Google Season of Docs

The Flux project is a participant in Google Season of Docs. In 2020, we had a technical writer work on our website. The project was a success and we have since then build out various projects specifically for interested technical writers.


Flux.jl is an elegant approach to machine learning in Julia. It is designed to be hackable and flexible, extendable, and exposes powerful AD tools. It also provides abstractions over the popular layers and optimizers used in neural networks. It is built with differentiable programming in mind. The mentors for this project are Dhairya Gandhi.

Potential Impact

Flux is an innovative approach to machine learning. This also means that not all the same patterns and assumptions truly hold when translating from a different framework. It also needs a way to communicate a compelling description of how to implement many of the user-facing niceties that one might need in the course of completing an ML project. Through this, we want to also find areas of improvement where we could offer a better user experience.

This would also greatly benefit the adoption of Flux in the larger ML ecosystem, which we feel is currently held back due to not having enough of these simple patterns documented in an approachable form. We want to see an increase in the number of contributors to the various packages too since that would help us improve our stack better. Flux also utilizes simple to understand and performant code, made possible by Julia, and through this, we also want to bring awareness to how our ecosystem has matured, and increase its adoption in research and industry.