horovod
einops
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horovod | einops | |
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8 | 17 | |
13,930 | 7,875 | |
0.8% | - | |
5.8 | 8.2 | |
21 days ago | 2 months ago | |
Python | Python | |
GNU General Public License v3.0 or later | MIT License |
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horovod
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[D] What is the recommended approach to training NN on big data set?
And in case scaling is really important to you. May I suggest you look into Horovod?
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[D] PyTorch Distributed Training Libraries: What are the current options?
Check out Horovod - https://github.com/horovod/horovod
einops
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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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[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.
I would like to second that! I converted multiple of my lab mates into Einops just by having them browse through the tutorial in https://einops.rocks/ :)
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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.
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[D] What are your favorite tools to visualize/explain tensor operations?
einops: just look at the pretty visual GIF and be amazed
What are some alternatives?
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DeepDanbooru - AI based multi-label girl image classification system, implemented by using TensorFlow.
mpi4jax - Zero-copy MPI communication of JAX arrays, for turbo-charged HPC applications in Python :zap:
extending-jax - Extending JAX with custom C++ and CUDA code
opt_einsum - ⚡️Optimizing einsum functions in NumPy, Tensorflow, Dask, and more with contraction order optimization.
NudeNet - Neural Nets for Nudity Detection and Censoring
onepanel - The open source, end-to-end computer vision platform. Label, build, train, tune, deploy and automate in a unified platform that runs on any cloud and on-premises.
thinc - 🔮 A refreshing functional take on deep learning, compatible with your favorite libraries
kymatio - Wavelet scattering transforms in Python with GPU acceleration
seq2seq - A general-purpose encoder-decoder framework for Tensorflow
pytorch-summary - Model summary in PyTorch similar to `model.summary()` in Keras
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.