Installing CUDA and cuDNN on Windows. This is an how-to guide for someone who is trying to figure our, how to install CUDA and cuDNN on windows to be used with tensorflow.
Libraries like Tensorflow and OpenCV are optimized for working with GPU. For these libraries to communicate with GPU we install CUDA and cuDNN, provided GPU is CUDA compatible. There are different versions of CUDA depending upon the architecture and model of GPU.
So, during the installation of CUDA, we need to first find its suitable version which is compatible with our machine’s GPU.
Cuda capable GPUs and their versions can be found here: (https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html)
According to your TensorFlow version check for the suitable Cuda version from this site: (https://www.tensorflow.org/install/gpu). In My case, Cuda version is 10.1
Now according to your Cuda version find a suitable cuDNN version and download through this link: (https://developer.nvidia.com/rdp/cudnn-archive). In My case, cuDNN version is 7.6.5
The Easy-Peasy Tensorflow-GPU Installation(Tensorflow 2.1, CUDA 11.0, and cuDNN) on Windows 10. The simplest way to install Tensorflow GPU on Windows 10.
In this article I am installing CUDA 11 in Ubuntu 20.04. My GPU is NVIDIA GT 730. Linux kernerl v 5.4.0–42-generic. gcc (Ubuntu 9.3.0–10ubuntu2) 9.3.0. Installing Tensorflow with CUDA & cuDNN GPU support on Ubuntu 20.04
It has been a while since I wrote on this platform, as if I had almost forgotten about it. But here I am again, and I have a bunch of long written posts that I will be rolling out at shorter intervals, and not just installation stuff, I promise
If you working with Machine Learning using GPU this story is the answer.
I am installing drivers for the Nvidia GPU which are compatible with the version of CUDA Toolkit, cuDNN and Tensorflow I wish to install on Ubuntu 18.04, namely Tensorflow 2.1 — this requires CUDA 10.1 or above. In doing so, in my case this involves also handling my current installations of Nvidia drivers, CUDA, cuDNN, and Tensorflow (details of which are set out at Step 1).