tealsql
Enzyme
tealsql | Enzyme | |
---|---|---|
7 | 16 | |
11 | 1,157 | |
- | 1.5% | |
8.3 | 9.7 | |
10 days ago | 7 days ago | |
Rust | LLVM | |
- | 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.
tealsql
-
Tealr 0.8 just released. Document your lua apis!
A project that uses tealr can be found at https://github.com/lenscas/tealsql/tree/master/pgteal, with online documentation available over at https://lenscas.github.io/tealsql/
-
Man, I love this language.
There is also https://github.com/lenscas/tealsql which is used as kind of showcase project for tealr and tealr_doc_gen. While at the same time (hopefully) filling a pain point in the lua/teal eco system.
-
What's everyone working on this week (7/2022)?
tealsql, my sql library for lua: Right now, it is being used to dog feed the changes in tealr. So, mostly improving its documentation, and preparing it to release it.
- What's everyone working on this week (6/2022)?
-
What's everyone working on this week (39/2021)?
last weekend I put in some more time in tealsql, a postgresql client written in Rust for lua/teal.
-
What's everyone working on this week (33/2021)?
The main focus is my sql client for teal/lua https://github.com/lenscas/tealsql , mainly getting rid of every part in the api that doesn't have a good type yet on the teal side of things (any, {any:any}, etc.
-
What's everyone working on this week (29/2021)?
tealsql, an sql client for teal/lua. Current plan is to get the async api finished (right now it ignores every error but otherwise works without problems).
Enzyme
-
Show HN: Curve Fitting Bezier Curves in WASM with Enzyme Ad
Automatic differentiation is done using https://enzyme.mit.edu/
-
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
-
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.
-
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.
-
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.
-
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.
-
Jax vs. Julia (Vs PyTorch)
Idk, Enzyme is pretty next gen, all the way down to LLVM code.
https://github.com/EnzymeAD/Enzyme
-
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
-
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.
What are some alternatives?
youki - A container runtime written in Rust
Zygote.jl - 21st century AD
koto - A simple, expressive, embeddable programming language, made with Rust
Flux.jl - Relax! Flux is the ML library that doesn't make you tensor
tealr - A wrapper around mlua and rlua to generate documentation and other helpers
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
rhyme-es
Lux.jl - Explicitly Parameterized Neural Networks in Julia
synth - The Declarative Data Generator
linfa - A Rust machine learning framework.
txrx
faust - Functional programming language for signal processing and sound synthesis