Enzyme
Dagger.jl
Enzyme | Dagger.jl | |
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16 | 4 | |
1,159 | 581 | |
1.6% | 1.2% | |
9.7 | 8.9 | |
3 days ago | 2 days ago | |
LLVM | Julia | |
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.
Dagger.jl
- Dagger: a new way to build CI/CD pipelines
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DTable a new distributed table implementation in Julia using Dagger.jl
Firstly, I'll say that we already have work started to implement out-of-core directly in Dagger: https://github.com/JuliaParallel/Dagger.jl/pull/289.
With that PR in place, it should be possible to define a "storage device" which is backed by a database. I haven't had a chance to actually try this, since the PR still needs quite some work and testing, but it's definitely something on my radar!
- From Julia to Rust
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Cerebras’ New Monster AI Chip Adds 1.4T Transistors
I'm not sure that's necessarily the domain of a low-level package like CUDA.jl though (which I assume you're referring to). That kind of interface is more the domain of higher-level packages like https://github.com/JuliaParallel/Dagger.jl/ and to a lesser extent https://juliagpu.github.io/KernelAbstractions.jl/stable/. Moreover, the jury is still out on whether the built-in Distributed module is an ideal abstraction for every use-case (clusters, heterogeneous compute, etc.)
WRT Nx, my biggest question is how they'll crack the problem of still needing big balls of C++ and the shims everywhere to get acceleration. Creating a compiler that generates efficient GPU or other accelerator code is a massive research project with no clear winners, never mind the challenge of reconciling the very mutation-heavy needs of GPU compute with a mostly immutable language model.
What are some alternatives?
Zygote.jl - 21st century AD
earthly - Super simple build framework with fast, repeatable builds and an instantly familiar syntax – like Dockerfile and Makefile had a baby.
Flux.jl - Relax! Flux is the ML library that doesn't make you tensor
julia - The Julia Programming Language
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
DuckDB.jl
Lux.jl - Explicitly Parameterized Neural Networks in Julia
determined - Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.
linfa - A Rust machine learning framework.
Metatheory.jl - General purpose algebraic metaprogramming and symbolic computation library for the Julia programming language: E-Graphs & equality saturation, term rewriting and more.
faust - Functional programming language for signal processing and sound synthesis
Symbolics.jl - Symbolic programming for the next generation of numerical software