einops VS framework-reproducibility

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

Even with a fixed seed there's no guarantee that you'll get the exact same results due to the fact that most floating operations are not deterministic when parallelized. You can enable determinism flags in your framework to try and mitigate that, but results may still vary depending on your model and how you're running it.

Setting the seed alone is not enough because there will be a randomness resulted from GPU operations (there is some way to eliminate randomness due to GPU operations like https://github.com/NVIDIA/framework-determinism, but I cannot make it work with the current latest version of TF). Another workaround is not using GPU, but the training time does not make sense as I need to iterate fast, trying new idea.

check out https://github.com/NVIDIA/framework-determinism, which should allow you to make fully reproducible to the bit code that runs on GPU. i've contributed to this repo and the author is extremely helpful.