I have stumbled upon many guides to install deep learning libraries, Cuda, cuDNN & nvidia-driver. But I have never been successful in one go following only one guide. So, I decided to write this article. First, this guide is to install deep learning libraries ( TensorFlow, Keras, PyTorch) with GPU support. CPU support is pretty straight-forward, so I won’t be talking about that.

You need a

NVIDIA® GPU card with CUDA® Compute Capability 3.5 or higher.

You can check whether your GPU is compatible or not by visiting this link.

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Installing Nvidia driver

Run these following commands. It adds the necessary repository to download the Nvidia driver. It will

sudo add-apt-repository ppa:graphic-drivers/ppa

sudo apt update
ubuntu-drivers devices | grep nvidia

It will show you the driver versions compatible with your GPU card. I have an RTX 2060 super GPU card. It shows

driver : nvidia-430 — third-party free recommended

It may also show multiple entries depending on the GPU card you are using. Example

driver : nvidia-340 — distro non-free
driver : nvidia-304 — distro non-free
driver : nvidia-384 — distro non-free recommended

Install the suitable version. Your driver will decide the minimum version of CUDA you are going to install. You can find the table here.

sudo apt install nvidia-xxx

Repace xxx with your desired version. Reboot and check the installation

sudo reboot
lsmod|grep nvidia
nvidia-smi

#cuda #cudnn #ubuntu #tensorflow #nvidia-driver