> Companies such as Google and Facebook should have supported Julia for machine learning, in my opinion.
Given that ML is becoming so important and Julia has ergonomic handling of numerical code, why don't they explicitly support it? Is it inertia?
Given that ML is becoming so important and Julia has ergonomic handling of numerical code, why don't they explicitly support it? Is it inertia?
There is Zygote.jl, which is used in Flux.jl. However, it's more in maintenance mode. At some point, Diffractor.jl was hyped but it didn't take off yet. And then there is Enzyme.jl which people hype now.
But for me as a user, it's not clear what I should really do to make my code well differentiable for those libraries.
If you stick with torch, jax or tensorflow, everything seems to work better regarding AD.
[0]: https://github.com/FluxML/Flux.jl [1]: https://github.com/FluxML/Zygote.jl [2]: https://github.com/JuliaDiff/Diffractor.jl [3]: https://github.com/EnzymeAD/Enzyme.jl
I wonder if GPT-4 could be used to efficiently start porting more stuff over to Julia. I feel like Python is really a poor fit for deep learning. Julia is much more pleasant.
So yeah, I'd be surprised if FB ever moved away from Python.
The big mistake was Microsoft not having .NET cross-platform from the beginning and not pitching F# as a Python alternative.
To play devil advocate, i think when F# was out (and still being developed), the Zeitgeist was really enamored with the idea of dynamic language, and the envisioned productivity gain from them. So F# was really too early to the dance.
Since then, things have change quite a bit with most major dynamic language adding a proto-type system.
Right now, i think we understand better what made language such as python and ruby so popular and are able to design better statically typed version with better tooling around them ( rust, zig, julia etc...)
ML on CUDA is very much part of Julia already, and Meta seems into that lib as well. =)
Compared to the CUDA dumpster fire at a cat food factory, it is often trivial and nearly transparent syntax for the users familiar with ML.
Really depends on the use-case =)
The conventional options are fairly well documented:
https://fluxml.ai/Flux.jl/stable/gpu/
It seems to me that Julia is still moving too fast for its own good. At some point, something needs to stabilize for it to be worth adopting.