Enzyme
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Enzyme | yew | |
---|---|---|
16 | 200 | |
1,150 | 29,888 | |
2.8% | 1.0% | |
9.6 | 8.6 | |
7 days ago | 7 days ago | |
LLVM | MDX | |
GNU General Public License v3.0 or later | Apache License 2.0 |
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.
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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.
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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.
yew
- Container2wasm: Convert Containers to WASM Blobs
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Show HN: Game of Life with grid editor in browser with Rust and WASM
I coded up a game of life implementation in rust and web assembly using https://yew.rs/ as an effort to sharpen my rust skills and it resulted as a fun toy. You can find the source here: https://gitlab.com/reedrichards/wvdom Enjoy!
- The best WebAssembly runtime may be no runtime at all
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Recreating the Apple Calculator in Rust using Tauri, Yew and Tailwind
UI template: Yew - (https://yew.rs/)
- Yew: Rust / WASM framework for creating reliable and efficient web applications
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Ask HN: If you were to build a web app today what tech stack would you choose?
I'm surprised nobody said they'd use [Yew](https://yew.rs/), especially given the premise of this being for a passion project.
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Semantics of method which takes Rc<Self> and returns Rc<Self>?
This example shows updating an existing state: https://github.com/yewstack/yew/blob/04909dd942eb64285652d96a2621bdf7be3fa912/examples/timer_functional/src/main.rs
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Speeding up the JavaScript ecosystem – Polyfills gone rogue
1. Not related to npm, but related to the web.
2. True, but compilers are generally better than transpilers.
3. Have you seen https://yew.rs/ ?
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Is it possible to create Android apps using Rust?
You could use Yew to write web apps, which can be opened on Android phones.
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Crate Suggestions for Web Frontend
What about Yew and Iced?
What are some alternatives?
Zygote.jl - 21st century AD
Seed - A Rust framework for creating web apps
Flux.jl - Relax! Flux is the ML library that doesn't make you tensor
dioxus - Fullstack GUI library for web, desktop, mobile, and more.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
sycamore - A library for creating reactive web apps in Rust and WebAssembly
Lux.jl - Explicitly Parameterized Neural Networks in Julia
leptos - Build fast web applications with Rust.
linfa - A Rust machine learning framework.
Rocket - A web framework for Rust.
faust - Functional programming language for signal processing and sound synthesis
tauri - Build smaller, faster, and more secure desktop applications with a web frontend.