Lux.jl
Clang.jl
Lux.jl | Clang.jl | |
---|---|---|
4 | 2 | |
440 | 217 | |
2.5% | 0.5% | |
9.8 | 8.3 | |
5 days ago | 15 days ago | |
Julia | Julia | |
MIT License | MIT License |
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Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Lux.jl
- Julia 1.10 Released
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[R] Easiest way to train RNN's in MATLAB or Julia?
There is also the less known Lux.jl package: https://github.com/avik-pal/Lux.jl
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“Why I still recommend Julia”
Can you point to a concrete example of one that someone would run into when using the differential equation solvers with the default and recommended Enzyme AD for vector-Jacobian products? I'd be happy to look into it, but there do not currently seem to be any correctness issues in the Enzyme issue tracker that are current (3 issues are open but they all seem to be fixed, other than https://github.com/EnzymeAD/Enzyme.jl/issues/278 which is actually an activity analysis bug in LLVM). So please be more specific. The issue with Enzyme right now seems to moreso be about finding functional forms that compile, and it throws compile-time errors in the event that it cannot fully analyze the program and if it has too much dynamic behavior (example: https://github.com/EnzymeAD/Enzyme.jl/issues/368).
Additional note, we recently did a overhaul of SciMLSensitivity (https://sensitivity.sciml.ai/dev/) and setup a system which amounts to 15 hours of direct unit tests doing a combinatoric check of arguments with 4 hours of downstream testing (https://github.com/SciML/SciMLSensitivity.jl/actions/runs/25...). What that identified is that any remaining issues that can arise are due to the implicit parameters mechanism in Zygote (Zygote.params). To counteract this upstream issue, we (a) try to default to never default to Zygote VJPs whenever we can avoid it (hence defaulting to Enzyme and ReverseDiff first as previously mentioned), and (b) put in a mechanism for early error throwing if Zygote hits any not implemented derivative case with an explicit error message (https://github.com/SciML/SciMLSensitivity.jl/blob/v7.0.1/src...). We have alerted the devs of the machine learning libraries, and from this there has been a lot of movement. In particular, a globals-free machine learning library, Lux.jl, was created with fully explicit parameters https://lux.csail.mit.edu/dev/, and thus by design it cannot have this issue. In addition, the Flux.jl library itself is looking to do a redesign that eliminates implicit parameters (https://github.com/FluxML/Flux.jl/issues/1986). Which design will be the one in the end, that's uncertain right now, but it's clear that no matter what the future designs of the deep learning libraries will fully cut out that part of Zygote.jl. And additionally, the other AD libraries (Enzyme and Diffractor for example) do not have this "feature", so it's an issue that can only arise from a specific (not recommended) way of using Zygote (which now throws explicit error messages early and often if used anywhere near SciML because I don't tolerate it).
So from this, SciML should be rather safe and if not, please share some details and I'd be happy to dig in.
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The Julia language has a number of correctness flaws
Lots of things are being rewritten. Remember we just released a new neural network library the other day, SimpleChains.jl, and showed that it gave about a 10x speed improvement on modern CPUs with multithreading enabled vs Jax Equinox (and 22x when AVX-512 is enabled) for smaller neural network and matrix-vector types of cases (https://julialang.org/blog/2022/04/simple-chains/). Then there's Lux.jl fixing some major issues of Flux.jl (https://github.com/avik-pal/Lux.jl). Pretty much everything is switching to Enzyme which improves performance quite a bit over Zygote and allows for full mutation support (https://github.com/EnzymeAD/Enzyme.jl). So an entire machine learning stack is already seeing parts release.
Right now we're in a bit of an uncomfortable spot where we have to use Zygote for a few things and then Enzyme for everything else, but the custom rules system is rather close and that's the piece that's needed to make the full transition.
Clang.jl
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Julia 1.10 Released
Are there solid C interfaces that can be used?
A large part of why I started using Julia is because calling into other languages through the C FFI is pretty easy and efficient. Most of the wrappers are a single line. If there is not existing driver support, I would pass the C headers through Clang.jl, which automatically wraps the C API in a C header.
https://github.com/JuliaInterop/Clang.jl
I most recently did this with libtiff. Here is the Clang.jl code to generate the bindings. It's less than 30 lines of sterotypical code.
https://github.com/mkitti/LibTIFF.jl/tree/main/gen
The generated bindings with a few tweaks is here:
https://github.com/mkitti/LibTIFF.jl/blob/main/src/LibTIFF.j...
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A new C++ <-> Julia Wrapper: jluna
If you are interested in C++ interop you can also have a look at Clang.jl and CxxWrap.jl (the usual Julia package chaos applies, where the package mentioned in old talks and docs that you find on google is superseded by some others...)
What are some alternatives?
Flux.jl - Relax! Flux is the ML library that doesn't make you tensor
CxxWrap.jl - Package to make C++ libraries available in Julia
Enzyme - High-performance automatic differentiation of LLVM and MLIR.
jluna - Julia Wrapper for C++ with Focus on Safety, Elegance, and Ease of Use
julia - The Julia Programming Language
Torch.jl - Sensible extensions for exposing torch in Julia.
Enzyme.jl - Julia bindings for the Enzyme automatic differentiator
LibTIFF.jl - Clang.jl generated wrapper around Libtiff_jll.jl
StatsBase.jl - Basic statistics for Julia
threads - Threads for Lua and LuaJIT. Transparent exchange of data between threads is allowed thanks to torch serialization.
BetaML.jl - Beta Machine Learning Toolkit
Distributions.jl - A Julia package for probability distributions and associated functions.