What does HackerNews think of jax?

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

Language: Python

MatX: Efficient C++17 GPU numerical computing library with Python-like syntax | Oct 2023
> Python/Numpy: 5360ms (Xeon(R) CPU E5-2698 v4 @ 2.20GHz) CuPy: 10.6ms (A100) MatX: 2.54ms (A100) >

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 and for GPU comparison focus to compare vs CuPy.

[1] https://github.com/google/jax

The C++ Killers (Not You, Rust) | Feb 2023
I've been noticing more and more ML projects such as hugging face adding support for JAX which makes Python have automatic differentiation which is a key feature of Julia.

https://github.com/google/jax

Google Calls In Help From Larry Page and Sergey Brin for A.I. Fight | Jan 2023
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> I love their JAX

For those out of loop: https://github.com/google/jax

See also: https://news.ycombinator.com/item?id=29682507
Why TensorFlow for Python is dying a slow death | Jan 2023
JAX should be mentioned [1]. It's also from Google and is getting popular these days. Not PyTorch-popular, but the progress and investment seem promissing.

[1] https://github.com/google/jax

Python 3.11 is faster than 3.8 | Oct 2022
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You should give JAX a go.

https://github.com/google/jax