jax
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jax | wasmer | |
---|---|---|
82 | 131 | |
27,842 | 17,735 | |
3.6% | 3.5% | |
10.0 | 9.9 | |
6 days ago | 5 days ago | |
Python | Rust | |
Apache License 2.0 | MIT License |
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.
jax
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The Elements of Differentiable Programming
The dual numbers exist just as surely as the real numbers and have been used well over 100 years
https://en.m.wikipedia.org/wiki/Dual_number
Pytorch has had them for many years.
https://pytorch.org/docs/stable/generated/torch.autograd.for...
JAX implements them and uses them exactly as stated in this thread.
https://github.com/google/jax/discussions/10157#discussionco...
As you so eloquently stated, "you shouldn't be proclaiming things you don't actually know on a public forum," and doubly so when your claimed "corrections" are so demonstrably and totally incorrect.
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Julia GPU-based ODE solver 20x-100x faster than those in Jax and PyTorch
On your last point, as long as you jit the topmost level, it doesn't matter whether or not you have inner jitted functions. The end result should be the same.
Source: https://github.com/google/jax/discussions/5199#discussioncom...
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Apple releases MLX for Apple Silicon
The design of MLX is inspired by frameworks like NumPy, PyTorch, Jax, and ArrayFire.
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MLPerf training tests put Nvidia ahead, Intel close, and Google well behind
I'm still not totally sure what the issue is. Jax uses program transformations to compile programs to run on a variety of hardware, for example, using XLA for TPUs. It can also run cuda ops for Nvidia gpus without issue: https://jax.readthedocs.io/en/latest/installation.html
There is also support for custom cpp and cuda ops if that's what is needed: https://jax.readthedocs.io/en/latest/Custom_Operation_for_GP...
I haven't worked with float4, but can imagine that new numerical types would require some special handling. But I assume that's the case for any ml environment.
But really you probably mean fixed point 4bit integer types? Looks like that has had at least some work done in Jax: https://github.com/google/jax/issues/8566
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MatX: Efficient C++17 GPU numerical computing library with Python-like syntax
>
Are they even comparing apples to apples to claim that they see these improvements over NumPy?
> While the code complexity and length are roughly the same, the MatX version shows a 2100x over the Numpy version, and over 4x faster than the CuPy version on the same GPU.
NumPy doesn't use GPU by default unless you use something like Jax [1] to compile NumPy code to run on GPUs. I think more honest comparison will mainly compare MatX running on same CPU like NumPy as focus the GPU comparison against CuPy.
[1] https://github.com/google/jax
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JAX – NumPy on the CPU, GPU, and TPU, with great automatic differentiation
Actually that never changed. The README has always had an example of differentiating through native Python control flow:
https://github.com/google/jax/commit/948a8db0adf233f333f3e5f...
The constraints on control flow expressions come from jax.jit (because Python control flow can't be staged out) and jax.vmap (because we can't take multiple branches of Python control flow, which we might need to do for different batch elements). But autodiff of Python-native control flow works fine!
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Julia and Mojo (Modular) Mandelbrot Benchmark
For a similar "benchmark" (also Mandelbrot) but took place in Jax repo discussion: https://github.com/google/jax/discussions/11078#discussionco...
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Functional Programming 1
2. https://github.com/fantasyland/fantasy-land (A bit heavy on jargon)
Note there is a python version of Ramda available on pypi and there’s a lot of FP tidbits inside JAX:
3. https://pypi.org/project/ramda/ (Worth making your own version if you want to learn, though)
4. For nested data, JAX tree_util is epic: https://jax.readthedocs.io/en/latest/jax.tree_util.html and also their curry implementation is funny: https://github.com/google/jax/blob/4ac2bdc2b1d71ec0010412a32...
Anyway don’t put FP on a pedestal, main thing is to focus on the core principles of avoiding external mutation and making helper functions. Doesn’t always work because some languages like Rust don’t have legit support for currying (afaik in 2023 August), but in those cases you can hack it with builder methods to an extent.
Finally, if you want to understand the middle of the midwit meme, check out this wiki article and connect the free monoid to the Kleene star (0 or more copies of your pattern) and Kleene plus (1 or more copies of your pattern). Those are also in regex so it can help you remember the regex symbols. https://en.wikipedia.org/wiki/Free_monoid?wprov=sfti1
The simplest example might be {0}^* in which case
0: “” // because we use *
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Best Way to Learn JAX
Hello! I'm trying to learn JAX over the next couple of weeks. Ideally, I want to be comfortable with using it for projects after about 3 weeks to a month, although I understand that may not be realistic. I currently have experience with PyTorch and TensorFlow. How should I go about learning JAX? Is there a specific YouTube tutorial or online course I should use, or should I just use the tutorial on https://jax.readthedocs.io/? Any information, advice, or experience you can share would be much appreciated!
- Codon: Python Compiler
wasmer
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Bebop v3: a fast, modern replacement to Protocol Buffers
This is awesome. I'd love to have upstream support in Wasmer ( https://wasmer.io )
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Unlocking the Power of WebAssembly
WebAssembly is extremely portable. WebAssembly runs on: all major web browsers, V8 runtimes like Node.js, and independent Wasm runtimes like Wasmtime, Lucet, and Wasmer.
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Show HN: dockerc – Docker image to static executable "compiler"
Unfortunately cosmopolitan wouldn't work for dockerc. Cosmopolitan works as long as you only use it but container runtimes require additional features. Also containers contain arbitrary executables so not sure how that would work either...
As for WASM, this is already possible using container2wasm[0] and wasmer[1]'s ability to generate static binaries.
[0]: https://github.com/ktock/container2wasm
[1]: https://wasmer.io/
- RustPython
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Howto: WASM runtimes in Docker / Colima
I could not find any guide how to add WASM container capability to Docker running on Colima. This guide provides a few Colima templates for exactly this, which adds WasmEdge, Wasmtime and Wasmer runtime types.
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Show HN: Mutable.ai – Turn your codebase into a Wiki
Just suggested as well Wasmer on Twitter! https://github.com/wasmerio/wasmer
Looking forward to seeing the results :)
- Jaq – A jq clone focused on correctness, speed, and simplicity
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Prettier $20k Bounty was Claimed
The Biome team has been incredibly fast on solving the challenge and achieving 95% compatibility with Prettier [1]
Just as a note, as it was not mentioned in the article, Wasmer [2] also participated with a $2,500 bounty to compile Biome to WASIX [3], and it has been awesome to see how their team has been working to achieve this as well... hopefully we'll get Biome running in Wasmer soon!
Keep up the great work!!
[1] https://github.com/biomejs/biome/issues/720
[2] https://wasmer.io/
[3] https://wasix.org/
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The Curse of Docker
It's funny how WebAssembly can help overcome most of the issues mentioned on the blogpost (packaging, configuration, portability) if addressed properly.
That's the main reason Wasmer [1] was created :)
[1] https://wasmer.io
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Bring garbage collected programming languages efficiently to WebAssembly
Thanks for the mention to Wasmer.
I'll put here a link in case is useful for future readers: https://wasmer.io/
What are some alternatives?
Numba - NumPy aware dynamic Python compiler using LLVM
wasmtime - A fast and secure runtime for WebAssembly
functorch - functorch is JAX-like composable function transforms for PyTorch.
SSVM - WasmEdge is a lightweight, high-performance, and extensible WebAssembly runtime for cloud native, edge, and decentralized applications. It powers serverless apps, embedded functions, microservices, smart contracts, and IoT devices.
julia - The Julia Programming Language
wasm3 - 🚀 A fast WebAssembly interpreter and the most universal WASM runtime
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
quickjs-emscripten - Safely execute untrusted Javascript in your Javascript, and execute synchronous code that uses async functions
Cython - The most widely used Python to C compiler
awesome-wasm-runtimes - A list of webassemby runtimes
jax-windows-builder - A community supported Windows build for jax.
wasm-bindgen - Facilitating high-level interactions between Wasm modules and JavaScript