Einops Alternatives

einops reviews and mentions

• Einops Rocks
  1 project | news.ycombinator.com | 15 Nov 2024
• Top 5 Production-Ready Open Source AI Libraries for Engineering Teams
  7 projects | dev.to | 4 Oct 2024

  PyTorch’s ease of use and flexibility, distributed processing, and cloud support make it a good choice for companies looking for open source production-ready solutions. It also has a large ecosystem of tools, such as ParlAI, EinOps, and Accelerate, and a very welcoming community on Slack and PyTorchDiscuss.

• NumPy 2.0.0
  13 projects | news.ycombinator.com | 16 Jun 2024

  https://einops.rocks/#why-use-einops-notation

• Einsum in 40 Lines of Python
  6 projects | news.ycombinator.com | 27 Apr 2024

  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/

• Einops: Flexible and powerful tensor operations for readable and reliable code
  1 project | news.ycombinator.com | 12 Dec 2023
• Yorick is an interpreted programming language for scientific simulations
  3 projects | news.ycombinator.com | 3 Nov 2023

  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.

• Torch qeuivalent to image_to_array (keras)
  1 project | /r/MLQuestions | 16 Apr 2023

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

• [D] Have their been any attempts to create a programming language specifically for machine learning?
  12 projects | /r/MachineLearning | 11 Feb 2023

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

• Delimiter-First Code
  3 projects | news.ycombinator.com | 9 Dec 2022
• [D] Any independent researchers ever get published/into conferences?
  3 projects | /r/MachineLearning | 29 Apr 2022

  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/

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