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

ml-stars

Primitives and Pipelines for Time Series Data.

• Documentation: https://sinte-dev.github.io/ml-stars
• Homepage: https://github.com/sinte-dev/ml-stars

Overview

TODO: Provide a short overview of the project here.

Install

Requirements

ml-stars has been developed and tested on Python 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 ml-stars is run.

These are the minimum commands needed to create a virtualenv using python3.6 for ml-stars:

pip install virtualenv
virtualenv -p $(which python3.6) ml-stars-venv

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

source ml-stars-venv/bin/activate

Remember to execute it every time you start a new console to work on ml-stars!

Install from source

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

git clone git@github.com:sinte-dev/ml-stars.git
cd ml-stars
git checkout stable
make install

Install for Development

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.

Quickstart

In this short tutorial we will guide you through a series of steps that will help you getting started with ml-stars.

TODO: Create a step by step guide here.

What's next?

For more details about ml-stars and all its possibilities and features, please check the documentation site.

History