colda 0.0.1

Collaborative Data Analysis for All

Introduction

ColDA is an open source project aimed at providing distributed machine learning tools for data analysis and machine learning based on Assisted Learning.

Package

Getting Started

Use case

• Examples and Instructions can be found in examples/

Package Stucture

• Basic package structure can be found in Github repository

• Compared to the Basic package structure, docs/ will contain different element. But at this point, you can follow the template

• py-pkg is the main part of the package, you can add more modules (with __init__.py) in this part. For example, if you add temp module, you can import temp module by:

import temp from py-pkg

• This package structure can be improved by learning PyTorch package structure.

• Basic Structure:

py-package-tempate/
 |-- docs/
 |-- |-- build_html/
 |-- |-- build_latex/
 |-- |-- source/
 |-- py-pkg/
 |-- |-- __init__.py
 |-- |-- __version__.py
 |-- |-- curves.py
 |-- |-- entry_points.py
 |-- tests/
 |-- |-- test_data/
 |-- |   |-- supply_demand_data.json
 |-- |   __init__.py
 |-- |   conftest.py
 |-- |   test_curves.py
 |-- .env
 |-- .gitignore
 |-- Pipfile
 |-- Pipfile.lock
 |-- README.md
 |-- setup.py

How to Manage Package Environment

• pipenv is used to manage package. You can install pipenv by:

pip3 install pipenv

• Use pipenv to install package. The first command is to install the package for development. The second command is to install the package for production.

pipenv install --dev
pipenv install 

• Use pipenv to uninstall package:

pipenv uninstall

Pipenv Shells

• Entering into a Pipenv-managed shell. Remeber doing this every time before running the project.

cd py-package-tempate
pipenv install
pipenv shell

License

ColDA is licensed under the Apache 2.0 License.

Code of Conduct

Please review and adhere to the Code of Conduct when contributing to ColDA.

Reference

Please use the following reference

@article{diao2022gal,
  title={GAL: Gradient Assisted Learning for Decentralized Multi-Organization Collaborations},
  author={Diao, Enmao and Ding, Jie and Tarokh, Vahid},
  journal={Advances in Neural Information Processing Systems},
  volume={35},
  pages={11854--11868},
  year={2022}
}