SmallPebble
chainer
| SmallPebble | chainer | |
|---|---|---|
| 6 | 2 | |
| 112 | 5,864 | |
| - | 0.0% | |
| 0.0 | 0.0 | |
| over 1 year ago | 8 months ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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SmallPebble
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Fastest Autograd in the West
You can implement autograd as a library. Just take a look at this
https://github.com/sradc/SmallPebble
The first line of the description is:
> SmallPebble is a minimal automatic differentiation and deep learning library written from scratch in Python, using NumPy/CuPy. -
Compiling ML models to C for fun
Thanks for this. My approach to speeding up an autodiff system like this was to write it in terms of nd-arrays rather than scalars, using numpy/cupy [1]. But it's still slower than deep learning frameworks that compile / fuse operations. Wondering how it compares to the approach in this post. (Might try to benchmark at some point.)
[1] https://github.com/sradc/SmallPebble
- Understanding Automatic Differentiation in 30 lines of Python
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[P] SmallPebble - minimal(/toy) deep learning framework written from scratch in Python, using NumPy/CuPy. <700 loc.
Located here: https://github.com/sradc/SmallPebble
- Show HN: I wrote a minimal(/toy) deep learning library from scratch in Python
- SmallPebble – Minimal automatic differentiation implementation in Python, NumPy
chainer
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ChaiNNer – Node/Graph based image processing and AI upscaling GUI
There is already an AI framework named Chainer: https://github.com/chainer/chainer
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Protip: the upscaler matters a lot
Sorry maybe someone could chime in and help but I use chainer to upscale. https://github.com/chainer/chainer
What are some alternatives?
MyGrad - Drop-in autodiff for NumPy.
chaiNNer - A node-based image processing GUI aimed at making chaining image processing tasks easy and customizable. Born as an AI upscaling application, chaiNNer has grown into an extremely flexible and powerful programmatic image processing application.
memoized_coduals - Shows that it is possible to implement reverse mode autodiff using a variation on the dual numbers called the codual numbers
leptonai - A Pythonic framework to simplify AI service building
Tensor-Puzzles - Solve puzzles. Improve your pytorch.
tmu - Implements the Tsetlin Machine, Coalesced Tsetlin Machine, Convolutional Tsetlin Machine, Regression Tsetlin Machine, and Weighted Tsetlin Machine, with support for continuous features, drop clause, Type III Feedback, focused negative sampling, multi-task classifier, autoencoder, literal budget, and one-vs-one multi-class classifier. TMU is written in Python with wrappers for C and CUDA-based clause evaluation and updating.
GPU-Puzzles - Solve puzzles. Learn CUDA.
XNOR-popcount-GEMM-PyTorch-CPU-CUDA - A PyTorch implemenation of real XNOR-popcount (1-bit op) GEMM Linear PyTorch extension support both CPU and CUDA
warp-drive - Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning Framework on a GPU (JMLR 2022)
pytortto - deep learning from scratch. uses numpy/cupy, trains in GPU, follows pytorch API
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
caer - High-performance Vision library in Python. Scale your research, not boilerplate.