Tutorial on how to setup your system with a NVIDIA GPU and to install Deep Learning Frameworks like TensorFlow, Darknet for YOLO, Theano and Keras, OpenCV and NVIDIA drivers, CUDA and cuDNN libraries on Ubuntu 16.04.3, 17.10 and 18.04.

Before installing anything, let us first update the information about the packages stored on the computer and upgrade the already installed packages to their latest versions.

sudo apt-get update
sudo apt-get upgrade

Next, we will install some basic packages which we might need during the installation process as well in future.

sudo apt-get install -y build-essential cmake gfortran git pkg-config 

NOTE: The following instructions are only for Ubuntu 17 and 18.04. Skip to the next section if you have Ubuntu 16.04

The defualt gcc vesrion on Ubuntu 17 and 18.04 is gcc-7. However, when we build OpenCV from source with CUDA support, it requires gcc-5.

sudo apt-get install gcc-5 g++-5

Verify the gcc version:

gcc --version

You may stil see version 7 detected. We have to set higher priority for gcc-5 as follows (assuming your gcc installation is located at /usr/bin/gcc-5, and gcc-7's priority is less than 60.

update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-5 60
update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-5 60

Now, to fix the CXX and CC environment variable systemwide, you need to put the lines in your .bashrc file:

echo 'export CXX=/usr/bin/g++-5.4' >> ~/.bashrc
echo 'export CC=/usr/bin/gcc-5.4' >> ~/.bashrc
source ~/.bashrc

The NVIDIA drivers will be automatically detected by Ubuntu in Software and Updates under Additional drivers. Select the driver for your GPU and click apply changes and reboot your system. You may also select and apply Intel Microcode drivers in this window.

At the time of writing this document, the latest stable driver version is 384.111

Run the following command to check whether the driver has installed successfully by running NVIDIA’s System Management Interface (nvidia-smi). It is a tool used for monitoring the state of the GPU.

nvidia-smi

CUDA (Compute Unified Device Architecture) is a parallel computing platform and API developed by NVIDIA which utilizes the parallel computing capabilities of the GPUs. In order to use the graphics card, we need to have CUDA drivers installed on our system.

Download the CUDA driver from the official nvidia website official nvidia website . We recommend you download the deb (local) version from Installer type as shown in the screenshot below.

NOTE: The following instructions will also work for Ubuntu 17 and 18.04.

After downloading the file, go to the folder where you have downloaded the file and run the following commands from the terminal to install the CUDA drivers. Please make sure that the filename used in the command below is the same as the downloaded file.

sudo dpkg -i cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64.deb
sudo apt-get update
sudo apt-get install -y cuda-8-0

Now, you have to install the CUDA performance update patch available from the same webpage where you downloaded the CUDA Base Installer.

sudo dpkg -i cuda-repo-ubuntu1604-8-0-local-cublas-performance-update_8.0.61-1_amd64.deb
sudo apt-get update

Next, update the paths for CUDA library and executables.

echo 'export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda-8.0/lib64:/usr/local/cuda-8.0/extras/CUPTI/lib64"' >> ~/.bashrc
echo 'export CUDA_HOME=/usr/local/cuda-8.0' >> ~/.bashrc
echo 'export PATH="/usr/local/cuda-8.0/bin:$PATH"' >> ~/.bashrc
source ~/.bashrc

You can verify the installation of CUDA 8.0 by running:

nvcc -V

CUDA Deep Neural Network (cuDNN) is a library used for further optimizing neural network computations. It is written using the CUDA API.

Go to official cuDNN website official cuDNN website and fill out the form for downloading the cuDNN library. You should download the “cuDNN v6.0 Library for Linux” under CUDA 8.0 from the options.

Now, go to the folder where you have downloaded the “.tgz” file and from the command line execute the following.

tar xvf cudnn-8.0-linux-x64-v6.0.tgz
sudo cp -P cuda/lib64/* /usr/local/cuda-8.0/lib64/
sudo cp cuda/include/* /usr/local/cuda-8.0/include/
sudo chmod a+r /usr/local/cuda-8.0/include/cudnn.h

To check installation of cuDNN, run this in your terminal:

function lib_installed() { /sbin/ldconfig -N -v $(sed 's/:/ /' <<< $LD_LIBRARY_PATH) 2>/dev/null | grep $1; }
function check() { lib_installed $1 && echo "$1 is installed" || echo "ERROR: $1 is NOT installed"; }
check libcudnn 

Now, we install Tensorflow, Keras and Theano along with other standard Python ML libraries like numpy, scipy, sklearn etc.

Install dependencies of deep learning frameworks:

sudo apt-get install -y libprotobuf-dev libleveldb-dev libsnappy-dev libhdf5-serial-dev protobuf-compiler libopencv-dev

Next, we install python 2 and 3 along with other important packages like boost, lmdb, glog, blas etc.

sudo apt-get install -y --no-install-recommends libboost-all-dev doxygen
sudo apt-get install -y libgflags-dev libgoogle-glog-dev liblmdb-dev libblas-dev 
sudo apt-get install -y libatlas-base-dev libopenblas-dev libgphoto2-dev libeigen3-dev libhdf5-dev 
 
sudo apt-get install -y python-dev python-pip python-nose python-numpy python-scipy python-wheel
sudo apt-get install -y python3-dev python3-pip python3-nose python3-numpy python3-scipy python3-wheel

Before we use pip, make sure you have the latest version of pip.

sudo pip install --upgrade pip

Now, we can install all the required python packages for deep learning frameworks:

sudo pip install numpy scipy matplotlib scikit-image scikit-learn ipython protobuf jupyter

Upgrade numpy to the latest version:

sudo pip install --upgrade numpy

NOTE: If you already have a built *.whl file, skip to next sub-section to install TensorFlow directly using PIP

First verify that you are using Python 2.7 as default and pip for Python 2.7. Upgrade pip to latest version if you see a warning message. Also verify that you are using numpy version >= 1.14

python
>>> import numpy
>>> numpy.__version__
>>> Ctrl+D
pip --version

You should be able to see the following output:

>>> Python 2.7.14 (default, Sep 23 2017, 22:06:14) 
>>> '1.14.0'	
pip <version> from /usr/lib/python2.7/dist-packages (python 2.7)

Now we will download the TensorFlow repository from GitHub in the /home folder. Checkout to the latest version of TensorFlow (r1.5 is used here).

cd ~
git clone https://github.com/tensorflow/tensorflow.git
cd tensorflow
git checkout r1.5

You must also install libcupti which for Cuda Toolkit >= 8.0 you do via:

sudo apt-get install cuda-command-line-tools-8-0 

For Cuda Toolkit <= 7.5, you install libcupti-dev by invoking the following command:

sudo apt-get install libcupti-dev 

Next we need to install Bazel

sudo apt-get install openjdk-8-jdk
sudo apt-get install curl
sudo apt-get install zip zlib1g-dev unzip

echo "deb [arch=amd64] http://storage.googleapis.com/bazel-apt stable jdk1.8" | sudo tee /etc/apt/sources.list.d/bazel.list
curl https://bazel.build/bazel-release.pub.gpg | sudo apt-key add -
sudo apt-get update && sudo apt-get install bazel
sudo apt-get upgrade bazel

wget https://github.com/bazelbuild/bazel/releases/download/0.11.0/bazel-0.11.0-installer-linux-x86_64.sh
chmod +x bazel-0.11.0-installer-linux-x86_64.sh
./bazel-0.11.0-installer-linux-x86_64.sh --user

export PATH="$PATH:$HOME/bin"
source ~/.bashrc

To verify installation of Bazel run:

bazel version

Now install brew on your system:

sudo apt-get install linuxbrew-wrapper
brew doctor
brew install coreutils

The root of the tensorflow folder contains a bash script named configure. This script asks you to identify the pathname of all relevant TensorFlow dependencies and specify other build configuration options such as compiler flags. You must run this script prior to creating the pip package and installing TensorFlow.

cd ~/tensorflow
./configure

Select Python 2.7, no to all additional packages, gcc as compiler (GCC 5.4).

For CUDA, enter 8.0

For cuDNN, enter 6

Enter your GPU Compute Capability (Eg: 3.0 or 6.1). Find yout GPU Compute Capability from here.

Use nvcc as the CUDA compiler.

Finally, build the pip package:

bazel build --config=opt --config=cuda --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" //tensorflow/tools/pip_package:build_pip_package 

The build might take upto an hour. If it fails to build, you must clean your build using the following command and configure the build once again.

bazel clean --expunge
./configure

The bazel build command builds a script named build_pip_package. Running this script as follows will build a .whl file within the /tmp/tensorflow_pkg directory:

bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg

Once the build is complete, invoke pip install to install that pip package. The filename of the .whl file depends on your platform. Use tab completion to find your package.

sudo pip install /tmp/tensorflow_pkg/tensorflow <TAB>

If you get an error saying package is not supported for the current platform, run pip as pip2 for Python 2.7:

sudo pip2 install /tmp/tensorflow_pkg/tensorflow <TAB> (*.whl)

You can make a backup of this .whl file.

cp /tmp/tensorflow_pkg/tensorflow <TAB> (*.whl) <BACKUP_LOCATION>

Verify that TensorFlow is using the GPU for computation by running the following python script.

NOTE: Running a script from the /tensorflow root directory might show some errors. Change to any other directory and run the following python script.

import tensorflow as tf
with tf.device('/gpu:0'):
    a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
    b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
    c = tf.matmul(a, b)

with tf.Session() as sess:
    print (sess.run(c))

Here,

• "/cpu:0": The CPU of your machine.
• "/gpu:0": The GPU of your machine, if you have one.

If you have a gpu and can use it, you will see the result. Otherwise you will see an error with a long stacktrace.

Now, install keras for TensorFlow and Theano

sudo pip install keras
sudo pip install Theano	

Check installation of frameworks:

python
>>> import numpy
>>> numpy.__version__
>>> import tensorflow
>>> tensorflow.__version__
>>> import keras
>>> Using TensorFlow backend.
>>> keras.__version__
>>> import theano
>>> theano.__version__

First we will install the dependencies:

sudo apt-get remove -y x264 libx264-dev
sudo apt-get install -y checkinstall yasm
sudo apt-get install -y libjpeg8-dev libjasper-dev libpng12-dev

sudo apt-get install -y libtiff5-dev
sudo apt-get install -y libavcodec-dev libavformat-dev libswscale-dev libdc1394-22-dev
 
sudo apt-get install -y libxine2-dev libv4l-dev
sudo apt-get install -y libgstreamer0.10-dev libgstreamer-plugins-base0.10-dev
sudo apt-get install -y libqt4-dev libgtk2.0-dev libtbb-dev
sudo apt-get install -y libfaac-dev libmp3lame-dev libtheora-dev
sudo apt-get install -y libvorbis-dev libxvidcore-dev
sudo apt-get install -y libopencore-amrnb-dev libopencore-amrwb-dev
sudo apt-get install -y x264 v4l-utils

Download OpenCV 3.4.0:

git clone https://github.com/opencv/opencv.git
cd opencv
git checkout 3.4.0
cd ..

Download OpenCV-contrib 3.4.0:

git clone https://github.com/opencv/opencv_contrib.git
cd opencv_contrib
git checkout 3.4.0
cd ..

Configure and generate the MakeFile in /opencv/build folder (make sure to specify paths to downloaded OpenCV-contrib modules correctly):

cd opencv
mkdir build
cd build

cmake -D CMAKE_BUILD_TYPE=RELEASE \
      -D CMAKE_INSTALL_PREFIX=/usr/local \
      -D INSTALL_C_EXAMPLES=ON \
      -D INSTALL_PYTHON_EXAMPLES=ON \
      -D WITH_TBB=ON \
      -D WITH_V4L=ON \
      -D WITH_QT=ON \
      -D WITH_OPENGL=ON \
      -D WITH_CUDA=ON \
      -D OPENCV_EXTRA_MODULES_PATH=../../opencv_contrib/modules \
      -D BUILD_EXAMPLES=ON ..

      -D CMAKE_CXX_COMPILER:FILEPATH=/usr/bin/g++-5 \
      -D CMAKE_C_COMPILER:FILEPATH=/usr/bin/gcc-5 \

Compile and install:

make -j$(nproc)
sudo make install
sudo ldconfig

Check installation of OpenCV:

python
>>> import cv2
>>> cv2.__version__

Retain the build folder in the same location. This will be required if you want to uninstall OpenCV or upgrade in the future or else the uninstall process might become very tedious.

To uninstall OpenCV:

cd /opencv/build
sudo make uninstall

First clone the Darknet git repository.

git clone https://github.com/pjreddie/darknet.git

Now, to compile Darknet with CUDA, CuDNN and OpenCV support, open the Makefile from the darknet folder and make the changes as following in the beginning of this file.

GPU=1
CUDNN=1
OPENCV=1

Once done, just run make from the darknet folder.

cd darknet
make

Refer here for more details on running YOLO and training the network.