Install Tensorflow 2.0 with GPU support via CUDA and CuDNN on your Ubuntu 18.04 desktop box

steps.sh

#!/bin/bash

## 10-Apr-2020
## Installing tf 2.0 with GPU support using CUDA v10.2 and cuDNN 7.6 on Ubuntu 18.04 

### steps ####
# verify the system has a cuda-capable gpu
# download and install the nvidia cuda toolkit and cudnn
# setup environmental variables
# verify the installation
###

### 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*


### to verify your gpu is cuda enable check
lspci | grep -i nvidia

### gcc compiler is required for development using the cuda toolkit. to verify the version of gcc install enter
gcc --version

# 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-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub
echo "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 /" | sudo tee /etc/apt/sources.list.d/cuda.list

 # installing CUDA-10.2
sudo apt-get -o Dpkg::Options::="--force-overwrite" install cuda-10.2 cuda-drivers


# setup your paths
echo 'export PATH=/usr/local/cuda-10.2/bin:$PATH' >> ~/.bashrc
echo 'export LD_LIBRARY_PATH=/usr/local/cuda-10.2/lib64:$LD_LIBRARY_PATH' >> ~/.bashrc
source ~/.bashrc
sudo ldconfig

# install cuDNN v7.6
# in order to download cuDNN you have to be regeistered here https://developer.nvidia.com/developer-program/signup
# then download cuDNN v7.6 form https://developer.nvidia.com/cudnn


# You need to log into CUDA Developer Network. Just download the tarball to your local machine and unzip it into a
# an arbitrary dir there



# # CUDNN_TAR_FILE="cudnn-10.2-linux-x64-v7.6.5.32.tgz"
# # https://developer.nvidia.com/compute/machine-learning/cudnn/secure/7.6.5.32/Production/10.2_201910.2/cudnn-10.2-linux-x64-v7.6.5.32.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-10.2/include
sudo cp -P cuda/lib64/libcudnn* /usr/local/cuda-10.2/lib64/
sudo chmod a+r /usr/local/cuda-10.2/lib64/libcudnn*

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

# Install Tensorflow with GPU support
# TODO: Make sure 2.0.0b0 is totally compatible with CUDA 10.2 Toolkit and cuDNN 7.6

pip3 install --user tensorflow-gpu==2.0.0b0