tensor_annotations
einops
| tensor_annotations | einops | |
|---|---|---|
| 2 | 19 | |
| 158 | 7,942 | |
| -0.6% | - | |
| 5.8 | 7.4 | |
| 10 months ago | 11 days ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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tensor_annotations
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[D] Have their been any attempts to create a programming language specifically for machine learning?
Not really an answer to your question, but there are Python packages that try to solve the problem of tensor shapes that you mentioned, e.g. https://github.com/patrick-kidger/torchtyping or https://github.com/deepmind/tensor_annotations
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Matrix Multiplication Inches Closer to Mythic Goal
I've explored this space quite a bit. In my view, static checking should be the goal.
https://github.com/deepmind/tensor_annotations and tsastanley seem to be the most far along. I've developed a mypy plugin that does similarly off of the "Named Tensor" dynamic feature (which isn't well supported yet), but haven't released it yet.
einops
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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
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[D] Any independent researchers ever get published/into conferences?
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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[D] Anyone using named tensors or a tensor annotation lib productively?
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.)
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A basic introduction to NumPy's einsum
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.
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Ask HN: What technologies greatly improve the efficiency of development?
This combined with something like einops [1] ( intuitive reshaping library) can be a huge time saver.
[1] https://github.com/arogozhnikov/einops
What are some alternatives?
dex-lang - Research language for array processing in the Haskell/ML family
extending-jax - Extending JAX with custom C++ and CUDA code
torchtyping - Type annotations and dynamic checking for a tensor's shape, dtype, names, etc.
opt_einsum - ⚡️Optimizing einsum functions in NumPy, Tensorflow, Dask, and more with contraction order optimization.
miniF2F - Formal to Formal Mathematics Benchmark
kymatio - Wavelet scattering transforms in Python with GPU acceleration
TablaM - The practical relational programing language for data-oriented applications
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.
tiny-cuda-nn - Lightning fast C++/CUDA neural network framework
data-science-ipython-notebooks - Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
MindsDB - The platform for customizing AI from enterprise data
horovod - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.