Enzyme
Nim
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Enzyme | Nim | |
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16 | 346 | |
1,153 | 16,060 | |
3.0% | 0.8% | |
9.6 | 9.9 | |
6 days ago | 3 days ago | |
LLVM | Nim | |
GNU General Public License v3.0 or later | 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.
Enzyme
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Show HN: Curve Fitting Bezier Curves in WASM with Enzyme Ad
Automatic differentiation is done using https://enzyme.mit.edu/
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Ask HN: What Happened to TensorFlow Swift
lattner left google and was the primary reason they chose swift, so they lost interest.
if you're asking from an ML perspective, i believe the original motivation was to incorporate automatic differentiation in the swift compiler. i believe enzyme is the spiritual successor.
https://github.com/EnzymeAD/Enzyme
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Show HN: Port of OpenAI's Whisper model in C/C++
https://ispc.github.io/ispc.html
For the auto-differentiation when I need performance or memory, I currently use tapenade ( http://tapenade.inria.fr:8080/tapenade/index.jsp ) and/or manually written gradient when I need to fuse some kernel, but Enzyme ( https://enzyme.mit.edu/ ) is also very promising.
MPI for parallelization across machines.
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Do you consider making a physics engine (for RL) worth it?
For autodiff, we are currently working again on publishing a new Enzyme (https://enzyme.mit.edu) Frontend for Rust which can also handle pure Rust types, first version should be done in ~ a week.
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What is a really cool thing you would want to write in Rust but don't have enough time, energy or bravery for?
Have you taken a look at enzymeAD? There is a group porting it to rust.
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The Julia language has a number of correctness flaws
Enzyme dev here, so take everything I say as being a bit biased:
While, by design Enzyme is able to run very fast by operating within the compiler (see https://proceedings.neurips.cc/paper/2020/file/9332c513ef44b... for details) -- it aggressively prioritizes correctness. Of course that doesn't mean that there aren't bugs (we're only human and its a large codebase [https://github.com/EnzymeAD/Enzyme], especially if you're trying out newly-added features).
Notably, this is where the current rough edges for Julia users are -- Enzyme will throw an error saying it couldn't prove correctness, rather than running (there is a flag for "making a best guess, but that's off by default"). The exception to this is garbage collection, for which you can either run a static analysis, or stick to the "officially supported" subset of Julia that Enzyme specifies.
Incidentally, this is also where being a cross-language tool is really nice -- namely we can see edge cases/bug reports from any LLVM-based language (C/C++, Fortran, Swift, Rust, Python, Julia, etc). So far the biggest code we've handled (and verified correctness for) was O(1million) lines of LLVM from some C++ template hell.
I will also add that while I absolutely love (and will do everything I can to support) Enzyme being used throughout arbitrary Julia code: in addition to exposing a nice user-facing interface for custom rules in the Enzyme Julia bindings like Chris mentioned, some Julia-specific features (such as full garbage collection support) also need handling in Enzyme.jl, before Enzyme can be considered an "all Julia AD" framework. We are of course working on all of these things (and the more the merrier), but there's only a finite amount of time in the day. [^]
[^] Incidentally, this is in contrast to say C++/Fortran/Swift/etc, where Enzyme has much closer to whole-language coverage than Julia -- this isn't anything against GC/Julia/etc, but we just have things on our todo list.
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Jax vs. Julia (Vs PyTorch)
Idk, Enzyme is pretty next gen, all the way down to LLVM code.
https://github.com/EnzymeAD/Enzyme
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What's everyone working on this week (7/2022)?
I'm working on merging my build-tool for (oxide)-enzyme into Enzyme itself. Also looking into improving the documentation.
- Wsmoses/Enzyme: High-performance automatic differentiation of LLVM
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Trade-Offs in Automatic Differentiation: TensorFlow, PyTorch, Jax, and Julia
that seems one of the points of enzyme[1], which was mentioned in the article.
[1] - https://enzyme.mit.edu/
being able in effect do interprocedural cross language analysis seems awesome.
Nim
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Top Paying Programming Technologies 2024
22. Nim - $80,000
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"14 Years of Go" by Rob Pike
I think the right answer to your question would be NimLang[0]. In reality, if you're seeking to use this in any enterprise context, you'd most likely want to select the subset of C++ that makes sense for you or just use C#.
[0]https://nim-lang.org/
- Odin Programming Language
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Ask HN: Interest in a Rust-Inspired Language Compiling to JavaScript?
I don't think it's a rust-inspired language, but since it has strong typing and compiles to javascript, did you give a look at nim [0] ?
For what it takes, I find the language very expressive without the verbosity in rust that reminds me java. And it is also very flexible.
[0] : https://nim-lang.org/
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The nim website and the downloads are insecure
I see a valid cert for https://nim-lang.org/
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Nim
FYI, on the front page, https://nim-lang.org, in large type you have this:
> Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula.
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Things I've learned about building CLI tools in Python
You better off with using a compiled language.
If you interested in a language that's compiled, fast, but as easy and pleasant as Python - I'd recommend you take a look at [Nim](https://nim-lang.org).
And to prove what Nim's capable of - here's a cool repo with 100+ cli apps someone wrote in Nim: [c-blake/bu](https://github.com/c-blake/bu)
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Mojo is now available on Mac
Chapel has at least several full-time developers at Cray/HPE and (I think) the US national labs, and has had some for almost two decades. That's much more than $100k.
Chapel is also just one of many other projects broadly interested in developing new programming languages for "high performance" programming. Out of that large field, Chapel is not especially related to the specific ideas or design goals of Mojo. Much more related are things like Codon (https://exaloop.io), and the metaprogramming models in Terra (https://terralang.org), Nim (https://nim-lang.org), and Zig (https://ziglang.org).
But Chapel is great! It has a lot of good ideas, especially for distributed-memory programming, which is its historical focus. It is more related to Legion (https://legion.stanford.edu, https://regent-lang.org), parallel & distributed Fortran, ZPL, etc.
- NIR: Nim Intermediate Representation
What are some alternatives?
Zygote.jl - 21st century AD
zig - General-purpose programming language and toolchain for maintaining robust, optimal, and reusable software.
Flux.jl - Relax! Flux is the ML library that doesn't make you tensor
go - The Go programming language
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
Odin - Odin Programming Language
Lux.jl - Explicitly Parameterized Neural Networks in Julia
rust - Empowering everyone to build reliable and efficient software.
linfa - A Rust machine learning framework.
crystal - The Crystal Programming Language
faust - Functional programming language for signal processing and sound synthesis
v - Simple, fast, safe, compiled language for developing maintainable software. Compiles itself in <1s with zero library dependencies. Supports automatic C => V translation. https://vlang.io