Instructions for CUDA v11.3 and cuDNN 8.2 installation on Ubuntu 20.04 for PyTorch 1.11

cuda_11.3_installation_on_Ubuntu_20.04

#!/bin/bash

## https://deeplearningcrashcourse.org/setup_ubuntu/

### steps ####
# verify the system has a cuda-capable gpu
# download and install the nvidia cuda toolkit and cudnn
# setup environmental variables
# verify the installation
###
ubuntu-drivers list
ubuntu-drivers devices
sudo ubuntu-drivers autoinstall

### 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 apt-get autoremove && sudo apt-get autoclean

sudo apt-get purge cuda*
sudo apt-get remove cuda*
sudo rm /etc/apt/sources.list.d/cuda*
sudo apt-get autoremove && sudo apt-get autoclean
sudo rm -rf /usr/local/cuda*

sudo rm -rf /usr/local/cuda  
sudo rm -rf /usr/local/cudnn
sudo updatedb
sudo locate -b '\cudnn' | sudo xargs rm

sudo apt-get purge libcudnn8 libcudnn8-dev libcudnn8-samples


# system update
sudo apt-get update

# install other import packages
sudo apt-get install cmake git 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-get install --no-install-recommends nvidia-driver-535

wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin
sudo mv cuda-ubuntu2004.pin /etc/apt/preferences.d/cuda-repository-pin-600
sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/3bf863cc.pub
sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/ /"
sudo apt-get update

 # installing CUDA-11.3
sudo apt install --no-install-recommends cuda-11-3 

# setup your paths
echo 'export PATH="$HOME/.local/bin:$PATH"' >> ~/.bashrc
echo 'export CUDADIR=/usr/local/cuda-11.3' >> ~/.bashrc
echo 'export PATH=/usr/local/cuda-11.3/bin:$PATH' >> ~/.bashrc
echo 'export LD_LIBRARY_PATH=/usr/local/cuda-11.3/lib64:$LD_LIBRARY_PATH' >> ~/.bashrc
source ~/.bashrc
sudo ldconfig

# export PATH="$HOME/.local/bin:$PATH"
# export CUDADIR=/usr/local/cuda-11.3
# export PATH=/usr/local/cuda-11.3/bin:$PATH
# export LD_LIBRARY_PATH=/usr/local/cuda-11.3/lib64:$LD_LIBRARY_PATH

# install cuDNN v11.3
# First register here: https://developer.nvidia.com/developer-program/signup

CUDNN_TAR_FILE="cudnn-11.3-linux-x64-v8.2.1.32.tgz"
wget https://developer.nvidia.com/compute/machine-learning/cudnn/secure/8.2.1.32/11.3_06072021/cudnn-11.3-linux-x64-v8.2.1.32.tgz
tar -xzvf ${CUDNN_TAR_FILE}

# copy the following files into the cuda toolkit directory.
sudo cp -P cuda/include/cudnn.h /usr/local/cuda-11.3/include
sudo cp -P cuda/lib64/libcudnn* /usr/local/cuda-11.3/lib64/
sudo chmod a+r /usr/local/cuda-11.3/lib64/libcudnn* /usr/local/cuda-11.3/include/cudnn*.h 

sudo dpkg -i libcudnn8_8.2.1.32-1+cuda11.3_amd64.deb 
sudo dpkg -i libcudnn8-dev_8.2.1.32-1+cuda11.3_amd64.deb
sudo dpkg -i libcudnn8-samples_8.2.1.32-1+cuda11.3_amd64.deb 


# cudnnn version
dpkg -l | grep libcudnn

# Again install nvidia-driver-470 for Tesla K40C
sudo apt-get purge nvidia*
sudo apt remove nvidia-*

# first get the PPA repository driver
sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt update
# install nvidia driver with dependencies
sudo apt-get install --no-install-recommends nvidia-driver-470

# Finally, to verify the installation, check
nvidia-smi
nvcc -V

# install Pytorch (an open source machine learning framework)