TEteruakohatu·Dec 27, 2023
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
In Julia 1.10chengchingwen
Transformers.jl
Julia2021–2023
chengchingwen / Transformers.jl
Transformers.jl
Julia Implementation of Transformer models
3Distinct people
4Mentions
0Explicit recommenders
574GitHub stars
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Julia Implementation of Transformer models
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Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.
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HN mentions by year4 dated mentions
Observed mentions: 2021-01-18 20:21:26 UTC to 2023-12-27 19:55:04 UTC.
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| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| 2021 | 2021-01-01 00:00:00 UTC | 2022-01-01 00:00:00 UTC | 2 | 0 | 2 |
| 2022 | 2022-01-01 00:00:00 UTC | 2023-01-01 00:00:00 UTC | 1 | 0 | 1 |
| 2023 | 2023-01-01 00:00:00 UTC | 2024-01-01 00:00:00 UTC | 1 | 0 | 1 |
What Hacker News says
4 sources- Jupiter: Yes, Julia was one of the first non-python IPython/Jupiter kernel
- Pandas: DataFrames.jl
- Numpy: Basically the available as part of the stdlibs/language
- Scipy: Yes, but not as one meta package
- Matplotlib: Yes directly as Pyplot.jl or in alternatives such as Plots.jl or Makie.jl
- Pytorch/Tensorflow: There are several ML Frameworks written in Julia (as well as Julia bindings to ML Frameworks) the biggest Julia native one is likely Flux.jl
Regarding HF Transformers a quick Google points to
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.
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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.
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
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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?
> When should I choose FastAI.jl vs fastai?
This depends on your use cases and how comfortable you are with a) Julia b) having to roll some of your own code. For the first, I'd recommend poking around with the language before as well as using the linked dev channel in TFA to get an informed opinion.
FastAI.jl itself is composed of multiple constituent packages that can and are used independently, so there's also the option of mixing and matching. For example, https://github.com/lorenzoh/DataLoaders.jl is completely library agnostic.
If NLP primitives are all that's keeping you from testing the waters, have a look at https://github.com/chengchingwen/Transformers.jl.
In Julia adoption keeps climbing