pytortto
chainer
pytortto | chainer | |
---|---|---|
1 | 2 | |
16 | 5,867 | |
- | 0.1% | |
7.7 | 0.0 | |
8 months ago | 8 months ago | |
Python | Python | |
MIT License | MIT License |
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pytortto
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pytorch written in numpy and trains in GPU with cupy
I've implemented a simple workable "pytorch" using only numpy and it can be trained in GPU with the help of cupy (a library that runs numpy functions in GPU): https://github.com/samrere/pytortto.
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?
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.
leptonai - A Pythonic framework to simplify AI service building
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.
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
SmallPebble - Minimal deep learning library written from scratch in Python, using NumPy/CuPy.
warp-drive - Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning Framework on a GPU (JMLR 2022)
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
caer - High-performance Vision library in Python. Scale your research, not boilerplate.