TensorFlow with Jupyter Notebooks using Virtualenv

Been trying to learn TensorFlow by working on the Udacity Deep Learning mooc.  All the programming assignments are based on Jupyter Notebooks.  Unfortunately, since I setup my computer with a NVIDIA GPU I've been using Virtualenv to mange my Python distributions as recommended in the Tensorflow installation documents.  However, I've had a really hard time getting IPython and Jupyter configured so I can access all the packages I needed until I read this.

The solution is quite simple.  From your tensorflow environment, first install ipykernel. Then you register the kernel with the tensorflow environment.

$ source ~/tensorflow/bin/activate
$ pip install ipykernel
$ python -m ipykernel install --user --name=tensorflow

Finally, when you open your notebook you will have to change kernels from the default Python ones to the special tensorflow one.  

Installing OpenCV 3.4 on Ubuntu 17.10

I found this nifty guide on installing OpenCV 3 on Ubuntu 16.04.   Although I have Ubuntu 17.10 this guide is still incredibly useful.  I'd like to share some tweaks I when I was following the guide to get OpenCV setup.

To be clear, I have Ubuntu 17.10.  I am installing OpenCV 3.4.0 and Python 3.6.3 which are the latest versions as of Feb 3, 2018.  The guide uses OpenCV 3.1 and Python 3.5.2 on Ubuntu 16.04.

Basically, I followed the guide exactly as written for Python 3 except for the following tweaks below.  I didn't bother with Python 2 configuration.

Step #1 modifications

I had issues installing libpng12-dev.  apt-get couldn't find the package so I had to update /etc/apt/sources.list as suggested here to include the following line:

deb http://mirrors.kernel.org/ubuntu xenial main

However, you might be able to skip libpng12-dev altogether since installing libgtk-3-dev later on in the guide seems to uninstall libpng12-dev.

Figure 1: No need for libpng12-dev

To get the headers and libraries for Python 3.6:

$ sudo apt-get install python3.6-dev

Finally, you need to install Qt 5.  In the past, I don't think we need to install this.  Maybe a recent dependency change?

$ sudo apt-get install qt5-default

Step #2 modifications

Instead of using wget to download OpenCV 3.1.0 I just downloaded the latest version 3.4.0 from the official GitHub repo https://github.com/opencv/opencv/archive/3.4.0.zip.

Likewise, I instead of using wget to pull opencv_contrib I pulled the latest from GitHub https://github.com/opencv/opencv_contrib.  On the web page, just click "Clone or download" on the top right to get the zip file: no need to use git.

 

Step #4 modifications

Since I installed a different version of OpenCV and related contributed code, my directory names are slightly different.  Also, I don't like building stuff in the root of my home directory. 

More importantly, my machine has TensorFlow with GPU support installed.  However, I couldn't get OpenCV to build properly with GPU support so I had to turn support off.  Notice the last part "WITH_CUDA=OFF". Also, we need to enable QT.

$ cd ~/Downloads/opencv-3.4.0/
$ mkdir build
$ cd build
$ cmake -D CMAKE_BUILD_TYPE=RELEASE \
    -D CMAKE_INSTALL_PREFIX=/usr/local \
    -D INSTALL_PYTHON_EXAMPLES=ON \
    -D INSTALL_C_EXAMPLES=OFF \
    -D OPENCV_EXTRA_MODULES_PATH=~/Downloads/opencv_contrib-3.4.0/modules \
    -D PYTHON_EXECUTABLE=~/.virtualenvs/cv/bin/python \
    -D BUILD_EXAMPLES=ON \
    -D WITH_QT=ON \
    -D WITH_CUDA=OFF  ..

Step #5 modifications

My OpenCV library binary came out with a different name and in a different directory than the ones in the guide which is expected. 

Here is where I found my build library.

$ ls -l /usr/local/lib/python3.6/site-packages/

To change the binding name:

$ cd /usr/local/lib/python3.6/site-packages/
$ sudo mv cv2.cpython-36m-x86_64-linux-gnu.so cv2.so

To symlink binding into virtualenv:

$ cd ~/.virtualenvs/cv/lib/python3.6/site-packages/
$ ln -s /usr/local/lib/python3.6/site-packages/cv2.so cv2.so

Step #6 changes

This is a screenshot of what Python 3.6.3 looks like with OpenCV 3.4.0 bindings.

Figure 3: Success!

I hope you find this micro-guide useful.