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<!-- PROJECT LOGO --> <br /> <p align="center"> <a href="#"><img src="docs/source/_static/Banner.svg" alt="Logo"/></a> <h3 align="center">A Practical Debugging Tool for Training Deep Neural Networks</h3> <p align="center"> A better status screen for deep learning. </p> </p> <p align="center"> <a href="#installation">Installation</a> • <a href="https://cockpit.readthedocs.io/">Docs</a> • <a href="https://github.com/fsschneider/cockpit-experiments">Experiments</a> • <a href="#license">License</a> • <a href="#citation">Citation</a> </p>
pip install cockpit-for-pytorch
Cockpit is a visual and statistical debugger specifically designed for deep learning. Training a deep neural network is often a pain! Successfully training such a network usually requires either years of intuition or expensive parameter searches involving lots of trial and error. Traditional debuggers provide only limited help: They can find syntactical errors but not training bugs such as ill-chosen learning rates. Cockpit offers a closer, more meaningful look into the training process with multiple well-chosen instruments.
Installation
To install Cockpit simply run
pip install cockpit-for-pytorch
Documentation
The documentation provides a full tutorial on how to get started using Cockpit as well as a detailed documentation of its API.
<!-- Experiments -->Experiments
To showcase the capabilities of Cockpit we performed several experiments illustrating the usefulness of our debugging tool. The code for the experiments can be found in a separate repository. For a discussion of those experiments please refer to our paper.
<!-- LICENSE -->License
Distributed under the MIT License. See LICENSE for more information.
Citation
If you use Cockpit, please consider citing:
@misc{schneider2021cockpit,
title={{Cockpit: A Practical Debugging Tool for Training Deep Neural Networks}},
author={Frank Schneider and Felix Dangel and Philipp Hennig},
year={2021},
eprint={2102.06604},
archivePrefix={arXiv},
primaryClass={cs.LG}
}