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About us#

History#

This project was started by Alykhan Tejani @alykhantejani, Francisco Massa @fmassa and Joost van Amersfoort @y0ast in the end of 2017 and was highly inspired by torchnet.

In 2018 project core teams has grown and the first public release was published in June 2018. Since then, several releases have appeared following a ~5 month cycle, and a thriving international community has been leading the development.

Governance#

This project is purely open-source community effort. Project’s affiliation to PyTorch group is due to historical reasons and it has the same implication to the project’s governance as for other community projects from PyTorch Ecosystem.

The decision making process and governance structure of the project is described in the governance document.

Authors#

The following people are currently core contributors to PyTorch-Ignite’s development and maintenance:

Emeritus Core Developers#

The following people have been active contributors in the past, but are no longer active in the project:

Join Core Team#

We are looking for motivated contributors to become collaborators and help out with the project. We can start considering a candidate after several successfully merged Github pull requests. If you are interested, for more details, please, contact Victor (@vfdev-5) on PyTorch Slack or via email vfdev.5 at gmail.com.

Citing PyTorch-Ignite#

If you use pytorch-ignite in a scientific publication, we would appreciate citations to the project.

@misc{pytorch-ignite,
  author = {V. Fomin, J. Anmol, S. Desroziers, J. Kriss and A. Tejani},
  title = {High-level library to help with training neural networks in PyTorch},
  year = {2020},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/pytorch/ignite}},
}

Acknowledgements#

We gratefully acknowledge the contributions of all open-source developers who helped get this project off of the ground and supported the project.

We are also grateful to Soumith Chintala @soumith, Joe Spisak @jspisak from Facebook for their help and to PyTorch group for infrastructure support (GPU CI and hosting of conda releases and our documentation).

In particular, Victor Fomin @vfdev-5 acknowledges Magellium and Quansight for supporting project’s development.

We also acknowledge scikit-learn project’s documentation on governance, author’s presentation etc from which this project is highly inspired.