An open source project from Data to AI Lab at MIT.

Python Boilerplate contains all the boilerplate you need to create a Python package.

• Documentation: https://oufattole.github.io/synthetic-data-generation-optimizer
• Homepage: https://github.com/oufattole/synthetic-data-generation-optimizer

TODO: Provide a short overview of the project here.

Synthetic Data Generation Optimizer has been developed and tested on Python 3.5, 3.6, 3.7 and 3.8

Also, although it is not strictly required, the usage of a virtualenv is highly recommended in order to avoid interfering with other software installed in the system in which Synthetic Data Generation Optimizer is run.

These are the minimum commands needed to create a virtualenv using python3.6 for Synthetic Data Generation Optimizer:

pip install virtualenv
virtualenv -p $(which python3.6) synthetic-data-generation-optimizer-venv

Afterwards, you have to execute this command to activate the virtualenv:

source synthetic-data-generation-optimizer-venv/bin/activate

Remember to execute it every time you start a new console to work on Synthetic Data Generation Optimizer!

With your virtualenv activated, you can clone the repository and install it from source by running make install on the stable branch:

git clone [email protected]:oufattole/synthetic-data-generation-optimizer.git
cd synthetic-data-generation-optimizer
git checkout stable
make install

If you want to contribute to the project, a few more steps are required to make the project ready for development.

Please head to the Contributing Guide for more details about this process.

In this short tutorial we will guide you through a series of steps that will help you getting started with Synthetic Data Generation Optimizer.

TODO: Create a step by step guide here.

For more details about Synthetic Data Generation Optimizer and all its possibilities and features, please check the documentation site.