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.
Running the demo
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 ,  and from Matthew Earl , 
 http://www.learnopencv.com/face-swap-using-opencv-c-python/  https://github.com/spmallick/learnopencv/tree/master/FaceSwap  http://matthewearl.github.io/2015/07/28/switching-eds-with-python/  https://github.com/matthewearl/faceswap/blob/master/faceswap.py