Just wanted to chime in on this specific example of package “redundancy”.
If I’m not mistaken, FowardDiff.jl will never be the best choice for large-scale deep learning because it implements forward-mode autodiff, whereas Zygote.jl (or Enzyme.jl) implements reverse-mode autodiff. These two methods have very different mathematical properties, and that’s why we need both.
However, I see your more general point, and it is true that in areas like AD, Julia doesn’t yet have a “one package to rule them all” approach. But that’s coherent with the language’s philosophy of dispatching on the tool that works best. For instance, if you are working on small neural networks, SimpleChains.jl seems to give impressive speedups compared to the default choice of Flux.jl.
Sure, it gives a little more work to the package user who has to look around and choose, but I’d argue that
- the gain in performance can be worth the cost
- interface packages such as
ChainRulesCore.jlorAbstractDifferentiation.jlmake it increasingly easy to switch between packages without altering your code (shoutout to their devs!)