Storlets and Jupyter notebook demonstrating end to end machine learning with storlets using IPython

On a fresh new 16.04 VM with apasswordless sudoer simply:

git clone https://github.com/eranr/e2emlstorlets.git
cd e2emlstorlets
./install.sh
tox -e func
./prepare_machine.sh

This will install Swift and Storlets on the VM together with a docker container that has the all the necessary packages for running the storlets in the repo. Those packages include: opencv, scikit-learn and dlib

In addition the prepare_machine.sh will install all the necessary packages to run jupyter and the demo notebook.

sudo python setup.py install
python upload_data.py create
jupyter notebook --no-browser --ip=<host ip>

Follow the output of the "jupyter_notebook" instruction above to connect from a browser. From the browser, open the notebook: "e2emlstorlets/e2e-demo-swift.ipynb"

These instructions do not cover the last part of the demo which compares run time with AWS S3.

The code behind the face swap is a combination of code borrowed from Satya Mallic [0], [1] and from Matthew Earl [2], [3]

[0] http://www.learnopencv.com/face-swap-using-opencv-c-python/ [1] https://github.com/spmallick/learnopencv/tree/master/FaceSwap [2] http://matthewearl.github.io/2015/07/28/switching-eds-with-python/ [3] https://github.com/matthewearl/faceswap/blob/master/faceswap.py