extending-jax
einops
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| extending-jax | einops | |
|---|---|---|
| 2 | 19 | |
| 352 | 7,916 | |
| - | - | |
| 3.5 | 7.4 | |
| 6 months ago | 11 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
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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?
mpi4jax - Zero-copy MPI communication of JAX arrays, for turbo-charged HPC applications in Python :zap:
opt_einsum - ⚡️Optimizing einsum functions in NumPy, Tensorflow, Dask, and more with contraction order optimization.
thinc - 🔮 A refreshing functional take on deep learning, compatible with your favorite libraries
kymatio - Wavelet scattering transforms in Python with GPU acceleration
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.
trax - Trax — Deep Learning with Clear Code and Speed
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.
elegy - A High Level API for Deep Learning in JAX
horovod - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
diffrax - Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/
3d-ken-burns - an implementation of 3D Ken Burns Effect from a Single Image using PyTorch