Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more (by google)

Jax Alternatives

Similar projects and alternatives to jax

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a better jax alternative or higher similarity.

jax reviews and mentions

Posts with mentions or reviews of jax. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-03-22.
  • The Elements of Differentiable Programming
    5 projects | news.ycombinator.com | 22 Mar 2024
    The dual numbers exist just as surely as the real numbers and have been used well over 100 years


    Pytorch has had them for many years.


    JAX implements them and uses them exactly as stated in this thread.


    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.

  • Julia GPU-based ODE solver 20x-100x faster than those in Jax and PyTorch
    6 projects | news.ycombinator.com | 23 Dec 2023
    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...

  • Apple releases MLX for Apple Silicon
    4 projects | /r/LocalLLaMA | 8 Dec 2023
    The design of MLX is inspired by frameworks like NumPy, PyTorch, Jax, and ArrayFire.
  • MLPerf training tests put Nvidia ahead, Intel close, and Google well behind
    1 project | news.ycombinator.com | 14 Nov 2023
    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

  • MatX: Efficient C++17 GPU numerical computing library with Python-like syntax
    5 projects | news.ycombinator.com | 3 Oct 2023

    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

  • JAX – NumPy on the CPU, GPU, and TPU, with great automatic differentiation
    12 projects | news.ycombinator.com | 28 Sep 2023
    Actually that never changed. The README has always had an example of differentiating through native Python control flow:


    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!

  • Julia and Mojo (Modular) Mandelbrot Benchmark
    10 projects | news.ycombinator.com | 8 Sep 2023
    For a similar "benchmark" (also Mandelbrot) but took place in Jax repo discussion: https://github.com/google/jax/discussions/11078#discussionco...
  • Functional Programming 1
    3 projects | news.ycombinator.com | 16 Aug 2023
    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 *

  • Best Way to Learn JAX
    1 project | /r/learnmachinelearning | 13 May 2023
    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
    9 projects | news.ycombinator.com | 8 May 2023
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    www.influxdata.com | 27 May 2024
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Basic jax repo stats
5 days ago

google/jax is an open source project licensed under Apache License 2.0 which is an OSI approved license.

The primary programming language of jax is Python.

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