Run this file to set up a fresh Ubuntu install ready for deep learning and computer vision

build.sh

cd ~

sudo apt-get update
sudo apt-get upgrade

sudo apt-get install build-essential cmake pkg-config git unzip libcurl3-dev

# Set up GPU support 
# 1. Install CUDA: This is a set of drivers for your GPU that allows it to run a low-level programming language for parallel computing.
# 2. Install CuDNN: This is a library of highly optimized primitives for deep learning.

################
##### CUDA #####
################

# NOTE
# I'm frequently running into login loops. The most likely cause of this are the Nvidia drivers.
# Possible fixes are listed here:
# http://askubuntu.com/questions/760934/graphics-issues-after-while-installing-ubuntu-16-04-16-10-with-nvidia-graphics
#
# Log into your account in the TTY.
# Run sudo apt-get purge nvidia-*
# Run sudo add-apt-repository ppa:graphics-drivers/ppa and then sudo apt-get update.
# Run sudo apt-get install nvidia-375.
# Reboot and your graphics issue should be fixed.

# Download CUDA
mkdir cuda_installers
wget https://developer.nvidia.com/compute/cuda/8.0/prod/local_installers/cuda_8.0.44_linux-run
sudo sh cuda_8.0.44_linux-run -extract=./cuda_installers

# Install CUDA
cd cuda_installers

# Hit CTRL+ALT+F1 and login using your credentials
sudo service lightdm stop
sudo init 3

# Run the CUDA installers in order
sudo ./NVIDIA-Linux-x86_64-367.48.run
modprobe nvidia
sudo ./cuda-linux64-rel-8.0.44-21122537.run
sudo ./cuda-samples-linux-8.0.44-21122537.run 

# Configure environment variables
# Make sure there is a cuda-8.0 folder in /usr/local
echo 'export CUDA_HOME=/usr/local/cuda-8.0' >> ~/.bashrc
echo 'LD_LIBRARY_PATH=${CUDA_HOME}/lib64:$LD_LIBRARY_PATH' >> ~/.bashrc
echo 'PATH=${CUDA_HOME}/bin:${PATH}' >> ~/.bashrc
source ~/.bashrc

# Test the CUDA Installation
cd /usr/local/cuda/samples
make

# After `make` has finished run `deviceQuery` to make sure CUDA and your GPU are on the same page.
# It should print out your GPU's specs.
cd /usr/local/cuda/samples/bin/x86_64/linux/release
./deviceQuery

#################
##### CuDNN #####
#################

# Download dependencies for deep learning
sudo apt-get install libatlas-base-dev gfortran liblapack-dev libhdf5-serial-dev libopenblas-dev


# Install CuDNN from https://developer.nvidia.com/rdp/cudnn-download
# NOTE: Will need an Nvidia account!
mkdir cudnn_installers
tar -zxf cudnn-8.0-linux-x64-v5.1.tgz -C ./cudnn_installers
cd cudnn_installers/cuda
sudo cp lib64/* /usr/local/cuda-8.0/lib64/
sudo cp include/* /usr/local/cuda-8.0/include/

###############################
##### pyenv && virtualenv #####
###############################

# Install pyenv
git clone https://github.com/yyuu/pyenv.git ~/.pyenv
echo 'export PYENV_ROOT="$HOME/.pyenv"' >> ~/.bashrc
echo 'export PATH="$PYENV_ROOT/bin:$PATH"' >> ~/.bashrc
echo 'eval "$(pyenv init -)"' >> ~/.bash_profile

# Install pyenv-virtualenv
git clone https://github.com/yyuu/pyenv-virtualenv.git $(pyenv root)/plugins/pyenv-virtualenv
echo 'eval "$(pyenv virtualenv-init -)"' >> ~/.bashrc

exec "$SHELL"

# Create a virtual environment for deep learning
pyenv install 3.5.0

pyenv virtualenv 3.5.0 tensorflow
pyenv activate tensorflow
pip install --upgrade pip
pip install numpy scipy matplotlib pydot-ng pandas scikit-learn scikit-image mahotas
sudo apt-get install graphviz

export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-0.12.1-cp35-cp35m-linux_x86_64.whl
pip install --upgrade $TF_BINARY_URL

# Create a virtual environment for computer vision
pyenv virtualenv 3.5.0 opencv3
pyenv activate opencv3
pip install numpy matplotlib scikit-learn scikit-image mahotas

##################
##### OpenCV #####
##################

# Download dependencies for computer vision
sudo apt-get install libjpeg8-dev libtiff5-dev libjasper-dev libpng12-dev
sudo apt-get install libavcodec-dev libavformat-dev libswscale-dev libv4l-dev
sudo apt-get install libxvidcore-dev libx264-dev
sudo apt-get install libgtk-3-dev
sudo apt-get install libgtk2.0-dev

# Download OpenCV
cd ~
wget -O opencv.zip https://github.com/Itseez/opencv/archive/3.2.0.zip
unzip opencv.zip

# Download additional computer vision algorithms
wget -O opencv_contrib.zip https://github.com/Itseez/opencv_contrib/archive/3.2.0.zip
unzip opencv_contrib.zip

# NOTE
# Encountered an issue where we couldn't build OpenCV3 because the file libGL.so
# could not be found. This file is part of the Nvidia drivers. The solution is
# detailed in the URL below. It required fixing a symlink.
# 
# See: http://techtidings.blogspot.com/2012/01/problem-with-libglso-on-64-bit-ubuntu.html
cmake -D CMAKE_BUILD_TYPE=RELEASE \
        -D CMAKE_INSTALL_PREFIX=/usr/local \
        -D OPENCV_EXTRA_MODULES_PATH=~/opencv_contrib-3.2.0/modules \
        -D BUILD_opencv_python2=OFF \
        -D BUILD_opencv_python3=ON \
        -D BUILD_opencv_java=OFF \
        -D BUILD_TIFF=ON \
        -D WITH_CUDA=ON \
        -D WITH_CUBLAS=ON \
        -D CUDA_NVCC_FLAGS="-D_FORCE_INLINES" \
        -D ENABLE_FAST_MATH=1 \
        -D CUDA_FAST_MATH=1 \
        -D WITH_QT=ON \
        -D ENABLE_AVX=ON \
        -D WITH_OPENGL=ON \
        -D WITH_OPENCL=OFF \
        -D WITH_IPP=OFF \
        -D WITH_TBB=ON \
        -D WITH_EIGEN=ON \
        -D WITH_V4L=OFF \
        -D WITH_VTK=OFF \
        -D BUILD_TESTS=OFF \
        -D BUILD_PERF_TESTS=OFF \
        -D INSTALL_PYTHON_EXAMPLES=ON \
        -D INSTALL_C_EXAMPLES=OFF \
        -D PYTHON3_LIBRARY=$(python -c "import re, os.path; print(os.path.normpath(os.path.join(os.path.dirname(re.__file__), '..', 'libpython3.6m.dylib')))") \
        -D PYTHON3_EXECUTABLE=$(which python) \
        -D PYTHON3_INCLUDE_DIR=$(python -c "from distutils.sysconfig import get_python_inc; print(get_python_inc())") \
        -D PYTHON3_PACKAGES_PATH=$(python -c "from distutils.sysconfig import get_python_lib; print(get_python_lib())") \
        -D BUILD_EXAMPLES=ON ../ \

make -j8
make install