Flux: The Julia Machine Learning Library
Flux is a library for machine learning. It comes "batteries-included" with many useful tools built in, but also lets you use the full power of the Julia language where you need it. We follow a few key principles:
- Doing the obvious thing. Flux has relatively few explicit APIs. Instead, writing down the mathematical form will work – and be fast.
- Extensible by default. Flux is written to be highly flexible while being performant. Extending Flux is as simple as using your own code as part of the model you want - it is all high-level Julia code.
- Play nicely with others. Flux works well with unrelated Julia libraries from images to differential equation solvers, rather than duplicating them.
Download Julia 1.6 or later, preferably the current stable release. You can add Flux using Julia's package manager, by typing
] add Flux in the Julia prompt. This will automatically install several other packages, including CUDA.jl for Nvidia GPU support.
The quick start page trains a simple neural network.
This rest of the guide provides a from-scratch introduction to Flux's take on models and how they work, starting with fitting a line. Once you understand these docs, congratulations, you also understand Flux's source code, which is intended to be concise, legible and a good reference for more advanced concepts.
There are some tutorials about building particular models. The model zoo has starting points for many other common ones. And finally, the ecosystem page lists packages which define Flux models.
The reference section includes, beside Flux's own functions, those of some companion packages: Zygote.jl (automatic differentiation), Optimisers.jl (training) and others.
Everyone is welcome to join our community on the Julia discourse forum, or the slack chat (channel #machine-learning). If you have questions or issues we'll try to help you out.
If you're interested in hacking on Flux, the source code is open and easy to understand – it's all just the same Julia code you work with normally. You might be interested in our intro issues to get started, or our contributing guide.