rust-gc
Flux.jl
rust-gc | Flux.jl | |
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10 | 22 | |
913 | 4,393 | |
- | 0.4% | |
3.9 | 8.7 | |
7 months ago | 1 day ago | |
Rust | Julia | |
Mozilla Public License 2.0 | GNU General Public License v3.0 or later |
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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.
rust-gc
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loxcraft: a compiler, language server, and online playground for the Lox programming language
rust-langdev has a lot of libraries for building compilers in Rust. Perhaps you could use these to make your implementation easier, and revisit it later if you want to build things from scratch. I'd suggest logos for lexing, LALRPOP / chumsky for parsing, and rust-gc for garbage collection.
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What would be your programming language of choice to implement a JIT compiler ?
There's nothing stopping you from doing that in Rust. See rust-gc for an example of a GC implemented in Rust. Another example is mozjs, which is Rust bindings to SpiderMonkey. The GC there is implemented in C++, but it shows how you'd structure wrapper types for GC'd pointers in Rust so that you can use them safely, even with all the "ugliness" of a browser-grade GC.
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Spotting and Avoiding Heap Fragmentation in Rust Apps
One can have a GC as a library, https://github.com/Manishearth/rust-gc
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Hey Rustaceans! Got a question? Ask here (7/2023)!
The ones I am aware of are gc and broom. None will be as simple to use as the one in old Rust as userland implementations don't have the benefit of first-class integrated compiler support.
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Chris Lattner on garbage collection vs. Automatic Reference Counting (2017)
Rust has rust-gc, which is an attempt to add opt-in GC over Rust's more traditional automatic memory management. It's a neat project, but I'm not sure where it's actually being used.
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I have programming skills! I am good at dealing with programs!
Inb4 "already exists": the question is about making it convenient, not just imlementing the behavior.
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how hard is rust for a javascript programmer?
There is also a library implementation of garbage collection for Rust, made by someone from the Rust core team.
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Is this the correct way to think about Rust? Correct me if I am wrong about anything.
Yep! And I'd actually fully agree those are garbage collection, there's also a crate by Manish which does """real""" garbage collection—https://github.com/Manishearth/rust-gc
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Garbage Collection Question.
I don't know that I'd say it "works" - it's never a technique I've needed to use myself, but it's the approach taken by e.g. https://github.com/Manishearth/rust-gc
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Microsoft Rust intro says "Rust is known to leak memory"
Anyway, I found something recent that implements "rc" but in terms of tracing: https://github.com/Manishearth/rust-gc/ . Maybe useful for projects involving graphs of objects.
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.
https://github.com/FluxML/Flux.jl/blob/master/Project.toml
What are some alternatives?
rfcs - RFCs for changes to Rust
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
unsafe-code-guidelines - Forum for discussion about what unsafe code can and can't do
Knet.jl - Koç University deep learning framework.
tokio - A runtime for writing reliable asynchronous applications with Rust. Provides I/O, networking, scheduling, timers, ...
tensorflow - An Open Source Machine Learning Framework for Everyone
its_rusty - learning rust
Transformers.jl - Julia Implementation of Transformer models
Primes - Prime Number Projects in C#/C++/Python
Torch.jl - Sensible extensions for exposing torch in Julia.
ixy-languages - A high-speed network driver written in C, Rust, C++, Go, C#, Java, OCaml, Haskell, Swift, Javascript, and Python
Lux.jl - Explicitly Parameterized Neural Networks in Julia