Как установить cuda на windows 10

Шпаргалка по установке CUDA, cuDNN, Tensorflow и PyTorch на Windows 10

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В очередной раз после переустановки Windows осознал, что надо накатить драйвера, CUDA, cuDNN, Tensorflow/Keras для обучения нейронных сетей.

Каждый раз для меня это оказывается несложной, но времязатратной операцией: найти подходящую комбинацию Tensorflow/Keras, CUDA, cuDNN и Python несложно, но вспоминаю про эти зависимости только в тот момент, когда при импорте Tensorflow вижу, что видеокарта не обнаружена и начинаю поиск нужной страницы в документации Tensorflow.

В этот раз ситуация немного усложнилась. Помимо установки Tensorflow мне потребовалось установить PyTorch. Со своими зависимостями и поддерживаемыми версиями Python, CUDA и cuDNN.

По итогам нескольких часов экспериментов решил, что надо зафиксировать все полезные ссылки в одном посте для будущего меня.

Краткий алгоритм установки Tensorflow и PyTorch

Примечание: Установить Tensorflow и PyTorch можно в одном виртуальном окружении, но в статье этого алгоритма нет.

Подготовка к установке

Определить какая версия Python поддерживается Tensorflow и PyTorch (на момент написания статьи мне не удалось установить PyTorch в виртуальном окружении с Python 3.9.5)
Для выбранной версии Python найти подходящие версии Tensorflow и PyTorch
Определить, какие версии CUDA поддерживают выбранные ранее версии Tensorflow и PyTorch
Определить поддерживаемую версию cuDNN для Tensorflow – не все поддерживаемые CUDA версии cuDNN поддерживаются Tensorflow. Для PyTorch этой особенности не заметил

Установка CUDA и cuDNN

Скачиваем подходящую версию CUDA и устанавливаем. Можно установить со всеми значениями по умолчанию
Скачиваем cuDNN, подходящую для выбранной версии Tensorflow (п.1.2). Для скачивания cuDNN потребуется регистрация на сайте NVidia. “Установка” cuDNN заключается в распакове архива и заменой существующих файлов CUDA на файлы из архива

Устанавливаем Tensorflow

Создаём виртуальное окружение для Tensorflow c выбранной версией Python. Назовём его, например, py38tf
Переключаемся в окружение py38tf и устанавливаем поддерживаемую версию Tensorflow pip install tensorflow==x.x.x
Проверяем поддержку GPU командой

python -c "import tensorflow as tf; print('CUDA available' if tf.config.list_physical_devices('GPU') else 'CUDA not available')"

Устанавливаем PyTorch

Создаём виртуальное окружение для PyTorch c выбранной версией Python. Назовём его, например, py38torch
Переключаемся в окружение py38torch и устанавливаем поддерживаемую версию PyTorch
Проверяем поддержку GPU командой

python -c "import torch; print('CUDA available' if torch.cuda.is_available() else 'CUDA not available')"

В моём случае заработала комбинация:

Python 3.8.8
Драйвер NVidia 441.22
CUDA 10.1
cuDNN 7.6
Tensorflow 2.3.0
PyTorch 1.7.1+cu101

Tensorflow и PyTorch установлены в разных виртуальных окружениях.

Итого

Польза этой статьи будет понятна не скоро: систему переустанавливаю я не часто.

Если воспользуетесь этим алгоритмом и найдёте какие-то ошибки – пишите в комментарии

1. Introduction

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

CUDA was developed with several design goals in mind:

Provide a small set of extensions to standard programming languages, like C, that enable a straightforward implementation of parallel algorithms. With CUDA C/C++, programmers can focus on the task of parallelization of the algorithms rather than spending time on their implementation.
Support heterogeneous computation where applications use both the CPU and GPU. Serial portions of applications are run on the CPU, and parallel portions are offloaded to the GPU. As such, CUDA can be incrementally applied to existing applications. The CPU and GPU are treated as separate devices that have their own memory spaces. This configuration also allows simultaneous computation on the CPU and GPU without contention for memory resources.

CUDA-capable GPUs have hundreds of cores that can collectively run thousands of computing threads. These cores have shared resources including a register file and a shared memory. The on-chip shared memory allows parallel tasks running on these cores to share data without sending it over the system memory bus.

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

1.1. System Requirements

To use CUDA on your system, you will need the following installed:

A CUDA-capable GPU
A supported version of Linux with a gcc compiler and toolchain
NVIDIA CUDA Toolkit (available at https://developer.nvidia.com/cuda-downloads)

Supported Microsoft Windows ® operating systems:

Microsoft Windows 11 21H2
Microsoft Windows 11 22H2-SV2
Microsoft Windows 10
Microsoft Windows Server 2022
Microsoft Windows Server 2019

Table 1 Windows Compiler Support in CUDA 12.3 Update 2 

Cross-compilation (32-bit on 64-bit)

MSVC Version 193x

Visual Studio 2022 17.0

C++14 (default), C++17, C++20

MSVC Version 192x

Visual Studio 2019 16.x

C++14 (default), C++17, C++20

MSVC Version 191x

Visual Studio 2017 15.x (RTW and all updates)

C++14 (default), C++17

* Support for Visual Studio 2015 is deprecated in release 11.1.

32-bit compilation native and cross-compilation is removed from CUDA 12.0 and later Toolkit. Use the CUDA Toolkit from earlier releases for 32-bit compilation. CUDA Driver will continue to support running 32-bit application binaries on GeForce GPUs until Ada. Ada will be the last architecture with driver support for 32-bit applications. Hopper does not support 32-bit applications.

Support for running x86 32-bit applications on x86_64 Windows is limited to use with:

CUDA Driver
CUDA Runtime (cudart)
CUDA Math Library (math.h)

1.2. About This Document

This document is intended for readers familiar with Microsoft Windows operating systems and the Microsoft Visual Studio environment. You do not need previous experience with CUDA or experience with parallel computation.

2. Installing CUDA Development Tools

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

The setup of CUDA development tools on a system running the appropriate version of Windows consists of a few simple steps:

Verify the system has a CUDA-capable GPU.
Download the NVIDIA CUDA Toolkit.
Install the NVIDIA CUDA Toolkit.
Test that the installed software runs correctly and communicates with the hardware.

2.1. Verify You Have a CUDA-Capable GPU

You can verify that you have a CUDA-capable GPU through the Display Adapters section in the Windows Device Manager. Here you will find the vendor name and model of your graphics card(s). If you have an NVIDIA card that is listed in https://developer.nvidia.com/cuda-gpus, that GPU is CUDA-capable. The Release Notes for the CUDA Toolkit also contain a list of supported products.

The Windows Device Manager can be opened via the following steps:

Open a run window from the Start Menu
Run:

control /name Microsoft.DeviceManager

2.2. Download the NVIDIA CUDA Toolkit

The NVIDIA CUDA Toolkit is available at https://developer.nvidia.com/cuda-downloads. Choose the platform you are using and one of the following installer formats:

Network Installer: A minimal installer which later downloads packages required for installation. Only the packages selected during the selection phase of the installer are downloaded. This installer is useful for users who want to minimize download time.
Full Installer: An installer which contains all the components of the CUDA Toolkit and does not require any further download. This installer is useful for systems which lack network access and for enterprise deployment.

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

Download Verification

The download can be verified by comparing the MD5 checksum posted at https://developer.download.nvidia.com/compute/cuda/12.3.1/docs/sidebar/md5sum.txt with that of the downloaded file. If either of the checksums differ, the downloaded file is corrupt and needs to be downloaded again.

2.3. Install the CUDA Software

Before installing the toolkit, you should read the Release Notes, as they provide details on installation and software functionality.

The driver and toolkit must be installed for CUDA to function. If you have not installed a stand-alone driver, install the driver from the NVIDIA CUDA Toolkit.

The installation may fail if Windows Update starts after the installation has begun. Wait until Windows Update is complete and then try the installation again.

Graphical Installation

Install the CUDA Software by executing the CUDA installer and following the on-screen prompts.

Silent Installation

The installer can be executed in silent mode by executing the package with the -s flag. Additional parameters can be passed which will install specific subpackages instead of all packages. See the table below for a list of all the subpackage names.

Table 2 Possible Subpackage Names 

Toolkit Subpackages (defaults to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.3)

CUDA Profiler API.

CUDA Runtime libraries.

Extracts information from cubin files.

The CUDA Profiling Tools Interface for creating profiling and tracing tools that target CUDA applications.

The CUDA cu++ filt demangler tool.

Prebuilt demo applications using CUDA.

CUDA HTML and PDF documentation files including the CUDA C++ Programming Guide, CUDA C++ Best Practices Guide, CUDA library documentation, etc.

Extracts information from standalone cubin files.

NVML development libraries and headers.

Tool for collecting and viewing CUDA application profiling data from the command-line.

Prunes host object files and libraries to only contain device code for the specified targets.

NVRTC runtime libraries.

NVTX on Windows.

Compute Sanitizer API.

cuBLAS runtime libraries.

cuFFT runtime libraries.

cuRAND runtime libraries.

cuSOLVER runtime libraries.

cuSPARSE runtime libraries.

NPP runtime libraries.

Installs the Nsight Visual Studio Edition plugin in all VS.

Installs CUDA project wizard and builds customization files in VS.

Installs the CUDA_Occupancy_Calculator.xls tool.

The NVIDIA Display Driver. Required to run CUDA applications.

For example, to install only the compiler and driver components:

.exe -s nvcc_12.1 Display.Driver

Use the -n option if you do not want to reboot automatically after install or uninstall, even if reboot is required.

Extracting and Inspecting the Files Manually

Sometimes it may be desirable to extract or inspect the installable files directly, such as in enterprise deployment, or to browse the files before installation. The full installation package can be extracted using a decompression tool which supports the LZMA compression method, such as 7-zip or WinZip.

Once extracted, the CUDA Toolkit files will be in the CUDAToolkit folder, and similarily for CUDA Visual Studio Integration. Within each directory is a .dll and .nvi file that can be ignored as they are not part of the installable files.

Accessing the files in this manner does not set up any environment settings, such as variables or Visual Studio integration. This is intended for enterprise-level deployment.

2.3.1. Uninstalling the CUDA Software

All subpackages can be uninstalled through the Windows Control Panel by using the Programs and Features widget.

2.4. Using Conda to Install the CUDA Software

This section describes the installation and configuration of CUDA when using the Conda installer. The Conda packages are available at https://anaconda.org/nvidia.

2.4.1. Conda Overview

The Conda installation installs the CUDA Toolkit. The installation steps are listed below.

2.4.2. Installation

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

conda install cuda -c nvidia

2.4.3. Uninstallation

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

conda remove cuda

2.4.4. Installing Previous CUDA Releases

All Conda packages released under a specific CUDA version are labeled with that release version. To install a previous version, include that label in the install command such as:

conda install cuda -c nvidia/label/cuda-11.3.0

Some CUDA releases do not move to new versions of all installable components. When this is the case these components will be moved to the new label, and you may need to modify the install command to include both labels such as:

conda install cuda -c nvidia/label/cuda-11.3.0 -c nvidia/label/cuda-11.3.1

This example will install all packages released as part of CUDA 11.3.0.

2.5. Use a Suitable Driver Model

On Windows 10 and later, the operating system provides two driver models under which the NVIDIA Driver may operate:

The WDDM driver model is used for display devices.
The Tesla Compute Cluster (TCC) mode of the NVIDIA Driver is available for non-display devices such as NVIDIA Tesla GPUs and the GeForce GTX Titan GPUs; it uses the Windows WDM driver model.

TCC is enabled by default on most recent NVIDIA Tesla GPUs. To check which driver mode is in use and/or to switch driver modes, use the nvidia-smi tool that is included with the NVIDIA Driver installation (see nvidia-smi -h for details).

Keep in mind that when TCC mode is enabled for a particular GPU, that GPU cannot be used as a display device.

NVIDIA GeForce GPUs (excluding GeForce GTX Titan GPUs) do not support TCC mode.

2.6. Verify the Installation

Before continuing, it is important to verify that the CUDA toolkit can find and communicate correctly with the CUDA-capable hardware. To do this, you need to compile and run some of the included sample programs.

2.6.1. Running the Compiled Examples

The version of the CUDA Toolkit can be checked by running nvcc -V in a Command Prompt window. You can display a Command Prompt window by going to:

Start > All Programs > Accessories > Command Prompt

CUDA Samples are located in https://github.com/nvidia/cuda-samples. To use the samples, clone the project, build the samples, and run them using the instructions on the Github page.

To verify a correct configuration of the hardware and software, it is highly recommended that you build and run the deviceQuery sample program. The sample can be built using the provided VS solution files in the deviceQuery folder.

This assumes that you used the default installation directory structure. If CUDA is installed and configured correctly, the output should look similar to Figure 1.

Valid Results from deviceQuery CUDA Sample

The exact appearance and the output lines might be different on your system. The important outcomes are that a device was found, that the device(s) match what is installed in your system, and that the test passed.

If a CUDA-capable device and the CUDA Driver are installed but deviceQuery reports that no CUDA-capable devices are present, ensure the deivce and driver are properly installed.

Running the bandwidthTest program, located in the same directory as deviceQuery above, ensures that the system and the CUDA-capable device are able to communicate correctly. The output should resemble Figure 2.

Valid Results from bandwidthTest CUDA Sample

The device name (second line) and the bandwidth numbers vary from system to system. The important items are the second line, which confirms a CUDA device was found, and the second-to-last line, which confirms that all necessary tests passed.

If the tests do not pass, make sure you do have a CUDA-capable NVIDIA GPU on your system and make sure it is properly installed.

To see a graphical representation of what CUDA can do, run the particles sample at

https://github.com/NVIDIA/cuda-samples/tree/master/Samples/particles

3. Pip Wheels

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.

Prerequisites

To install Wheels, you must first install the nvidia-pyindex package, which is required in order to set up your pip installation to fetch additional Python modules from the NVIDIA NGC PyPI repo. If your pip and setuptools Python modules are not up-to-date, then use the following command to upgrade these Python modules. If these Python modules are out-of-date then the commands which follow later in this section may fail.

py -m pip install --upgrade setuptools pip wheel

You should now be able to install the nvidia-pyindex module.

py -m pip install nvidia-pyindex

If your project is using a requirements.txt file, then you can add the following line to your requirements.txt file as an alternative to installing the nvidia-pyindex package:

--extra-index-url https://pypi.ngc.nvidia.com

Procedure

Install the CUDA runtime package:

py -m pip install nvidia-cuda-runtime-cu12

Optionally, install additional packages as listed below using the following command:

py -m pip install nvidia-

Metapackages

The following metapackages will install the latest version of the named component on Windows for the indicated CUDA version. “cu12” should be read as “cuda12”.

nvidia-cuda-runtime-cu12
nvidia-cuda-cupti-cu12
nvidia-cuda-nvcc-cu12
nvidia-nvml-dev-cu12
nvidia-cuda-nvrtc-cu12
nvidia-nvtx-cu12
nvidia-cuda-sanitizer-api-cu12
nvidia-cublas-cu12
nvidia-cufft-cu12
nvidia-curand-cu12
nvidia-cusolver-cu12
nvidia-cusparse-cu12
nvidia-npp-cu12
nvidia-nvjpeg-cu12
nvidia-opencl-cu12
nvidia-nvjitlink-cu12
nvidia-opencl-cu12
nvidia-nvjitlink-cu12

These metapackages install the following packages:

nvidia-nvml-dev-cu123
nvidia-cuda-nvcc-cu123
nvidia-cuda-runtime-cu123
nvidia-cuda-cupti-cu123
nvidia-cublas-cu123
nvidia-cuda-sanitizer-api-cu123
nvidia-nvtx-cu123
nvidia-cuda-nvrtc-cu123
nvidia-npp-cu123
nvidia-cusparse-cu123
nvidia-cusolver-cu123
nvidia-curand-cu123
nvidia-cufft-cu123
nvidia-nvjpeg-cu123
nvidia-opencl-cu123
nvidia-nvjitlink-cu123
nvidia-opencl-cu123
nvidia-nvjitlink-cu123

4. Compiling CUDA Programs

The project files in the CUDA Samples have been designed to provide simple, one-click builds of the programs that include all source code. To build the Windows projects (for release or debug mode), use the provided *.sln solution files for Microsoft Visual Studio 2015 (deprecated in CUDA 11.1), 2017, 2019, or 2022. You can use either the solution files located in each of the examples directories in https://github.com/nvidia/cuda-samples

4.1. Compiling Sample Projects

The bandwidthTest project is a good sample project to build and run. It is located in https://github.com/NVIDIA/cuda-samples/tree/master/Samples/1_Utilities/bandwidthTest.

If you elected to use the default installation location, the output is placed in CUDA Samples\v12.3\bin\win64\Release . Build the program using the appropriate solution file and run the executable. If all works correctly, the output should be similar to Figure 2.

4.2. Sample Projects

The sample projects come in two configurations: debug and release (where release contains no debugging information) and different Visual Studio projects.

A few of the example projects require some additional setup.

These sample projects also make use of the $CUDA_PATH environment variable to locate where the CUDA Toolkit and the associated .props files are.

The environment variable is set automatically using the Build Customization CUDA 12.3.props file, and is installed automatically as part of the CUDA Toolkit installation process.

Table 3 CUDA Visual Studio .props locations 

CUDA 12.3 .props file Install Directory

Visual Studio 2015 (deprecated)

C:Program Files (x86)\MSBuild\Microsoft.Cpp\v4.0\V140\BuildCustomizations

Visual Studio 2017

Visual Studio 2019

C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\MSBuild\Microsoft\VC\v160\BuildCustomizations

Visual Studio 2022

C:\Program Files\Microsoft Visual Studio\2022\Professional\MSBuild\Microsoft\VC\v170\BuildCustomizations

You can reference this CUDA 12.3.props file when building your own CUDA applications.

4.3. Build Customizations for New Projects

When creating a new CUDA application, the Visual Studio project file must be configured to include CUDA build customizations. To accomplish this, click File-> New | Project… NVIDIA-> CUDA->, then select a template for your CUDA Toolkit version. For example, selecting the “CUDA 12.3 Runtime” template will configure your project for use with the CUDA 12.3 Toolkit. The new project is technically a C++ project (.vcxproj) that is preconfigured to use NVIDIA’s Build Customizations. All standard capabilities of Visual Studio C++ projects will be available.

To specify a custom CUDA Toolkit location, under CUDA C/C++, select Common, and set the CUDA Toolkit Custom Dir field as desired. Note that the selected toolkit must match the version of the Build Customizations.

A supported version of MSVC must be installed to use this feature.

4.4. Build Customizations for Existing Projects

When adding CUDA acceleration to existing applications, the relevant Visual Studio project files must be updated to include CUDA build customizations. This can be done using one of the following two methods:

Open the Visual Studio project, right click on the project name, and select Build Dependencies > Build Customizations…, then select the CUDA Toolkit version you would like to target.
Alternatively, you can configure your project always to build with the most recently installed version of the CUDA Toolkit. First add a CUDA build customization to your project as above. Then, right click on the project name and select Properties. Under CUDA C/C++, select Common, and set the CUDA Toolkit Custom Dir field to $(CUDA_PATH) . Note that the $(CUDA_PATH) environment variable is set by the installer.

While Option 2 will allow your project to automatically use any new CUDA Toolkit version you may install in the future, selecting the toolkit version explicitly as in Option 1 is often better in practice, because if there are new CUDA configuration options added to the build customization rules accompanying the newer toolkit, you would not see those new options using Option 2.

If you use the $(CUDA_PATH) environment variable to target a version of the CUDA Toolkit for building, and you perform an installation or uninstallation of any version of the CUDA Toolkit, you should validate that the $(CUDA_PATH) environment variable points to the correct installation directory of the CUDA Toolkit for your purposes. You can access the value of the $(CUDA_PATH) environment variable via the following steps:

Open a run window from the Start Menu.
Run:

control sysdm.cpl

Files which contain CUDA code must be marked as a CUDA C/C++ file. This can done when adding the file by right clicking the project you wish to add the file to, selecting Add New Item, selecting NVIDIA CUDA 12.3\CodeCUDA C/C++ File, and then selecting the file you wish to add.

For advanced users, if you wish to try building your project against a newer CUDA Toolkit without making changes to any of your project files, go to the Visual Studio command prompt, change the current directory to the location of your project, and execute a command such as the following:

msbuild /t:Rebuild /p:CudaToolkitDir="drive:/path/to/new/toolkit/"

5. Additional Considerations

Now that you have CUDA-capable hardware and the NVIDIA CUDA Toolkit installed, you can examine and enjoy the numerous included programs. To begin using CUDA to accelerate the performance of your own applications, consult the CUDA C Programming Guide, located in the CUDA Toolkit documentation directory.

A number of helpful development tools are included in the CUDA Toolkit or are available for download from the NVIDIA Developer Zone to assist you as you develop your CUDA programs, such as NVIDIA ® Nsight™ Visual Studio Edition, and NVIDIA Visual Profiler.

For technical support on programming questions, consult and participate in the developer forums at https://developer.nvidia.com/cuda/.

6. Notices

6.1. Notice

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6.2. OpenCL

OpenCL is a trademark of Apple Inc. used under license to the Khronos Group Inc.

6.3. Trademarks

NVIDIA and the NVIDIA logo are trademarks or registered trademarks of NVIDIA Corporation in the U.S. and other countries. Other company and product names may be trademarks of the respective companies with which they are associated.

1. Introduction

CUDA was developed with several design goals in mind:

Provide a small set of extensions to standard programming languages, like C, that enable a straightforward implementation of parallel algorithms. With CUDA C/C++, programmers can focus on the task of parallelization of the algorithms rather than spending time on their implementation.
Support heterogeneous computation where applications use both the CPU and GPU. Serial portions of applications are run on the CPU, and parallel portions are offloaded to the GPU. As such, CUDA can be incrementally applied to existing applications. The CPU and GPU are treated as separate devices that have their own memory spaces. This configuration also allows simultaneous computation on the CPU and GPU without contention for memory resources.

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

1.1. System Requirements

To use CUDA on your system, you will need the following installed:

A CUDA-capable GPU
A supported version of Microsoft Windows
A supported version of Microsoft Visual Studio
The NVIDIA CUDA Toolkit (available at https://developer.nvidia.com/cuda-downloads)

The next two tables list the currently supported Windows operating systems and compilers.

Table 1. Windows Operating System Support in CUDA 12.1 

Cross (x86_32 on x86_64)

Windows Server 2022

Windows Server 2019

32-bit compilation native and cross-compilation is removed from CUDA 12.0 and later Toolkit. Use the CUDA Toolkit from earlier releases for 32-bit compilation. CUDA Driver will continue to support running existing 32-bit applications on existing GPUs except Hopper. Hopper does not support 32-bit applications. Ada will be the last architecture with driver support for 32-bit applications.

Table 2. Windows Compiler Support in CUDA 12.0 

MSVC Version 193x

Visual Studio 2022 17.0

MSVC Version 192x

Visual Studio 2019 16.x

MSVC Version 191x

Visual Studio 2017 15.x (RTW and all updates)

* Support for Visual Studio 2015 is deprecated in release 11.1.

Support for running x86 32-bit applications on x86_64 Windows is limited to use with:

GeForce GPUs
CUDA Driver
CUDA Runtime (cudart)
CUDA Math Library (math.h)

1.2. About This Document

2. Installing CUDA Development Tools

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

The setup of CUDA development tools on a system running the appropriate version of Windows consists of a few simple steps:

Verify the system has a CUDA-capable GPU.
Download the NVIDIA CUDA Toolkit.
Install the NVIDIA CUDA Toolkit.
Test that the installed software runs correctly and communicates with the hardware.

2.1. Verify You Have a CUDA-Capable GPU

The Windows Device Manager can be opened via the following steps:

Open a run window from the Start Menu
Run:

control /name Microsoft.DeviceManager

2.2. Download the NVIDIA CUDA Toolkit

The NVIDIA CUDA Toolkit is available at https://developer.nvidia.com/cuda-downloads. Choose the platform you are using and one of the following installer formats:

Network Installer: A minimal installer which later downloads packages required for installation. Only the packages selected during the selection phase of the installer are downloaded. This installer is useful for users who want to minimize download time.
Full Installer: An installer which contains all the components of the CUDA Toolkit and does not require any further download. This installer is useful for systems which lack network access and for enterprise deployment.

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

Download Verification

The download can be verified by comparing the MD5 checksum posted at https://developer.download.nvidia.com/compute/cuda/12.1.0/docs/sidebar/md5sum.txt with that of the downloaded file. If either of the checksums differ, the downloaded file is corrupt and needs to be downloaded again.

2.3. Install the CUDA Software

Before installing the toolkit, you should read the Release Notes, as they provide details on installation and software functionality.

The driver and toolkit must be installed for CUDA to function. If you have not installed a stand-alone driver, install the driver from the NVIDIA CUDA Toolkit.

The installation may fail if Windows Update starts after the installation has begun. Wait until Windows Update is complete and then try the installation again.

Graphical Installation

Install the CUDA Software by executing the CUDA installer and following the on-screen prompts.

Silent Installation

Table 3. Possible Subpackage Names 

Toolkit Subpackages (defaults to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.0)

CUDA Profiler API.

CUDA Runtime libraries.

Extracts information from cubin files.

The CUDA Profiling Tools Interface for creating profiling and tracing tools that target CUDA applications.

The CUDA cu++ filt demangler tool.

Prebuilt demo applications using CUDA.

CUDA HTML and PDF documentation files including the CUDA C++ Programming Guide, CUDA C++ Best Practices Guide, CUDA library documentation, etc.

Extracts information from standalone cubin files.

NVML development libraries and headers.

Tool for collecting and viewing CUDA application profiling data from the command-line.

Prunes host object files and libraries to only contain device code for the specified targets.

NVRTC runtime libraries.

NVTX on Windows.

Compute Sanitizer API.

cuBLAS runtime libraries.

cuFFT runtime libraries.

cuRAND runtime libraries.

cuSOLVER runtime libraries.

cuSPARSE runtime libraries.

NPP runtime libraries.

Installs the Nsight Visual Studio Edition plugin in all VS.

Installs CUDA project wizard and builds customization files in VS.

Installs the CUDA_Occupancy_Calculator.xls tool.

The NVIDIA Display Driver. Required to run CUDA applications.

For example, to install only the compiler and driver components:

.exe -s nvcc_12.1 Display.Driver

Use the -n option if you do not want to reboot automatically after install or uninstall, even if reboot is required.

Extracting and Inspecting the Files Manually

Accessing the files in this manner does not set up any environment settings, such as variables or Visual Studio integration. This is intended for enterprise-level deployment.

2.3.1. Uninstalling the CUDA Software

All subpackages can be uninstalled through the Windows Control Panel by using the Programs and Features widget.

2.4. Using Conda to Install the CUDA Software

This section describes the installation and configuration of CUDA when using the Conda installer. The Conda packages are available at https://anaconda.org/nvidia.

2.4.1. Conda Overview

The Conda installation installs the CUDA Toolkit. The installation steps are listed below.

2.4.2. Installation

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

conda install cuda -c nvidia

2.4.3. Uninstallation

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

conda remove cuda

2.4.4. Installing Previous CUDA Releases

All Conda packages released under a specific CUDA version are labeled with that release version. To install a previous version, include that label in the install command such as:

conda install cuda -c nvidia/label/cuda-11.3.0

conda install cuda -c nvidia/label/cuda-11.3.0 -c nvidia/label/cuda-11.3.1

This example will install all packages released as part of CUDA 11.3.0.

2.5. Use a Suitable Driver Model

On Windows 10 and later, the operating system provides two driver models under which the NVIDIA Driver may operate:

The WDDM driver model is used for display devices.
The Tesla Compute Cluster (TCC) mode of the NVIDIA Driver is available for non-display devices such as NVIDIA Tesla GPUs and the GeForce GTX Titan GPUs; it uses the Windows WDM driver model.

Keep in mind that when TCC mode is enabled for a particular GPU, that GPU cannot be used as a display device.

NVIDIA GeForce GPUs (excluding GeForce GTX Titan GPUs) do not support TCC mode.

2.6. Verify the Installation

2.6.1. Running the Compiled Examples

The version of the CUDA Toolkit can be checked by running nvcc -V in a Command Prompt window. You can display a Command Prompt window by going to:

Start > All Programs > Accessories > Command Prompt

CUDA Samples are located in https://github.com/nvidia/cuda-samples. To use the samples, clone the project, build the samples, and run them using the instructions on the Github page.

This assumes that you used the default installation directory structure. If CUDA is installed and configured correctly, the output should look similar to Figure 1.

Valid Results from deviceQuery CUDA Sample

If a CUDA-capable device and the CUDA Driver are installed but deviceQuery reports that no CUDA-capable devices are present, ensure the deivce and driver are properly installed.

Valid Results from bandwidthTest CUDA Sample

If the tests do not pass, make sure you do have a CUDA-capable NVIDIA GPU on your system and make sure it is properly installed.

To see a graphical representation of what CUDA can do, run the particles sample at

https://github.com/NVIDIA/cuda-samples/tree/master/Samples/particles

3. Pip Wheels

Prerequisites

py -m pip install --upgrade setuptools pip wheel

You should now be able to install the nvidia-pyindex module.

py -m pip install nvidia-pyindex

If your project is using a requirements.txt file, then you can add the following line to your requirements.txt file as an alternative to installing the nvidia-pyindex package:

--extra-index-url https://pypi.ngc.nvidia.com

Procedure

Install the CUDA runtime package:

py -m pip install nvidia-cuda-runtime-cu12

Optionally, install additional packages as listed below using the following command:

py -m pip install nvidia-

Metapackages

The following metapackages will install the latest version of the named component on Windows for the indicated CUDA version. “cu12” should be read as “cuda12”.

nvidia-cuda-runtime-cu12
nvidia-cuda-cupti-cu12
nvidia-cuda-nvcc-cu12
nvidia-nvml-dev-cu12
nvidia-cuda-nvrtc-cu12
nvidia-nvtx-cu12
nvidia-cuda-sanitizer-api-cu12
nvidia-cublas-cu12
nvidia-cufft-cu12
nvidia-curand-cu12
nvidia-cusolver-cu12
nvidia-cusparse-cu12
nvidia-npp-cu12
nvidia-nvjpeg-cu12
nvidia-opencl-cu12
nvidia-nvjitlink-cu12

These metapackages install the following packages:

nvidia-nvml-dev-cu121
nvidia-cuda-nvcc-cu121
nvidia-cuda-runtime-cu121
nvidia-cuda-cupti-cu121
nvidia-cublas-cu121
nvidia-cuda-sanitizer-api-cu121
nvidia-nvtx-cu121
nvidia-cuda-nvrtc-cu121
nvidia-npp-cu121
nvidia-cusparse-cu121
nvidia-cusolver-cu121
nvidia-curand-cu121
nvidia-cufft-cu121
nvidia-nvjpeg-cu121
nvidia-opencl-cu121
nvidia-nvjitlink-cu121

4. Compiling CUDA Programs

https://github.com/nvidia/cuda-samples

4.1. Compiling Sample Projects

The bandwidthTest project is a good sample project to build and run. It is located in https://github.com/NVIDIA/cuda-samples/tree/master/Samples/1_Utilities/bandwidthTest.

If you elected to use the default installation location, the output is placed in CUDA Samples\v12.0\bin\win64\Release . Build the program using the appropriate solution file and run the executable. If all works correctly, the output should be similar to Figure 2.

4.2. Sample Projects

The sample projects come in two configurations: debug and release (where release contains no debugging information) and different Visual Studio projects.

A few of the example projects require some additional setup.

These sample projects also make use of the $CUDA_PATH environment variable to locate where the CUDA Toolkit and the associated .props files are.

The environment variable is set automatically using the Build Customization CUDA 12.0.props file, and is installed automatically as part of the CUDA Toolkit installation process.

Table 4. CUDA Visual Studio .props locations 

CUDA 12.0 .props file Install Directory

Visual Studio 2015 (deprecated)

C:Program Files (x86)MSBuildMicrosoft.Cppv4.0V140BuildCustomizations

Visual Studio 2017

Visual Studio 2019

C:Program Files (x86)Microsoft Visual Studio2019ProfessionalMSBuildMicrosoftVCv160BuildCustomizations

Visual Studio 2022

C:Program FilesMicrosoft Visual Studio2022ProfessionalMSBuildMicrosoftVCv170BuildCustomizations

You can reference this CUDA 12.0.props file when building your own CUDA applications.

4.3. Build Customizations for New Projects

When creating a new CUDA application, the Visual Studio project file must be configured to include CUDA build customizations. To accomplish this, click File-> New | Project… NVIDIA-> CUDA->, then select a template for your CUDA Toolkit version. For example, selecting the “CUDA 12.0 Runtime” template will configure your project for use with the CUDA 12.0 Toolkit. The new project is technically a C++ project (.vcxproj) that is preconfigured to use NVIDIA’s Build Customizations. All standard capabilities of Visual Studio C++ projects will be available.

A supported version of MSVC must be installed to use this feature.

4.4. Build Customizations for Existing Projects

Open the Visual Studio project, right click on the project name, and select Build Dependencies > Build Customizations…, then select the CUDA Toolkit version you would like to target.
Alternatively, you can configure your project always to build with the most recently installed version of the CUDA Toolkit. First add a CUDA build customization to your project as above. Then, right click on the project name and select Properties. Under CUDA C/C++, select Common, and set the CUDA Toolkit Custom Dir field to $(CUDA_PATH) . Note that the $(CUDA_PATH) environment variable is set by the installer.

Open a run window from the Start Menu.
Run:

control sysdm.cpl

Files which contain CUDA code must be marked as a CUDA C/C++ file. This can done when adding the file by right clicking the project you wish to add the file to, selecting Add New Item, selecting NVIDIA CUDA 12.0\CodeCUDA C/C++ File, and then selecting the file you wish to add.

msbuild /t:Rebuild /p:CudaToolkitDir="drive:/path/to/new/toolkit/"

5. Additional Considerations

For technical support on programming questions, consult and participate in the developer forums at https://developer.nvidia.com/cuda/.

6. Notices

6.1. Notice

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.

6.2. OpenCL

OpenCL is a trademark of Apple Inc. used under license to the Khronos Group Inc.

6.3. Trademarks

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 Samples’ nbody directory in https://github.com/NVIDIA/cuda-samples/tree/master/Samples/5_Domain_Specific/nbody.
Open the nbody Visual Studio solution file for the version of Visual Studio you have installed, for example, nbody_vs2019.sln .
Open the Build menu within Visual Studio and click Build Solution.
Navigate to the CUDA Samples build directory and run the nbody sample.

Note Run samples by navigating to the executable’s location, otherwise it will fail to locate dependent resources.

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 Samples’ nbody directory in https://github.com/NVIDIA/cuda-samples/tree/master/Samples/5_Domain_Specific/nbody.
Open the nbody Visual Studio solution file for the version of Visual Studio you have installed.
Open the Build menu within Visual Studio and click Build Solution.
Navigate to the CUDA Samples build directory and run the nbody sample.

Note Run samples by navigating to the executable’s location, otherwise it will fail to locate dependent resources.

2.3. Pip Wheels — Windows

Prerequisites

py -m pip install --upgrade setuptools pip wheel

You should now be able to install the nvidia-pyindex module.

py -m pip install nvidia-pyindex

If your project is using a requirements.txt file, then you can add the following line to your requirements.txt file as an alternative to installing the nvidia-pyindex package:

--extra-index-url https://pypi.ngc.nvidia.com

Procedure

Install the CUDA runtime package:

py -m pip install nvidia-cuda-runtime-cu12

Optionally, install additional packages as listed below using the following command:

py -m pip install nvidia-

Metapackages

The following metapackages will install the latest version of the named component on Windows for the indicated CUDA version. “cu12” should be read as “cuda12”.

nvidia-cuda-runtime-cu12
nvidia-cuda-cupti-cu12
nvidia-cuda-nvcc-cu12
nvidia-nvml-dev-cu12
nvidia-cuda-nvrtc-cu12
nvidia-nvtx-cu12
nvidia-cuda-sanitizer-api-cu12
nvidia-cublas-cu12
nvidia-cufft-cu12
nvidia-curand-cu12
nvidia-cusolver-cu12
nvidia-cusparse-cu12
nvidia-npp-cu12
nvidia-nvjpeg-cu12

These metapackages install the following packages:

nvidia-nvml-dev-cu123
nvidia-cuda-nvcc-cu123
nvidia-cuda-runtime-cu123
nvidia-cuda-cupti-cu123
nvidia-cublas-cu123
nvidia-cuda-sanitizer-api-cu123
nvidia-nvtx-cu123
nvidia-cuda-nvrtc-cu123
nvidia-npp-cu123
nvidia-cusparse-cu123
nvidia-cusolver-cu123
nvidia-curand-cu123
nvidia-cufft-cu123
nvidia-nvjpeg-cu123

2.4. Conda

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

Installation

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

conda install cuda -c nvidia

Uninstallation

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

conda remove cuda

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:

subscription-manager repos --enable=rhel-7-workstation-optional-rpms

subscription-manager repos --enable=rhel-7-for-power-9-optional-rpms

subscription-manager repos --enable=rhel-7-server-optional-rpms

sudo rpm --install cuda-repo--..rpm sudo rpm --erase gpg-pubkey-7fa2af80* sudo yum clean expire-cache sudo yum install cuda

sudo reboot

export PATH=/usr/local/cuda-12.3/bin$> export LD_LIBRARY_PATH=/usr/local/cuda-12.3/lib64\ $>

Note Run samples by navigating to the executable’s location, otherwise it will fail to locate dependent resources.

3.1.1.2. Runfile Installer

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

blacklist nouveau options nouveau modeset=0

sudo dracut --force

sudo sh cuda__linux.run --silent

sudo nvidia-xconfig

sudo reboot

export PATH=/usr/local/cuda-12.3/bin$> export LD_LIBRARY_PATH=/usr/local/cuda-12.3/lib64\ $>

Note Run samples by navigating to the executable’s location, otherwise it will fail to locate dependent resources.

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.

su -c 'dnf install --nogpgcheck http://download1.rpmfusion.org/free/fedora/rpmfusion-free-release-$(rpm -E %fedora).noarch.rpm'

sudo rpm --install cuda-repo--..rpm sudo rpm --erase gpg-pubkey-7fa2af80* sudo dnf clean expire-cache sudo dnf install cuda

sudo reboot

export PATH=/usr/local/cuda-12.3/bin$> export LD_LIBRARY_PATH=/usr/local/cuda-12.3/lib64\ $>

Note Run samples by navigating to the executable’s location, otherwise it will fail to locate dependent resources.

3.1.2.2. Runfile Installer

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

blacklist nouveau options nouveau modeset=0

sudo dracut --force

sudo grub2-mkconfig -o /boot/grub2/grub.cfg

sudo reboot

sudo sh cuda__linux.run --silent

sudo nvidia-xconfig

export PATH=/usr/local/cuda-12.3/bin$> export LD_LIBRARY_PATH=/usr/local/cuda-12.3/lib64\ $>

Note Run samples by navigating to the executable’s location, otherwise it will fail to locate dependent resources.

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.

sudo rpm --install cuda-repo--..rpm sudo SUSEConnect --product PackageHub/15/x86_64 sudo zypper refresh sudo rpm --erase gpg-pubkey-7fa2af80* sudo dnf config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/$distro/$arch/cuda-$distro.repo sudo zypper install cuda

sudo usermod -a -G video

sudo reboot

export PATH=/usr/local/cuda-12.3/bin$> export LD_LIBRARY_PATH=/usr/local/cuda-12.3/lib64\ $>

Note Run samples by navigating to the executable’s location, otherwise it will fail to locate dependent resources.

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):

sudo sh cuda__linux.run --silent

sudo nvidia-xconfig

sudo reboot

export PATH=/usr/local/cuda-12.3/bin$> export LD_LIBRARY_PATH=/usr/local/cuda-12.3/lib64\ $>

Note Run samples by navigating to the executable’s location, otherwise it will fail to locate dependent resources.

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.

sudo rpm --install cuda-repo--..rpm sudo rpm --erase gpg-pubkey-7fa2af80* sudo zypper refresh sudo zypper install cuda

sudo usermod -a -G video

sudo reboot

export PATH=/usr/local/cuda-12.3/bin$> export LD_LIBRARY_PATH=/usr/local/cuda-12.3/lib64\ $>

Note Run samples by navigating to the executable’s location, otherwise it will fail to locate dependent resources.

3.1.4.2. Runfile Installer

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

blacklist nouveau options nouveau modeset=0

sudo /sbin/mkinitrd

sudo sh cuda__linux.run --silent

sudo nvidia-xconfig

sudo reboot

export PATH=/usr/local/cuda-12.3/bin$> export LD_LIBRARY_PATH=/usr/local/cuda-12.3/lib64\ $>

Note Run samples by navigating to the executable’s location, otherwise it will fail to locate dependent resources.

3.1.5. Pip Wheels — Linux

Prerequisites

python3 -m pip install --upgrade setuptools pip wheel

You should now be able to install the nvidia-pyindex module.

python3 -m pip install nvidia-pyindex

If your project is using a requirements.txt file, then you can add the following line to your requirements.txt file as an alternative to installing the nvidia-pyindex package:

--extra-index-url https://pypi.ngc.nvidia.com

Procedure

Install the CUDA runtime package:

python3 -m pip install nvidia-cuda-runtime-cu12

Optionally, install additional packages as listed below using the following command:

python3 -m pip install nvidia-

Metapackages

The following metapackages will install the latest version of the named component on Linux for the indicated CUDA version. “cu12” should be read as “cuda12”.

nvidia-cuda-runtime-cu12
nvidia-cuda-cupti-cu12
nvidia-cuda-nvcc-cu12
nvidia-nvml-dev-cu12
nvidia-cuda-nvrtc-cu12
nvidia-nvtx-cu12
nvidia-cuda-sanitizer-api-cu12
nvidia-cublas-cu12
nvidia-cufft-cu12
nvidia-curand-cu12
nvidia-cusolver-cu12
nvidia-cusparse-cu12
nvidia-npp-cu12
nvidia-nvjpeg-cu12
nvidia-opencl-cu12
nvidia-nvjitlink-cu12

These metapackages install the following packages:

nvidia-nvml-dev-cu123
nvidia-cuda-nvcc-cu123
nvidia-cuda-runtime-cu123
nvidia-cuda-cupti-cu123
nvidia-cublas-cu123
nvidia-cuda-sanitizer-api-cu123
nvidia-nvtx-cu123
nvidia-cuda-nvrtc-cu123
nvidia-npp-cu123
nvidia-cusparse-cu123
nvidia-cusolver-cu123
nvidia-curand-cu123
nvidia-cufft-cu123
nvidia-nvjpeg-cu123
nvidia-opencl-cu123
nvidia-nvjitlink-cu123

3.1.6. Conda

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

Installation

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

conda install cuda -c nvidia

Uninstallation

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

conda remove cuda

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.

sudo dpkg -i cuda-repo-__.deb

Update the CUDA public GPG key

sudo apt-key del 7fa2af80

When installing using the local repo:

sudo cp /var/cuda-repo-ubuntu2004-12-0-local/cuda-*-keyring.gpg /usr/share/keyrings/

When installing using the network repo:

wget https://developer.download.nvidia.com/compute/cuda/repos/wsl-ubuntu/x86_64/cuda-keyring_1.0-1_all.deb sudo dpkg -i cuda-keyring_1.0-1_all.deb

Pin file to prioritize CUDA repository:

wget https://developer.download.nvidia.com/compute/cuda/repos///cuda-.pin sudo mv cuda-.pin /etc/apt/preferences.d/cuda-repository-pin-600

sudo apt-get update sudo apt-get 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.

sudo dpkg --install cuda-repo--..deb sudo apt-key del 7fa2af80 wget https://developer.download.nvidia.com/compute/cuda/repos/$distro/$arch/cuda-keyring_1.0-1_all.deb sudo dpkg -i cuda-keyring_1.0-1_all.deb sudo add-apt-repository contrib sudo apt-get update sudo apt-get -y install cuda

sudo reboot

export PATH=/usr/local/cuda-12.3/bin$> export LD_LIBRARY_PATH=/usr/local/cuda-12.3/lib64\ $>

Note Run samples by navigating to the executable’s location, otherwise it will fail to locate dependent resources.

3.1.8.2. Runfile Installer

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

blacklist nouveau options nouveau modeset=0

sudo update-initramfs -u

sudo sh cuda__linux.run --silent

sudo nvidia-xconfig

sudo reboot

export PATH=/usr/local/cuda-12.3/bin$> export LD_LIBRARY_PATH=/usr/local/cuda-12.3/lib64\ $>

Note Run samples by navigating to the executable’s location, otherwise it will fail to locate dependent resources.

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.

sudo dpkg -i cuda-repo-__.deb sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/debian10/x86_64/7fa2af80.pub sudo apt-key del 7fa2af80 wget https://developer.download.nvidia.com/compute/cuda/repos/$distro/$arch/cuda-keyring_1.0-1_all.deb sudo dpkg -i cuda-keyring_1.0-1_all.deb sudo add-apt-repository contrib sudo apt-get update sudo apt-get -y install cuda

sudo reboot

export PATH=/usr/local/cuda-12.3/bin$> export LD_LIBRARY_PATH=/usr/local/cuda-12.3/lib64\ $>

Note Run samples by navigating to the executable’s location, otherwise it will fail to locate dependent resources.

3.1.9.2. Runfile Installer

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

blacklist nouveau options nouveau modeset=0

sudo update-initramfs -u

sudo sh cuda__linux.run --silent

sudo nvidia-xconfig

sudo reboot

export PATH=/usr/local/cuda-12.3/bin$> export LD_LIBRARY_PATH=/usr/local/cuda-12.3/lib64\ $>

Note Run samples by navigating to the executable’s location, otherwise it will fail to locate dependent resources.

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.

sudo dpkg --install cuda-repo--..deb sudo apt-key del 7fa2af80 wget https://developer.download.nvidia.com/compute/cuda/repos/$distro/$arch/cuda-keyring_1.0-1_all.deb sudo dpkg -i cuda-keyring_1.0-1_all.deb sudo add-apt-repository contrib sudo apt-get update sudo apt-get -y install cuda

export PATH=/usr/local/cuda-12.3/bin$> export LD_LIBRARY_PATH=/usr/local/cuda-12.3/lib64\ $>

Note Run samples by navigating to the executable’s location, otherwise it will fail to locate dependent resources.

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:

sudo rpm --install cuda-repo--..rpm sudo yum clean expire-cache sudo yum install cuda

export PATH=/usr/local/cuda-12.3/bin$> export LD_LIBRARY_PATH=/usr/local/cuda-12.3/lib64\ $>

Note Run samples by navigating to the executable’s location, otherwise it will fail to locate dependent resources.

3.2.3. Conda

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

Installation

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

conda install cuda -c nvidia

Uninstallation

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

conda remove cuda

4. Notices

4.1. Notice

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.

4.2. OpenCL

OpenCL is a trademark of Apple Inc. used under license to the Khronos Group Inc.