einops VS productdiv

Not sure if the wrapper you’re talking about is your own custom code, but I really like using einops lately. It’s got similar axis naming capabilities and it dispatches to both numpy and pytorch

http://einops.rocks/

Thanks for the pointer. I can believe that a language that looks so different will find that different patterns and primitives are natural for it.

My experience from writing a lot of array-based code in NumPy/Matlab is that broadcasting absolutely has made it easier to write my code in those ecosystems. Axes of length 1 have often been in the right places already, or have been easy to insert. It's of course possible to create a big mess in any language; it seems likely that the NumPy code you saw could have been neater too.

In machine learning there can be many array dimensions floating around: batch-dims, sequence and/or channel-dims, weight matrices, and so on. It can be necessary to expand two or more dimensions, and/or line up dimensions quite carefully. Einops[1] has emerged from that community as a tool to succinctly express many operations that involve lots of array dimensions. You're likely to bump into more and more people who've used it, and again it seems there's some overlap with what Rank does. (And again, you'll see uses of Einops in the wild that are unnecessarily convoluted.)

[1] https://einops.rocks/ -- It works with all of the existing major array-based frameworks for Python (NumPy/PyTorch/Jax/etc), and the emerging array API standard for Python.

this is definitely what you're looking for: https://github.com/arogozhnikov/einops

Einops all the things! https://einops.rocks/

It depends on what are their main purposes. I know some figures who have done an amazing job in this field but never because of publications, e.g. https://github.com/lucidrains and https://github.com/rwightman, https://einops.rocks/

On tsalib's warp: this is very similar to einops. I think it might even be slightly more general. However, I'm honestly not sure to what extent tsalib is still maintained, as it looks like the most recent commit was about two years ago. (Which is a shame.)

Also see Einops: https://github.com/arogozhnikov/einops, which uses a einsum-like notation for various tensor operations used in deep learning.

https://einops.rocks/pytorch-examples.html shows how it can be used to implement various neural network architectures in a more simplified manor.

This combined with something like einops [1] ( intuitive reshaping library) can be a huge time saver.

[1] https://github.com/arogozhnikov/einops

I started work on ProductDiv in July of 2021. It went through many design iterations to finally become a productive tool for web development.

While not quite ready for public consumption, I'm working on a project that allows front end developers to build websites visually with templates and utility classes called ProductDiv https://github.com/awestbro/productdiv.

As a web developer of ~10 years I wanted a tool that could match the productivity of something like Elementor while producing clean code. There are a few other tools out there, but I wanted something that:

- Focused on utility classes and clean code

- Is extensible with your own templates

- Local config, not service based so everything is version controlled and always available for all members of your team

- Framework agnostic, but configurable for utility classes (currently bootstrap) with any framework that produces HTML

Visually building and sites is a game changer for me as I can save a ton of time dragging templates onto pages while making small adjustments with the utility class editor.

To give it a try, you can download the repo, npm i, and npm run dev. I'd love some feedback on UX!

I’m actually launching a low-code tool specifically for this soon. I have a similar full stack background and decided to make my own low code front end framework where you can drag and drop prebuilt components on the page. Works with any framework as well: https://github.com/awestbro/productdiv