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 – and be fast.
- You could have written Flux. All of it, from LSTMs to GPU kernels, is straightforward Julia code. When in doubt, it’s 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 data frames and images to differential equation solvers, so you can easily build complex data processing pipelines that integrate Flux models.
Download Julia 1.0 or later, if you haven't already. You can add Flux from using Julia's package manager, by typing
] add Flux in the Julia prompt.
If you have CUDA you can also run
] add CUDA to get GPU support; see here for more details.
NOTE: Flux used to have a CuArrays.jl dependency until v0.10.4, replaced by CUDA.jl in v0.11.0. If you're upgrading Flux from v0.10.4 or a lower version, you may need to remove CuArrays (run
] rm CuArrays) before you can upgrade.
There are several different ways to learn Flux. If you just want to get started writing models, the model zoo gives good starting points for many common ones. This documentation provides a reference to all of Flux's APIs, as well as a from-scratch introduction to Flux's take on models and how they work. 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.