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 for features like regularisation or embeddings. Instead, writing down the mathematical form will work β and be fast.
- Extensible by default. Flux is written to be highly extensible and 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. When in doubt, itβs well worth looking at the source. If you need something different, you can easily roll your own.
- Play nicely with others. Flux works well with Julia libraries from images to differential equation solvers, so you can easily build complex data processing pipelines that integrate Flux models.
Installation
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 which supports Nvidia GPUs. To directly access some of its functionality, you may want to add ] add CUDA too. The page on GPU support has more details.
Other closely associated packages, also installed automatically, include Zygote, Optimisers, NNlib, Functors and MLUtils.
Learning Flux
The quick start page trains a simple neural network.
This rest of this documentation 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.
Sections with π contain API listings. The same text is avalable at the Julia prompt, by typing for example ?gpu.
If you just want to get started writing models, the model zoo gives good starting points for many common ones.
Community
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 β 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.