Cookiecutter GPN:

A flexible template for implementing and sharing machine learning models.

Project Website

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For a detailed description of this cookiecutter template, visit: https://gnplab.github.io/ccgpn/

Requirements to use this cookiecutter:

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• Python 2.7 or 3.8+
• PyTorch >= 1.9
• Tensorboard >= 2.6
• Cookiecutter Python package >= 1.7.0:
• This can be installed with pip by or conda depending on how you manage your Python packages:

$ pip install cookiecutter

or

$ conda config --add channels conda-forge
$ conda install cookiecutter

To start a new project, run:

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$ cookiecutter https://github.com/gpnlab/ccgpn

Contributing

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Best practices change, tools evolve, and lessons are learned. The goal of this project is to make it easier to start, structure, and share an analysis. Pull requests and filing issues is encouraged. We'd love to hear what works for you, and what doesn't!

Acknowledgements

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Many thanks to the Cookiecutter project (github), which is helping us all spend less time molding and more time getting things baked.

This cookiecutter was developed abreast the awesome Cookiecutter Data Science project (github), borrowing features from shablona (e.g. duecredit, travis CI) and inspired by the directory scaffold of Cookiecutter-Pytorch.