Flux is a 100% pure-Julia stack and provides lightweight abstractions on top of Julia's native GPU and AD support. It makes the easy things easy while remaining fully hackable.
Flux has features that sets it apart among ML systems.
Flux provides a single, intuitive way to define models, just like mathematical notation. Julia transparently compiles your code, optimizing kernels for the GPU, for the best performance.
Existing Julia libraries are differentiable and can be incorporated directly into Flux models. Cutting edge models such as Universal Neural Differential Equations are first class, and Zygote enables overhead-free gradients.
GPU kernels can be written directly in Julia via CUDA.jl. Flux is uniquely hackable and any part can be tweaked, from GPU code to custom gradients and layers.
Check out Flux's website for more information on the FluxML stack!
