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.


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

Making your Ubuntu deep learning ready
41.65 GEEK