Install cuda+libcudnn

README.md

Install PyTorch, CUDA and libcudnn

Motivation

If you write a PyTorch extension utilarizing the GPU and you want to build a unit test using CMake for it you have to install several packages by yourself.

Download

Test what CUDA is supported by your driver

nvidia-smi

Out output contains

Driver Version: 450.80.02 CUDA Version: 11.0

That's why we install all tools for CUDA 11 and try to match the driver version 450.

CUDA Tools

Go to https://developer.nvidia.com/cuda-downloads?target_os=Linux&target_arch=x86_64&target_distro=Ubuntu&target_version=2004&target_type=runfilelocal and select your local settings.

Here we can download

wget https://developer.download.nvidia.com/compute/cuda/11.1.1/local_installers/cuda_11.1.1_455.32.00_linux.run

cuDNN

You need a Nvidia account to be download cudnn packages.

If so go to https://developer.nvidia.com/rdp/cudnn-download and download the cuDNN Runtime Library as well as the cuDNN Developer Library .

Install

sudo ./cuda_11.0.3_450.51.06_linux.run

If they warn you that you have installed the driver already via your package mananger select continue and unselect the driver on the next page.

Make sure you install the runtime before the developer package.

sudo dpkg -i libcudnn8_8.0.5.39-1+cuda11.0_amd64.deb
sudo dpkg -i libcudnn8-dev_8.0.5.39-1+cuda11.0_amd64.deb

pip3 install --user torch==1.7.0+cu110 torchvision==0.8.1+cu110 torchaudio===0.7.0 -f https://download.pytorch.org/whl/torch_stable.html

Add the symlink to your cuda installation.

sudo ln -s /usr/local/cuda-11.0/ /usr/local/cuda

Set the enviroment variables in you .zshrc or .bashrc:

PATH=$PATH:/usr/local/cuda/bin
LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64
CUDA_BIN_PATH=/usr/local/cuda/bin
export Torch_DIR=~/.local/lib/python3.8/site-packages/torch