Docknet

The Docknet project comprises:

• a Python package with a pure NumPy implementation of neural networks
• unit tests to validate the code
• a set of Jupyter notebooks making use of the Python package
• a Docker container and REST API to provide an online classification service based on precomputed models

The neural network implementation is strongly based on courses 1 and 2 of Coursera's Deep Learning Specialization: https://www.coursera.org/specializations/deep-learning.

This project has been developed for educational purposes only, namely to:

• understand the math and algorithms required to implement and train neural networks
• illustrate how one could unit test this code
• illustrate how to build a Python package
• illustrate how to consume the Python package with Jupyter notebooks, allowing to mix structured code (the Python package) with exploration code (the notebooks)
• illustrate how to dockerize a Python application and provide a REST API to use it as and online service

Requirements

To run this project Python 3.8 or higher is required, as well as pipenv in order to create a Python virtual environment where to install all the python packages as well as JupyterLab. In Ubuntu, run the command:

sudo apt-get install python3.8 python3.8-dev python3-venv

In macOS one can easily install the necessary packages with Homebrew. Homebrew can be installed with the following command:

/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

Then one can install the necessary packages as follows:

brew install python pipenv

Docker is needed in order to run Docknet as a service inside a Docker container. More information on how to install Docker can be found here:

https://docs.docker.com/get-docker/

Note, the service can also be run without a Docker container, in which case Docker is not needed.

Installation

Run the bash script at:

delivery/scripts/build.sh

This script will create a Python virtual environment at:

$HOME/docknet_venv

and install there the Docknet Python package along with all the necessary Python dependencies and JupyterLab.

Running the notebooks

Activate the Docknet virtual environment:

source $HOME/docknet_venv/bin/activate

Go to the main project folder and open JupyterLab with the following command:

jupyter lab

A web browser should open with the JupyterLab interface. Navigate to the folder:

exploration

4 example notebooks are located there. Each notebook contains a binary classification problem solved with a neural network. The Docknet library contains a set of dataset generators, which produce a random sample for binary classification. A Docknet is created using an appropriate number of layers, neurons and other hyperparameters in order to properly classify the generated data.

Running the web service

There are 2 options for running the web service, running the service directly in your machine or inside a Docker container. For running the service directly in your machine, follow the previous installation steps, then activate the Docknet virtual environment:

source $HOME/docknet_venv/bin/activate

then run the command:

docknet_start

For running the service inside a Docker container, first go to the project main folder and build the container with the command:

docker build -t docknet .

Then run the Docker container with the command:

docker run -p 8080:8080 -it docknet

Independently on whether the service is run inside a Docker container or not, 4 classification services will then be available from the following URLs, each one corresponding to one of the classification problems illustrated in the 4 notebooks:

http://localhost:8080/chessboardprediction?x0=2&x1=2
http://localhost:8080/clusterprediction?x0=2&x1=2
http://localhost:8080/islandprediction?x0=2&x1=2
http://localhost:8080/swirlprediction?x0=2&x1=2

Parameters x0 and x1 correspond to the data point to classify where the values (2, 2) have been given as an example. The services return a JSON such as:

{"message": 1, "success": true}

where "message" is either the predicted label (if "success" is true) or the error message (if "success" is false, for instance if there are missing parameters in the URL).

License

Docknet is distributed under the Apache 2.0 license. A copy of the license can be found in the file LICENSE.txt.