## Introduction
ColDA is an open source project aimed at providing distributed machine learning tools for data analysis and machine learning based on [Assisted Learning](https://assisted-learning.org/).
## Package
### Getting Started
### Use case
- Examples and Instructions can be found in `examples/`
### Package Stucture
- Basic package structure can be found in [Github repository](https://github.com/AlexIoannides/py-package-template)
- 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:
```bash
import temp from py-pkg
```
- This package structure can be improved by learning [PyTorch](https://github.com/pytorch/pytorch) package structure.
- Basic Structure:
```bash
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:
```bash
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.
```bash
pipenv install --dev
pipenv install
```
- Use ``pipenv`` to uninstall package:
```bash
pipenv uninstall
```
### Pipenv Shells
- Entering into a Pipenv-managed shell. Remeber doing this **every time** before running the project.
```bash
cd py-package-tempate
pipenv install
pipenv shell
```
## License
ColDA is licensed under the [Apache 2.0 License](LICENSE).
## Code of Conduct
Please review and adhere to the [Code of Conduct](CODE_OF_CONDUCT.md) when contributing to ColDA.
## Reference
Please use the following reference
```bibtex
@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}
}
```
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