jax VS rich

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.

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...

The design of MLX is inspired by frameworks like NumPy, PyTorch, Jax, and ArrayFire.

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

>

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

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!

For a similar "benchmark" (also Mandelbrot) but took place in Jax repo discussion: https://github.com/google/jax/discussions/11078#discussionco...

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 *

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!

There is an open issue [1] on GitHub to make it more modular and get rid of markdown and syntax highlighting but I have no hope for rich to get more minimal.

[1]: https://github.com/Textualize/rich/issues/2277

I hope he doesn't mind, but the creator of Rich and Textualize is a good guy, and Scottish: https://www.willmcgugan.com/about/

https://www.textualize.io/

https://github.com/Textualize/rich

They keep getting improved error messaging and this is one of my favorite features. But I'd love if we could get some real rich text. Idk if anyone else uses rich, but it has infected all my programs now. Not just to print with colors, but because it makes debugging so much easier. Not just print(f"{var=}") but the handler[0,1]. Color is so important to these types of things and so is formatting. Plus, the progress bars are nice and have almost completely replaced tqdm for me[2]. They're just easier and prettier.

[0] https://rich.readthedocs.io/en/stable/logging.html

[1] Try this example: https://github.com/Textualize/rich/blob/master/examples/exce...

[2] Side note: does anyone know how to get these properly working when using DDP with pytorch? I get flickering when using this and I think it is actually down to a pytorch issue and how they're handling their loggers and flushing the screen. I know pytorch doesn't want to depend on rich, but hey, pip uses rich so why shouldn't everyone?

colors.crumb extends Crumb with basic terminal styling functions and RGB, HEX, ANSI conversion functions. It is in the realm of JavaScript's chalk and Python's rich but slightly more functional 😉.

I am working on a new python project and one of the first things I added was https://github.com/Textualize/rich because of how easy it is to make things look good in the terminal.

I am not rewriting anything but I'd love to have a library like `rich` in Rust: https://github.com/textualize/rich

Add some cool-looking stuff to your output with rich.

You might consider taking inspiration from the rich module. In particular, I like how rich supports inline color theming which seems much more cumbersome in your framework, requiring the use of context managers as well as familiarity with how your framework structures color objects. Other than that though, I'm impressed!

Today I released coBib 4.0, my console bibliography manager written in Python, which now uses rich and textual to provide a cohesive and modern user experience in both its CLI and TUI.