Nvidia cuda drivers for windows

ПРОГРАММИРОВАНИЕ:

Как установить драйвер NVIDIA CUDA, CUDA Toolkit, CuDNN и TensorRT в Windows

Хорошие и простые руководства с пошаговыми инструкциями

Резюме:

В этой статье устанавливаются драйверы и программы, необходимые для использования графических процессоров NVIDIA для обучения моделей и выполнения пакетных выводов. Он загружает и устанавливает драйверы CUDA, CUDA Toolkits и обновления CUDA Toolkit. Он загружает, распаковывает и перемещает файлы CuDNN и TensorRT в каталог CUDA. Он также настраивает, создает и запускает образец BlackScholes для тестирования графического процессора.

Оглавление:

1. Установить требования
2. Установить драйвер CUDA
3. Установить CUDA Toolkit 10
4. Установить CUDA Toolkit 11
5. Установить библиотеку CuDNN
6. Установить библиотеку TensorRT
7. Протестируйте GPU на образце CUDA

Приложение:

1. Учебники: настройка искусственного интеллекта
2. Учебники: курс искусственного интеллекта
3. Учебники: репозитории искусственного интеллекта

Установите требования:

В этом разделе загружается и устанавливается Visual Studio с поддержкой C и C ++.

# open the powershell shell
1. press “⊞ windows”
2. enter “powershell” into the search bar
3. right-click "windows powershell"
4. click “run as administrator”
# download the visual studio 2019 installer
invoke-webrequest -outfile "$home\downloads\vsc.exe" -uri https://download.visualstudio.microsoft.com/download/pr/45dfa82b-c1f8-4c27-a5a0-1fa7a864ae21/9dd77a8d1121fd4382494e40840faeba0d7339a594a1603f0573d0013b0f0fa5/vs_Community.exe
# open the visual studio 2019 installer
invoke-item "$home\downloads\vsc.exe"
# install visual studio 2019
1. check “desktop development with c++”
2. click "install"

Установите драйвер CUDA:

В этом разделе загружается и устанавливается последняя версия драйвера CUDA на тот момент.

# download the cuda driver installer
invoke-webrequest -outfile "$home\downloads\cuda_driver.exe" -uri https://us.download.nvidia.com/Windows/471.68/471.68-desktop-win10-win11-64bit-international-nsd-dch-whql.exe
# open the cuda driver installer
invoke-item "$home\downloads\cuda_driver.exe"
# install the cuda driver
1. select “nvidia graphics driver”
2. click "agree & continue"
3. click "next"

Установите CUDA Toolkit 10:

В этом разделе загружается и устанавливается CUDA Toolkit 10 и обновления.

# download the cuda toolkit 10 installer
invoke-webrequest -outfile "$home\downloads\cuda_toolkit_10.exe" https://developer.download.nvidia.com/compute/cuda/10.2/Prod/network_installers/cuda_10.2.89_win10_network.exe
# open the cuda toolkit 10 installer
invoke-item "$home\downloads\cuda_toolkit_10.exe"
# install cuda toolkit 10
1. click "agree & continue"
2. click "next"
3. select custom (advanced)
4. click "next"
5. uncheck “nvidia geforce experience components”
6. uncheck “driver components”
7. uncheck “other components”
8. click "next"
# download the cuda 10 update 1installer
invoke-webrequest -outfile "$home\downloads\cuda_10_update_1.exe" https://developer.download.nvidia.com/compute/cuda/10.2/Prod/patches/1/cuda_10.2.1_win10.exe
# open the cuda 10 update 1 installer
invoke-item "$home\downloads\cuda_10_update_1.exe"
# install the cuda 10 update 1
1. click "agree & continue"
2. click "next"
# download the cuda 10 update 2 installer
invoke-webrequest -outfile "$home\downloads\cuda_10_update_2.exe" https://developer.download.nvidia.com/compute/cuda/10.2/Prod/patches/2/cuda_10.2.2_win10.exe
# open the cuda 10 update 2 installer
invoke-item "$home\downloads\cuda_10_update_2.exe"
# install the cuda 10 update 2
1. click "agree & continue"
2. click "next"

Установите CUDA Toolkit 11:

В этом разделе загружается и устанавливается CUDA Toolkit 11.

# download the cuda toolkit 11 installer
invoke-webrequest -outfile "$home\downloads\cuda_toolkit_11.exe" https://developer.download.nvidia.com/compute/cuda/11.4.1/network_installers/cuda_11.4.1_win10_network.exe
# open the cuda toolkit 11 installer
invoke-item "$home\downloads\cuda_toolkit_11.exe"
# install cuda toolkit 11
1. click "agree & continue"
2. click "next"
3. select custom (advanced)
4. click "next"
5. uncheck “nvidia geforce experience components”
6. uncheck “driver components”
7. uncheck “other components”
8. click "next"

Установите библиотеку CuDNN:

Этот раздел присоединяется к Программе разработчика NVIDIA и загружает библиотеку CuDNN, распаковывает и перемещает файлы в каталог CUDA.

# join the nvidia developer program
start-process iexplore "https://developer.nvidia.com/developer-program"
# download the cudnn library for cuda toolkit 10 
start-process iexplore https://developer.nvidia.com/compute/machine-learning/cudnn/secure/8.2.2/10.2_07062021/cudnn-10.2-windows10-x64-v8.2.2.26.zip
# unzip the cudnn library for cuda toolkit 10
expand-archive "$home\downloads\cudnn-10.2-windows10-x64-v8.2.2.26.zip" -destinationpath "$home\downloads\cudnn_cuda_toolkit_10\"
# move the dll files
move-item "$home\downloads\cudnn_cuda_toolkit_10\cuda\bin\cudnn*.dll" "c:\program files\nvidia gpu computing toolkit\cuda\v10.2\bin\"
# move the h files
move-item "$home\downloads\cudnn_cuda_toolkit_10\cuda\include\cudnn*.h" "c:\program files\nvidia gpu computing toolkit\cuda\v10.2\include\"
# move the lib files
move-item "$home\downloads\cudnn_cuda_toolkit_10\cuda\lib\x64\cudnn*.lib" "c:\program files\nvidia gpu computing toolkit\cuda\v10.2\lib\x64"
# download the cudnn library for cuda toolkit 11
start-process iexplore https://developer.nvidia.com/compute/machine-learning/cudnn/secure/8.2.2/11.4_07062021/cudnn-11.4-windows-x64-v8.2.2.26.zip
# unzip the cudnn library for cuda toolkit 11
expand-archive "$home\downloads\cudnn-11.4-windows-x64-v8.2.2.26.zip" -destinationpath "$home\downloads\cudnn_cuda_toolkit_11\"
# move the dll files
move-item "$home\downloads\cudnn_cuda_toolkit_11\cuda\bin\cudnn*.dll" "c:\program files\nvidia gpu computing toolkit\cuda\v11.4\bin\"
# move the h files
move-item "$home\downloads\cudnn_cuda_toolkit_11\cuda\include\cudnn*.h" "c:\program files\nvidia gpu computing toolkit\cuda\v11.4\include\"
# move the lib files
move-item "$home\downloads\cudnn_cuda_toolkit_11\cuda\lib\x64\cudnn*.lib" "c:\program files\nvidia gpu computing toolkit\cuda\v11.4\lib\x64"

Установите библиотеку TensorRT:

Этот раздел загружает библиотеку TensorRT, распаковывает и перемещает файлы в каталог CUDA и устанавливает несколько необходимых программ на Python.

# download the tensorrt library for cuda toolkit 10
start-process iexplore https://developer.nvidia.com/compute/machine-learning/tensorrt/secure/8.0.1/zip/tensorrt-8.0.1.6.windows10.x86_64.cuda-10.2.cudnn8.2.zip
# unzip the tensorrt library for cuda 10
expand-archive "$home\downloads\tensorrt-8.0.1.6.windows10.x86_64.cuda-10.2.cudnn8.2.zip" "$home\downloads\tensorrt_cuda_toolkit_10\"
# move the dll files
move-item "$home\downloads\tensorrt_cuda_toolkit_10\tensorrt-8.0.1.6\lib\*.dll" "c:\program files\nvidia gpu computing toolkit\cuda\v10.2\bin\"
# download the tensorrt library for cuda toolkit 11
start-process iexplore https://developer.nvidia.com/compute/machine-learning/tensorrt/secure/8.0.1/zip/tensorrt-8.0.1.6.windows10.x86_64.cuda-11.3.cudnn8.2.zip
# unzip the tensorrt library for cuda 11
expand-archive "$home\downloads\tensorrt-8.0.1.6.windows10.x86_64.cuda-11.3.cudnn8.2.zip" "$home\downloads\tensorrt_cuda_toolkit_11\"
# move the dll files
move-item "$home\downloads\tensorrt_cuda_toolkit_11\tensorrt-8.0.1.6\lib\*.dll" "c:\program files\nvidia gpu computing toolkit\cuda\v11.4\bin\"
# install graph surgeon
python -m pip install "$home\downloads\tensorrt_cuda_toolkit_11\tensorrt-8.0.1.6\graphsurgeon\graphsurgeon-0.4.5-py2.py3-none-any.whl"
# install onnx graph surgeon
python -m pip install "$home\downloads\tensorrt_cuda_toolkit_11\tensorrt-8.0.1.6\onnx_graphsurgeon\onnx_graphsurgeon-0.3.10-py2.py3-none-any.whl"
# install universal framework format
python -m pip install "$home\downloads\tensorrt_cuda_toolkit_11\tensorrt-8.0.1.6\uff\uff-0.6.9-py2.py3-none-any.whl"

Протестируйте графический процессор на примере CUDA:

В этом разделе настраивается, строится и запускается образец BlackScholes.

# open the visual studio file
start-process "c:\programdata\nvidia corporation\cuda samples\v11.4\4_finance\blackscholes\blackscholes_vs2019.sln"
# edit the linker input properties
1. click the "project" menu
2. click "properties"
3. double-click "linker"
4. click "input"
5. click "additional dependencies"
6. click the "down arrow" button
7. click "edit"
# add the cudnn library
1. type "cudnn.lib" at the bottom of the additional dependencies
2. click "ok"
# add the cuda toolkit 11 directory
1. click "cuda c/c++"
2. double-click "cuda toolkit custom dir"
3. enter "c:\program files\nvidia gpu computing toolkit\cuda\v11.4"
4. click "ok"
# build the sample
1. click the “build” menu
2. click “build solution”
# run the sample
cmd /k "c:\programdata\nvidia corporation\cuda samples\v11.4\bin\win64\debug\blackscholes.exe"

«Наконец, не забудьте подписаться и удерживать кнопку хлопка, чтобы получать регулярные обновления и помощь».

Приложение:

Этот блог существует, чтобы предоставить комплексные решения, ответить на ваши вопросы и ускорить ваш прогресс в области искусственного интеллекта. В нем есть все необходимое, чтобы настроить компьютер и пройти первую половину курса fastai. Он откроет вам самые современные репозитории в подполях искусственного интеллекта. Он также будет охватывать вторую половину курса фастая.

Учебники: настройка искусственного интеллекта

В этом разделе есть все, что нужно для настройки вашего компьютера.

# linux
01. install and manage multiple python versions
02. install the nvidia cuda driver, toolkit, cudnn, and tensorrt
03. install the jupyter notebook server
04. install virtual environments in jupyter notebook
05. install the python environment for ai and machine learning
06. install the fastai course requirements
# wsl 2
01. install windows subsystem for linux 2
02. install and manage multiple python versions
03. install the nvidia cuda driver, toolkit, cudnn, and tensorrt 
04. install the jupyter notebook home and public server
05. install virtual environments in jupyter notebook
06. install the python environment for ai and machine learning
07. install ubuntu desktop with a graphical user interface
08. install the fastai course requirements
# windows 10
01. install and manage multiple python versions
02. install the nvidia cuda driver, toolkit, cudnn, and tensorrt
03. install the jupyter notebook home and public server
04. install virtual environments in jupyter notebook
05. install the programming environment for ai and machine learning
# mac
01. install and manage multiple python versions
02. install the jupyter notebook server
03. install virtual environments in jupyter notebook
04. install the python environment for ai and machine learning
05. install the fastai course requirements

Учебники: курс искусственного интеллекта

Этот раздел содержит ответы на анкету в конце каждого урока.

# fastai course
01. chapter 1: your deep learning journey q&a
02. chapter 2: from model to production q&a
03. chapter 3: data ethics q&a
04. chapter 4: under the hood: training a digit classifier q&a
05. chapter 5: image classification q&a
06. chapter 6: other computer vision problems q&a
07. chapter 7: training a state-of-the-art model q&a
08. chapter 8: collaborative filtering deep dive q&a

Учебники: репозитории искусственного интеллекта

Этот раздел содержит современные репозитории в различных подполях.

# repositories related to audio
01. raise audio quality using nu-wave
02. change voices using maskcyclegan-vc
03. clone voices using real-time-voice-cloning toolbox
# repositories related to images
01. achieve 90% accuracy using facedetection-dsfd

Nvidia Cuda Driver is essential software to communicate your CUDA-enabled GPU (Graphics Card) and Operating System installed in your computer. If you are looking for driver for CUDA-enabled GPU, then you are in right place. Here, we are going to discuss about Nvidia CUDA, and how to download and install its driver in your machine.

What is Nvidia CUDA?

CUDA stands for ‘Compute Unified Device Architecture’. It is parallel computing platform and application programming interface (API) model designed & developed by Nvidia Company. It offers software developers and software engineers to use a CUDA-enabled GPU for general purpose processing.

CUDA platform offers developers direct access to GPU’s Virtual computer architecture and parallel computational components to run computer kernels. This platform is designed to work with some programming language including C, C++ and FORTRAN. Nvidia CUDA powered GPUs also supports programming framework like OpenACC and OpenCL and HIP by compiling (translating) such code to CUDA.

Nvidia CUDA-Enabled GPUs can run thousands of Computing Threads

CUDA-enabled GPUs (Graphics Processing Units) have hundreds of cores that can execute thousands of computing tasks or threads simultaneously. These cores have shared resources such as a register file and shared memory which offers parallel tasks executing on these cores to transfer data without transmitting it over System memory bus.

System Requirements for Nvidia CUDA

If you want to use Nvidia CUDA on your Windows device, then you should make sure that you have CUDA-capable GPU, a supported version of Microsoft Windows, a supported version of Microsoft Visual Studio and Nvidia CUDA Toolkit all are installed properly.

CUDA Toolkit installer package includes Nvidia Cuda Driver which support the application development for all CUDA-capable GPUs supported by CUDA Toolkit. For example, if you are deploying applications Nvidia Tesla products in a server or cluster environment, you should make sure latest Tesla driver is installed in your device.

Installer package include the CUDA Toolkit, CUDA samples, CUDA Tools such as Nsight Visual Studio Edition for windows, and Nsight Eclipse Edition for Linux and MacOS and developers drivers. The installer package will also give an option to install the included driver. If you select the option to install Nvidia Cuda Driver, this replaces the driver with currently installed driver in your computer.

How to download and install Nvidia Cuda Driver on Windows 10?

CUDA Drivers are included in Nvidia CUDA Toolkit so you need to download and install Nvidia CUDA Toolkit, and Nvidia drivers will automatically installed with this installer.

Step 1: At first, you need to visit ‘Nvidia CUDA Downloads Page’ (Official)

Step 2: Depending Operating System version and architecture, download the latest CUDA Toolkit in your computer like ‘CUDA Toolkit 11.2’

Step 3: Double-click on ‘Setup file’ or ‘Downloaded file’ and follow on-screen instructions to finish installation. This way, the driver and toolkit will be installed in your computer for CUDA to function.

Step 4: Once done, reboot your device to see the changes.

How to Download or Reinstall Nvidia Cuda Driver on Windows 10 using ‘Windows Update’?

The installation of Nvidia CUDA Toolkit and CUDA driver may fails if Windows update starts after the installation has begun. Wait to complete Windows Update process and try installation again using above method. Or you can get the Nvidia Cuda Driver update using ‘Windows Update’.

Step 1: Press ‘Windows + I’ keys together from keyboard to open ‘Settings’ App

Step 2: In the opened ‘Settings’ App, go to ‘Update & Security > Windows Update’

Step 3: Click on ‘Check for updates’ button under ‘Windows update’ section. This way, you can download the latest Nvidia Cuda Driver or CUDA Driver update in your computer.

Step 4: Once done, restart your computer to see the changes.

Conclusion

I am sure this post helped you on How to download and install Nvidia Cuda Driver update, CUDA Toolkit in your computer. You can read & follow our instructions to do so. If this post really helped you, then you can share this post with others to help them.

CUDA Install Guide

This is a must-read guide if you want to setup a new Deep Learning PC. This guide includes the installation of the following:

• NVIDIA Driver
• CUDA Toolkit
• cuDNN
• TensorRT

Recommendation

Debian installation method is recommended for all CUDA toolkit, cuDNN and TensorRT installation.

For PyTorch, CUDA 11.0 and CUDA 10.2 are recommended.

For TensorFlow, up to CUDA 10.2 are supported.

TensorRT is still not supported for Ubuntu 20.04. So, Ubuntu 18.04 is recommended

Install NVIDIA Driver

Windows

Windows Update automatically install and update NVIDIA Driver.

Linux

Update first:

sudo apt update
sudo apt upgrade

Check latest and recommended drivers:

sudo ubuntu-drivers devices

Install recommended driver automatically:

sudo ubuntu-drivers install

Or, Install specific driver version using:

sudo apt install nvidia-driver-xxx

Then reboot:

Verify the Installation

After reboot, verify using:

Install CUDA Toolkit

Installation Steps

1. Go to https://developer.nvidia.com/cuda-toolkit-archive and choose your desire CUDA toolkit version that is compatible with the framework you want to use.
2. Select your OS.
3. Select your system architecture.
4. Select your OS version.
5. Select Installer Type and Follow the steps provided. (.exe on Windows and .run or .deb on Linux)

Post-Installation Actions

Windows exe CUDA Toolkit installation method automatically adds CUDA Toolkit specific Environment variables. You can skip the following section.

Before CUDA Toolkit can be used on a Linux system, you need to add CUDA Toolkit path to PATH variable.

Open a terminal and run the following command.

export PATH=/usr/local/cuda-11.1/bin${PATH:+:${PATH}}

or add this line to .bashrc file.

In addition, when using the runfile installation method, you also need to add LD_LIBRARY_PATH variable.

For 64-bit system,

export LD_LIBRARY_PATH=/usr/local/cuda-11.1/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

For 32-bit system,

export LD_LIBRARY_PATH=/usr/local/cuda-11.1/lib${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

Note: The above paths change when using a custom install path with the runfile installation method.

Verify the Installation

Check the CUDA Toolkit version with:

Install cuDNN

The NVIDIA CUDA Deep Neural Network library (cuDNN) is a GPU-accelerated lirbary of primitives for deep neural networks. cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization and activation layers.

1. Go to https://developer.nvidia.com/cudnn and click «Download cuDNN».
2. You need to sing in to proceed.
3. Then, check «I Agree to the Terms…».
4. Click on your desire cuDNN version compatible with your installed CUDA version. (If you don’t find desire cuDNN version, click on «Archived cuDNN Releases» and find your version. If you don’t know which version to install, latest cuDNN version is recommended).

Windows

1. Choose «cuDNN Library for Windows (x86)» and download. (That is the only one available for Windows).

2. Extract the downloaded zip file to a directory of your choice.

3. Copy the following files into the CUDA Toolkit directory.

   a. Copy <extractpath>\cuda\bin\cudnn*.dll to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\vx.x\bin.

   b. Copy <extractpath>\cuda\include\cudnn*.h to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\vx.x\include.

   c. Copy <extractpath>\cuda\lib\x64\cudnn*.lib to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\vx.x\lib\x64.

Linux

Download the 2 files named as:

1. cuDNN Runtime Library for …
2. cuDNN Developer Library for …

for your installed OS version.

Then, install the downloaded files with the following command:

sudo dpkg -i libcudnn8_x.x.x...deb
sudo dpkg -i libcudnn8-dev_x.x.x...deb

Install TensorRT

TensorRT is meant for high-performance inference on NVIDIA GPUs. TensorRT takes a trained network, which consists of a network definition and a set of trained parameters, and produces a highly optimized runtime engine that performs inference for that network.

1. Go to https://developer.nvidia.com/tensorrt and click «Download Now».
2. You need to sing in to proceed.
3. Click on your desire TensorRT version. (If you don’t know which version to install, latest TensorRT version is recommended).
4. Then, check «I Agree to the Terms…».
5. Click on your desire TensorRT sub-version. (If you don’t know which version to install, latest version is recommended).

Windows

1. Download «TensorRT 7.x.x for Windows10 and CUDA xx.x ZIP package» that matches CUDA version.
2. Unzip the downloaded archive.
3. Copy the DLL files from <extractpath>/lib to your CUDA installation directory C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\vx.x\bin

Then install the uff, graphsurgeon and onnx_graphsurgeon wheel packages.

pip install <extractpath>\graphsurgeon\graphsurgeon-x.x.x-py2.py3-none-any.whl
pip install <extractpath>\uff\uff-x.x.x-py2.py3-none-any.whl
pip install <extractpath>\onnx_graphsurgeon\onnx_graphsurgeon-x.x.x-py2.py3-none-any.whl

Linux

Download «TensorRT 7.x.x for Ubuntu xx.04 and CUDA xx.x DEB local repo package» that matches your OS version, CUDA version and CPU architecture.

Then install with:

os="ubuntuxx04"
tag="cudax.x-trt7.x.x.x-ga-yyyymmdd"

sudo dpkg -i nv-tensorrt-repo-${os}-${tag}_1-1_amd64.deb
sudo apt-key add /var/nv-tensorrt-repo-${tag}/7fa2af80.pub

sudo apt update
sudo apt install -y tensorrt

If you plan to use TensorRT with TensorFlow, install this also:

sudo apt install uff-converter-tf

Verify the Installation

For Linux,

You should see packages related with TensorRT.

Upgrading TensorRT

Download and install the new version as if you didn’t install before. You don’t need to uninstall your previous version.

Uninstalling TensorRT

sudo apt purge "libvinfer*"
sudo apt purge graphsurgeon-tf onnx-graphsurgeon
sudo apt autoremove
sudo pip3 uninstall tensorrt
sudo pip3 uninstall uff
sudo pip3 uninstall graphsurgeon
sudo pip3 uninstall onnx-graphsurgeon

PyCUDA

PyCUDA is used within Python wrappers to access NVIDIA’s CUDA APIs.

Install PyCUDA with:

If you want to upgrade PyCUDA for newest CUDA version or if you change the CUDA version, you need to uninstall and reinstall PyCUDA.

For that purpose, do the following:

1. Uninstall the existing PyCUDA.
2. Upgrade CUDA.
3. Install PyCUDA again.

References

• Official CUDA Toolkit Installation
• Official cuDNN Installation
• Official TensorRT Installation