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Instructions for CUDA v11.8 and cuDNN 8.9.7 installation on Ubuntu 22.04 for PyTorch 2.1.2
### steps ####
# Verify the system has a cuda-capable gpu
# Download and install the nvidia cuda toolkit and cudnn
# Setup environmental variables
# Verify the installation
### to verify your gpu is cuda enable check
lspci | grep -i nvidia
### If you have previous installation remove it first.
sudo apt-get purge nvidia*
sudo apt remove nvidia-*
sudo rm /etc/apt/sources.list.d/cuda*
sudo apt-get autoremove && sudo apt-get autoclean
sudo rm -rf /usr/local/cuda*
# system update
sudo apt-get update
sudo apt-get upgrade
# install other import packages
sudo apt-get install g++ freeglut3-dev build-essential libx11-dev libxmu-dev libxi-dev libglu1-mesa libglu1-mesa-dev
# first get the PPA repository driver
sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt update
# install nvidia driver with dependencies
sudo apt install libnvidia-common-470
sudo apt install libnvidia-gl-470
sudo apt install nvidia-driver-470
# installing CUDA-11.8
sudo dpkg -i cuda-keyring_1.0-1_all.deb
sudo apt-get update
sudo apt-get -y install cuda
# setup your paths
echo 'export PATH=/usr/local/cuda-11.8/bin:$PATH' >> ~/.bashrc
echo 'export LD_LIBRARY_PATH=/usr/local/cuda-11.8/lib64:$LD_LIBRARY_PATH' >> ~/.bashrc
source ~/.bashrc
sudo ldconfig
# install cuDNN v8.9.7
# First register here:
tar -xvf ${CUDNN_TAR_FILE}
# copy the following files into the cuda toolkit directory.
sudo cp cudnn-*-archive/include/cudnn*.h /usr/local/cuda/include
$ sudo cp -P cudnn-*-archive/lib/libcudnn* /usr/local/cuda/lib64
$ sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn*
# Finally, to verify the installation, check
nvcc -V
# install Pytorch (an open source machine learning framework)
pip3 install torch torchvision torchaudio --index-url
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This helped A LOT! Thanks!

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Ahanio commented Apr 27, 2023

Thank you! It was veeery helpful!

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lolo912 commented Jun 29, 2023

Thank you verry much you just forgotten a star character after cudnn here :
sudo cp -P cuda/include/cudnn*.h /usr/local/cuda-11.3/include

Verry important because else an error can be encountered while compiling for example pytorch "cudnn_version.h" not found.

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