rune
Flux.jl
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rune | Flux.jl | |
---|---|---|
21 | 22 | |
1,535 | 4,386 | |
2.8% | 0.9% | |
9.0 | 8.7 | |
2 days ago | 2 days ago | |
Rust | Julia | |
Apache License 2.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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.
rune
- RustPython
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Steel – An embedded scheme interpreter in Rust
A Lisp, a weird dialect of Lisp, is not better than Lua. Why use Rune [0]?!
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Embeddable Scripting Language for Embedded Rust
This is what I based my comment on - https://github.com/rune-rs/rune/issues/444
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-🎄- 2022 Day 13 Solutions -🎄-
Late start today as well. I really thought today would be the day that I'd have to abandon my goal of no heap allocations. But, luckily I had an arena allocator available that I could fairly easily adapt to store data on the stack. And with some tweaks we have today's solution:
- ᚣ the Rune Programming Language
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thought you guys might like this monstrosity i created (that i actually use in a project)
I'd have given you bonus points for using a rust styled scripting language like rune but that's pretty neat still
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Visual scripting for Rust
As note about using rust syntax for scripting: https://rune-rs.github.io/
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Designing a Rust -> Rust plugin system
I know you said you don’t want to embed another language but IMO Rune is worth a consideration here. It can be a pretty thin abstraction over rust by passing native structs to scripts and calling methods on them. The syntax and semantics are very close to rust so it feels natural. https://github.com/rune-rs/rune
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Rune vs Rhai?
The biggest technical difference I'd say is that Rune uses a stack-based machine which makes adding deep C support somewhat obvious while Rhai performs AST walking to execute scripts.
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How a Single Line of Code Made a 24-core Server Slower Than a Laptop
Here is the repro I ended up writing to validate the problem if anyone wants to take it for a spin.
Flux.jl
- Julia 1.10 Released
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What Apple hardware do I need for CUDA-based deep learning tasks?
If you are really committed to running on Apple hardware then take a look at Tensorflow for macOS. Another option is the Julia programming language which has very basic Metal support at a CUDA-like level. FluxML would be the ML framework in Julia. I’m not sure either option will be painless or let you do everything you could do with a Nvidia GPU.
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[D] ClosedAI license, open-source license which restricts only OpenAI, Microsoft, Google, and Meta from commercial use
Flux dominance!
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What would be your programming language of choice to implement a JIT compiler ?
I’m no compiler expert but check out flux and zygote https://fluxml.ai/ https://fluxml.ai/
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Any help or tips for Neural Networks on Computer Clusters
I would suggest you to look into Julia ecosystem instead of C++. Julia is almost identical to Python in terms of how you use it but it's still very fast. You should look into flux.jl package for Julia.
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[D] Why are we stuck with Python for something that require so much speed and parallelism (neural networks)?
Give Julia a try: https://fluxml.ai
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Deep Learning With Flux: Loss Doesn't Converge
2) Flux treats softmax a little different than most other activation functions (see here for more details) such as relu and sigmoid. When you pass an activation function into a layer like Dense(3, 32, relu), Flux expects that the function is broadcast over the layer's output. However, softmax cannot be broadcast as it operates over vectors rather than scalars. This means that if you want to use softmax as the final activation in your model, you need to pass it into Chain() like so:
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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.
- Flux: The Elegant Machine Learning Stack
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Jax vs. Julia (Vs PyTorch)
> In his item #1, he links to https://discourse.julialang.org/t/loaderror-when-using-inter... The issue is actually a Zygote bug, a Julia package for auto-differentiation, and is not directly related to Julia codebase (or Flux package) itself. Furthermore, the problematic code is working fine now, because DiffEqFlux has switched to Enzyme, which doesn't have that bug. He should first confirm whether the problem he is citing is actually a problem or not.
> Item #2, again another Zygote bug.
If flux chose a buggy package as a dependency, that's on them, and users are well justified in steering clear of Flux if it has buggy dependencies. As of today, the Project.toml for both Flux and DiffEqFlux still lists Zygote as a dependency. Neither list Enzyme.
What are some alternatives?
Rhai - Rhai - An embedded scripting language for Rust.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
RustPython - A Python Interpreter written in Rust
Knet.jl - Koç University deep learning framework.
mun - Source code for the Mun language and runtime.
tensorflow - An Open Source Machine Learning Framework for Everyone
miniserve - 🌟 For when you really just want to serve some files over HTTP right now!
Transformers.jl - Julia Implementation of Transformer models
miri - An interpreter for Rust's mid-level intermediate representation
Lux.jl - Explicitly Parameterized Neural Networks in Julia
dyon - A rusty dynamically typed scripting language
Torch.jl - Sensible extensions for exposing torch in Julia.