Oceananigans.jl VS Transformers.jl

I think it’s also the design philosophy. JuMP and ForwardDiff are great success stories and are packages very light on dependencies. I like those.

The DiffEq library seems to pull you towards the SciML ecosystem and that might not be agreeable to everyone.

For instance a known Julia project that simulates diff equations seems to have implemented their own solver

https://github.com/CliMA/Oceananigans.jl

I‘m currently playing around with Oceananigans.jl (https://github.com/CliMA/Oceananigans.jl). Do you know how both are similar or different?

Oceananigans.jl has really intuitive step-by-step examples and a great discussion page on GitHub.

Flux is quite a nice lower level library:

https://github.com/FluxML/Flux.jl

On top of that there are many higher level libraries such as Transformers.jl

https://github.com/chengchingwen/Transformers.jl

Check out Transformers.jl. It’s a library that implements transformer based models in Julia using Flux.jl. They have support for some of the huggingface transformers.

https://github.com/chengchingwen/Transformers.jl but I have not had any personal experience with.

All of this is build by the community and your mileage may vary.

In my rather biased opinion the strengths of Julia are that the various ML libraries can share implementations, e.g. Pytorch and Tensorflow contain separate Numpy derivatives. One could say that you can write an ML framework in Julia, instead of writting a DSL in Python as part of your C++ ML library. As an example Julia has a GPU compiler so you can write your own layer directly in Julia and integrate it into your pipeline.

I think you might have some luck with looking at a transformers implementation in flux, e.g: https://github.com/chengchingwen/Transformers.jl/tree/master/src/basic

Having tried fastai for a "serious" research project and helped (just a bit) towards FastAI.jl development, here's my take:

> motivation behind this is unclear.

Julia currently has two main DL libraries. Flux, which is somewhere between PyTorch and (tf.)Keras abstraction wise, and Knet, which is a little lower level (think just below PyTorch/around where MXNet Gluon sits). Frameworks like fastai, PyTorch Lightning and Keras demonstrate that there's a desire for higher-level, more batteries included libraries. FastAI.jl is looking to fill that gap in Julia.

> Since FastAI.jl uses Flux, and not PyTorch, functionality has to be reimplemented. FastAI.jl has vision support but no text support yet.

This is correct. That said, FastAI.jl is not and does not plan to be a copy of the Python API (hence "inspired by"). One consequence of this is that integration with other libraries is much easier, e.g. https://github.com/chengchingwen/Transformers.jl for NLP tasks.

> What is the timeline for FastAI.jl to achieve parity?

If NLP primitives are all that's keeping you from testing the waters, have a look at https://github.com/chengchingwen/Transformers.jl.