Installer allows you to download only the files you need. The Local Installer is a stand-alone installer with a large initial download. The instructions for the Local and Network variants are the same. For more details, refer to the Linux Installation Guide.

    3.2.1.1. Debian Installer

    Perform the following steps to install CUDA and verify the installation.

    1. Install the repository meta-data, update the apt-get cache, and install CUDA:
    2. Reboot the system to load the NVIDIA drivers.
    3. Set up the development environment by modifying the PATH and LD_LIBRARY_PATH variables:
    4. Install a writable copy of the samples then build and run the vectorAdd sample:

    3.2.2. Redhat / CentOS

    When installing CUDA on Redhat on POWER8, you must use the RPM Installer. The RPM Installer is available as both a Local Installer and a Network Installer. The Network Installer allows you to download only the files you need. The Local Installer is a stand-alone installer with a large initial download. The instructions for the Local and Network variants are the same. For more details, refer to the Linux Installation Guide.

    3.2.2.1. RPM Installer

    Perform the following steps to install CUDA and verify the installation.

    1. Install EPEL to satisfy the DKMS dependency by following the instructions at EPEL’s website.
    2. Install the repository meta-data, clean the yum cache, and install CUDA:
    3. Reboot the system to load the NVIDIA drivers.
    4. Set up the development environment by modifying the PATH and LD_LIBRARY_PATH variables:
    5. Install a writable copy of the samples then build and run the vectorAdd sample:

    Conda

    The Conda packages are available at https://anaconda.org/nvidia.

    To perform a basic install of all CUDA Toolkit components using Conda, run the following command:

    To uninstall the CUDA Toolkit using Conda, run the following command:

    Notices

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