- Электрический блогнот
- мои заметки на полях
- Linux как установить CUDA
- Предисловие
- Шаг 1 — проверяем nvidia драйвер
- Шаг 2 — качаем CUDA Toolkit
- Шаг 3 — устанавливаем CUDA Toolkit
- Шаг 4 — Тест
- Шаг 5 — устанавливаем cuDNN
- Выводы
- Installation Guide¶
- Supported Platforms¶
- Linux Distributions¶
- Container Runtimes¶
- Pre-Requisites¶
- NVIDIA Drivers¶
- Platform Requirements¶
- Docker¶
- Getting Started¶
- Installing on Ubuntu and Debian¶
- Installing on CentOS 7/8В¶
- Install cuda drivers linux
- 1. Introduction
- 2. Windows
- 2.1. Network Installer
- 2.2. Local Installer
- 2.3. Pip Wheels — Windows
- 2.4. 3.1.6. 3.2.3. Conda
- 3. Linux
- 3.1. Linux x86_64
- 3.1.1. Redhat / CentOS
- 3.1.1.1. RPM Installer
- 3.1.1.2. Runfile Installer
- 3.1.2. Fedora
- 3.1.2.1. RPM Installer
- 3.1.2.2. Runfile Installer
- 3.1.3. SUSE Linux Enterprise Server
- 3.1.3.1. RPM Installer
- 3.1.3.2. Runfile Installer
- 3.1.4. OpenSUSE
- 3.1.4.1. RPM Installer
- 3.1.4.2. Runfile Installer
- 3.1.5. Pip Wheels — Linux
- Conda
- 3.1.7. WSL
- 3.1.8. Ubuntu
- 3.1.8.1. Debian Installer
- 3.1.8.2. Runfile Installer
- 3.1.9. Debian
- 3.1.9.1. Debian Installer
- 3.1.9.2. Runfile Installer
- 3.2. Linux POWER8
- 3.2.1. Ubuntu
- 3.2.1.1. Debian Installer
- 3.2.2. Redhat / CentOS
- 3.2.2.1. RPM Installer
- Conda
- Notices
- Notice
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:
- Наличие карты Nvidia (будем считать, что она уже есть);
- Установленные в системе драйвера от Nvidia (будем исходить из того, что тоже установлены);
- 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:
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.
Источник
Installation Guide¶
Supported Platforms¶
The NVIDIA Container Toolkit is available on a variety of Linux distributions and supports different container engines.
Linux Distributions¶
Supported Linux distributions are listed below:
OS Name / Version
Amazon Linux 2017.09
Amazon Linux 2018.03
Open Suse/SLES 15.0
Open Suse/SLES 15.x
Debian Linux 10
(*) Minor releases of RHEL 7 and RHEL 8 (i.e. 7.4 -> 7.9 are symlinked to centos7 and 8.0 -> 8.3 are symlinked to centos8 resp.)
Container Runtimes¶
Supported container runtimes are listed below:
OS Name / Version
RHEL/CentOS 8 podman
CentOS 8 Docker
RHEL/CentOS 7 Docker
On Red Hat Enterprise Linux (RHEL) 8, Docker is no longer a supported container runtime. See Building, Running and Managing Containers for more information on the container tools available on the distribution.
Pre-Requisites¶
NVIDIA Drivers¶
Before you get started, make sure you have installed the NVIDIA driver for your Linux distribution. The recommended way to install drivers is to use the package manager for your distribution but other installer mechanisms are also available (e.g. by downloading .run installers from NVIDIA Driver Downloads).
For instructions on using your package manager to install drivers from the official CUDA network repository, follow the steps in this guide.
Platform Requirements¶
The list of prerequisites for running NVIDIA Container Toolkit is described below:
GNU/Linux x86_64 with kernel version > 3.10
Docker >= 19.03 (recommended, but some distributions may include older versions of Docker. The minimum supported version is 1.12)
NVIDIA GPU with Architecture >= Kepler (or compute capability 3.0)
NVIDIA Linux drivers >= 418.81.07 (Note that older driver releases or branches are unsupported.)
Your driver version might limit your CUDA capabilities. Newer NVIDIA drivers are backwards-compatible with CUDA Toolkit versions, but each new version of CUDA requires a minimum driver version. Running a CUDA container requires a machine with at least one CUDA-capable GPU and a driver compatible with the CUDA toolkit version you are using. The machine running the CUDA container only requires the NVIDIA driver, the CUDA toolkit doesn’t have to be installed. The CUDA release notes includes a table of the minimum driver and CUDA Toolkit versions.
Docker¶
Getting Started¶
For installing Docker CE, follow the official instructions for your supported Linux distribution. For convenience, the documentation below includes instructions on installing Docker for various Linux distributions.
If you are migrating fron nvidia-docker 1.0, then follow the instructions in the Migration from nvidia-docker 1.0 guide.
Installing on Ubuntu and Debian¶
The following steps can be used to setup NVIDIA Container Toolkit on Ubuntu LTS — 16.04, 18.04, 20.4 and Debian — Stretch, Buster distributions.
Setting up Docker¶
Docker-CE on Ubuntu can be setup using Docker’s official convenience script:
Follow the official instructions for more details and post-install actions.
Setting up NVIDIA Container Toolkit¶
Setup the stable repository and the GPG key:
To get access to experimental features such as CUDA on WSL or the new MIG capability on A100, you may want to add the experimental branch to the repository listing:
Install the nvidia-docker2 package (and dependencies) after updating the package listing:
Restart the Docker daemon to complete the installation after setting the default runtime:
At this point, a working setup can be tested by running a base CUDA container:
This should result in a console output shown below:
Installing on CentOS 7/8В¶
The following steps can be used to setup the NVIDIA Container Toolkit on CentOS 7/8.
Setting up Docker on CentOS 7/8В¶
If you’re on a cloud instance such as EC2, then the official CentOS images may not include tools such as iptables which are required for a successful Docker installation. Try this command to get a more functional VM, before proceeding with the remaining steps outlined in this document.
Источник
Install cuda drivers 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.
- Launch the downloaded installer package.
- Read and accept the EULA.
- Select «next» to download and install all components.
- Once the download completes, the installation will begin automatically.
- Once the installation completes, click «next» to acknowledge the Nsight Visual Studio Edition installation summary.
- Click «close» to close the installer.
- Navigate to the CUDA Samples’ nbody directory.
- 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.
- Launch the downloaded installer package.
- Read and accept the EULA.
- Select «next» to install all components.
- Once the installation completes, click «next» to acknowledge the Nsight Visual Studio Edition installation summary.
- Click «close» to close the installer.
- Navigate to the CUDA Samples’ nbody directory.
- 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.
- Install EPEL to satisfy the DKMS dependency by following the instructions at EPEL’s website.
- 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:
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.
- Install the RPMFusion free repository to satisfy the Akmods dependency:
- Install the repository meta-data, clean the dnf 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:
3.1.2.2. 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.
- Install the repository meta-data, refresh the Zypper cache, and install CUDA:
- Add the user to the video group:
- 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 vectorAdd sample:
3.1.3.2. Runfile Installer
Perform the following steps to install CUDA and verify the installation.
- Reboot into runlevel 3 by temporarily adding the number «3» and the word «nomodeset» to the end of the system’s kernel boot parameters.
- Run the installer silently to install with the default selections (implies acceptance of the EULA):
- Create an xorg.conf file to use the NVIDIA GPU for display:
- Reboot the system to load the graphical interface.
- 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 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.
- Install the repository meta-data, refresh the Zypper cache, and install CUDA:
- Add the user to the video group:
- 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:
3.1.4.2. 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.
- Install the repository meta-data, install GPG key, update the apt-get 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:
3.1.8.2. 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.
- Install the repository meta-data, install GPG key, update the apt-get 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:
3.1.9.2. 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.
- Install the repository meta-data, update the apt-get 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 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.
- Install EPEL to satisfy the DKMS dependency by following the instructions at EPEL’s website.
- 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 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
Notice
This document is provided for information purposes only and shall not be regarded as a warranty of a certain functionality, condition, or quality of a product. NVIDIA Corporation (“NVIDIA”) makes no representations or warranties, expressed or implied, as to the accuracy or completeness of the information contained in this document and assumes no responsibility for any errors contained herein. NVIDIA shall have no liability for the consequences or use of such information or for any infringement of patents or other rights of third parties that may result from its use. This document is not a commitment to develop, release, or deliver any Material (defined below), code, or functionality.
NVIDIA reserves the right to make corrections, modifications, enhancements, improvements, and any other changes to this document, at any time without notice.
Customer should obtain the latest relevant information before placing orders and should verify that such information is current and complete.
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