extending-jax
einops
| extending-jax | einops | |
|---|---|---|
| 2 | 23 | |
| 398 | 9,026 | |
| 1.0% | 0.8% | |
| 3.0 | 6.8 | |
| 11 months ago | 10 days ago | |
| Python | Python | |
| MIT License | MIT License |
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extending-jax
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[D] Should We Be Using JAX in 2022?
You can check out this or this for more info. I think it is safe to assume that it is less stable than PyTorch - some other commenters have spoken about running into trouble with XLA in certain corner cases, but I have not experienced this so I can't speak to it.
- Extending JAX with custom C++ and CUDA code
einops
- Einops
- Einops Rocks
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Top 5 Production-Ready Open Source AI Libraries for Engineering Teams
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.
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NumPy 2.0.0
https://einops.rocks/#why-use-einops-notation
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Einsum in 40 Lines of Python
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
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Yorick is an interpreted programming language for scientific simulations
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.
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Torch qeuivalent to image_to_array (keras)
this is definitely what you're looking for: https://github.com/arogozhnikov/einops
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[D] Have their been any attempts to create a programming language specifically for machine learning?
Einops all the things! https://einops.rocks/
- Delimiter-First Code
What are some alternatives?
equinox - Elegant easy-to-use neural networks + scientific computing in JAX. https://docs.kidger.site/equinox/
d2l-en - Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
thinc - 🔮 A refreshing functional take on deep learning, compatible with your favorite libraries
opt_einsum - ⚡️Optimizing einsum functions in NumPy, Tensorflow, Dask, and more with contraction order optimization.
mpi4jax - Zero-copy MPI communication of JAX arrays, for turbo-charged HPC applications in Python :zap:
kymatio - Wavelet scattering transforms in Python with GPU acceleration