Transformers.jl
PyCall.jl
Transformers.jl | PyCall.jl | |
---|---|---|
7 | 28 | |
504 | 1,438 | |
- | 0.3% | |
6.9 | 6.1 | |
3 months ago | about 2 months ago | |
Julia | Julia | |
MIT License | MIT License |
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Transformers.jl
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Julia 1.10 Released
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
- How is Julia Performance with GPUs (for LLMs)?
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Load a transformer model with julia
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.
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Ask HN: Why hasn't the Deep Learning community embraced Julia yet?
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.
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Help on Differentiable Programming
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
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Fastai.jl: Fastai for Julia
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?
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Julia Update: Adoption Keeps Climbing; Is It a Python Challenger?
If NLP primitives are all that's keeping you from testing the waters, have a look at https://github.com/chengchingwen/Transformers.jl.
PyCall.jl
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I just started into Julia for ML
For point 3 you can use https://github.com/cjdoris/PythonCall.jl or https://github.com/JuliaPy/PyCall.jl (and their respective Python sister packages).
- The Mojo Programming Language: A Python Superset Drawing from Rust's Strengths
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Calling Chapel, Carbon, and zig code in Julia
PyCall.jl is really handy. Are there any similar projects for calling Chapel code, or Carbon/zig?
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Am I dumb in thinking I can use Rust as a Fast Python and leave it at that?
Julia and Python interop should not be a problem at all. Actually Julia has one of the best interops I’ve ever seen, so much that swift copied it. https://github.com/JuliaPy/PyCall.jl
- Which tools do you use for python + Data Science?
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I don't want to abandon Rust for Julia
One small note, julia also has great python interop via PyCall.jl
- Faster Python calculations with Numba: 2 lines of code, 13× speed-up
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Interoperability in Julia
It is possible to call Python from Julia using PyCall. Then to install PyCall, run the command in the Julia REPL.
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Why is Python so used in the machine learning?
That said, you can run python modules in Julia. So you can just export your code as a module and then use it in Julia via the PyCall package. short description here github here <— you’d just add the pacakge via the really nice package manager built into julia, but for link for more detailed documentation
- Use rust code in Python with pyo3
What are some alternatives?
Flux.jl - Relax! Flux is the ML library that doesn't make you tensor
py2many - Transpiler of Python to many other languages
PackageCompiler.jl - Compile your Julia Package
Revise.jl - Automatically update function definitions in a running Julia session
model-zoo - Please do not feed the models
julia - The Julia Programming Language
DataLoaders.jl - A parallel iterator for large machine learning datasets that don't fit into memory inspired by PyTorch's `DataLoader` class.
Genie.jl - 🧞The highly productive Julia web framework
Chain.jl - A Julia package for piping a value through a series of transformation expressions using a more convenient syntax than Julia's native piping functionality.
are-we-fast-yet - Are We Fast Yet? Comparing Language Implementations with Objects, Closures, and Arrays
StatsPlots.jl - Statistical plotting recipes for Plots.jl
fast-ruby - :dash: Writing Fast Ruby :heart_eyes: -- Collect Common Ruby idioms.