thinc
fast-soft-sort
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| thinc | fast-soft-sort | |
|---|---|---|
| 4 | 1 | |
| 2,789 | 544 | |
| 0.5% | 2.0% | |
| 7.6 | 1.8 | |
| 5 days ago | 2 months ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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thinc
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JAX – NumPy on the CPU, GPU, and TPU, with great automatic differentiation
Agree, though I wouldn’t call PyTorch a drop-in for NumPy either. CuPy is the drop-in. Excepting some corner cases, you can use the same code for both. Thinc’s ops work with both NumPy and CuPy:
https://github.com/explosion/thinc/blob/master/thinc/backend...
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Tinygrad: A simple and powerful neural network framework
I love those tiny DNN frameworks, some examples that I studied in the past (I still use PyTorch for work related projects) :
thinc.by the creators of spaCy https://github.com/explosion/thinc
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good examples of functional-like python code that one can study?
thinc - defining neural nets in functional way jax, a new deep learning framework puts emphasis on functions rather than tensors, I've tested it for a couple of applications and it's really cool, you can write stuff like you'd write math expressions in papers using numpy. That speeds up development significantly, and makes code much more readable
- thinc - A refreshing functional take on deep learning, compatible with your favorite libraries
fast-soft-sort
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[P] Torchsort - Fast, differentiable sorting and ranking in PyTorch
The original implementation (https://github.com/google-research/fast-soft-sort) uses numba for the forward pass and pure python for the backwards pass, while Torchsort has both implemented in C++/CUDA with additional parallelization over the batch dimension. You can find some benchmarks in the Torchsort readme.
What are some alternatives?
quantulum3 - Library for unit extraction - fork of quantulum for python3
google-research - Google Research
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
torchsort - Fast, differentiable sorting and ranking in PyTorch
horovod - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
extending-jax - Extending JAX with custom C++ and CUDA code
einops - Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
dm-haiku - JAX-based neural network library
ivy - The Unified AI Framework
AIF360 - A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.
textacy - NLP, before and after spaCy