#Nvidia cuda toolkit windows download install#
#Nvidia cuda toolkit windows download drivers#
#Nvidia cuda toolkit windows download upgrade#

So after installation of CUDA Toolkit, we need to verify that the CUDA software platform is able to talk with the GPU hardware. CUDA is a software layer that gives direct access to the GPU’s virtual instruction set and parallel computational elements, for the execution of compute kernels.

#Nvidia cuda toolkit windows download upgrade#

I am using the 11.2.0 version and I think it should be fine for 11.2.1 and 11.2.2 as they are minor upgrade to the 11.2 version.Īnd the file is huge (2.8GB), so take a break.Īccording to Wikipedia, CUDA (or Compute Unified Device Architecture) is a parallel computing platform and application programming interface (API) that allows software to use certain types of graphics processing unit (GPU) for general purpose processing – an approach called general-purpose computing on GPUs (GPGPU). As of Dec 2021, TensorFlow only supports CUDA 11.2 or older version. Don’t click “Download Latest CUDA Toolkit” as it typically will not give the right version for your system. It is a must that you download the version which supports your Python and TensorFlow versions. Navigate to the CUDA supporting website and download the CUDA Toolkit for your system.

#Nvidia cuda toolkit windows download install#

After download the driver file, just follow the screen instructions to install the driver for your card. The file size is more than 500MB, so it may take a few seconds. Go to NVIDIA’s download website and download the driver for your video card. You may not use it but it comes with the C++ compiler which is required to drive the GPU support. Visual Studio is a very powerful Integrated Development Environment (IDE) for programmers. Just go to the Microsoft website and download the Visual Studio 2019 or newer version (as of Dec 2021, it is version 2022).

Install the Microsoft Visual C++ (MSVC) compiler

the Microsoft Visual C++ (MSVC) compiler.

We need to install four software and a few checks to make GPU work on Windows. Take note of the version numbers as we need to use them later. Check here for the version numbers.Īs my TensorFlow is 2.7.0, the corresponding CUDA and cuDNN versions are 11.2 and 8.1, respectively. The versions of CUDA and cuDNN must match with the version of TensorFlow, otherwise it won’t work.

#Nvidia cuda toolkit windows download drivers#

To drive the card, we need to install the CUDA drivers and the cuDNN libraries. GPU support requires a CUDA®-enabled card. Check the required CUDA and cuDNN version Type python -c 'import tensorflow as tf print(tf._version_)' in your command shell and it should output the version number if you have installed the TensorFlow. My Python version is from Anaconda which is 3.8.5. As of Dec 2021, Tensorflow 2 supports Python 3.7 - 3.9.

In your command shell (Anaconda Powershell if you use Anaconda for Python), type python -version to show the version of your installed Python. You may also check the list of CUDA®-enabled GPU card here. My card is an entry level card: NVIDIA Quadro P1000 with 4GB dedicated GPU memory. To check your computer’s video card info, just go to Task Manager by clicking Ctrl + Alt + Del. Before the installation, we need to check the following things: Check your CUDA®-enabled GPU card info