Alternatives

Products that do what Graphlearn-for-PyTorch, distributed graph learning on PyTorch does

Hello HN, We are pleased to introduce you graphlearn-for-pytorch (https://github.com/alibaba/graphlearn-for-pytorch), an open-source distributed graph neural network library based on PyTorch and compatible with PyG. Our library is designed to make it easy for developers to build and train large-scale graph models in a distributed environment. With graphlearn-for-pytorch, you can leverage GPUs to accelerate graph sampling and utilize UVA to reduce the overheads of feature collection. Following a scalable design, graphlearn-for-pytorch supports training GNN models on…

1
Tensorflow Research Cloud▲288
Accelerating open machine learning research with Cloud TPUs
2017
2KA
KarateClub a Python library for unsupervised machine learning on graphs▲102
2020 · github.com
3
TensorFlow 1.0▲80
Open-source machine learning library by Google
2018
4
TensorPort▲143
Help teams apply machine learning to real-world applications
2017
5
Hyta▲128
AI training network to connect and share insights
Feb 2026
6TR
TensorFlow-Resources – Organized and Useful Resources about TensorFlow▲152
2018 · github.com
7
Weights & Biases▲110
Developer tools for deep learning & machine learning
2019
8
TorchTPU▲106
Running PyTorch Natively on TPUs at Google Scale
Apr 2026
9
Forge Agent▲113
Swarm Agents That Turn Slow PyTorch Into Fast GPU Kernels
Jan 2026
10BA
Brancher – A user-friendly PyTorch module for probabilistic inference▲96
2019 · brancher.org
11IR
Initial release of the distributed TensorFlow runtime▲85
2016 · github.com
12TR
TorchGAN – Research Framework for Modelling GANs Based on Pytorch▲56
2018 · github.com
13FR
Faster R-CNN object detector implemented in PyTorch and TensorFlow 2▲15
Fresh and (I think) clean implementations of Faster R-CNN in PyTorch and TensorFlow 2/Keras. I wanted to learn about object detectors and decided to understand and implement a foundational model in the field, Faster R-CNN (elements of which are still used in modern models to this day), using the paper alone. That proved to be more difficult than expected and I had to relent and take a peak at existing implementations to fill in some important gaps. I've documented my struggles and learnings in the README for others to benefit from. I also wanted to solidify my understanding of…
2022 · github.com
14GW
Graph Wavelet Neural Network, ICLR 2019 (PyTorch)▲6
2019 · github.com
15SM
Syna – Minimal ML and RL Framework Built from Scratch with NumPy▲8
Hello HN, I built Syna to understand how modern ML frameworks like PyTorch actually work — from the ground up. It’s a minimal, define-by-run (dynamic graph) framework inspired by DeZero, written entirely with NumPy. Unlike most libraries, Syna includes a basic reinforcement learning module right inside the same framework — no separate packages. It’s not about speed or GPUs — it’s about clarity, simplicity, and learning the internals of machine learning. Great for students, educators, and anyone curious about what’s really happening under the hood. GitHub:…
Oct 2025 · github.com
16PT
PyWarm, the functional API to build neural networks for PyTorch▲6
2019 · github.com
17BE
Bagua - Efficient Distributed Deep Learning for PyTorch Written in Rust▲5
2021 · github.com
18IC
I co-wrote a book on training Deep Learning models in Go▲45
A friend and I wrote a book on how to build and train Deep Learning models in Go. We wanted it to be a useful reference for deep learning basics for Go programmers. Deep Learning is slowly seeping into everything we use every day and we thought it would be great if more people could do it in Go. The book is available here and on Amazon as well. https://www.packtpub.com/big-data-and-business-intelligence/hands-deep-learning-go We would appreciate any feedback and we're always looking to improve.
2019
19RP
Reimplementing PyTorch from scratch (MLP, CNN) to learn the internals▲5
Feb 2026 · github.com
20OA
Opytimizer – A Nature-Inspired Python Optimizer▲23
https://github.com/gugarosa/opytimizer Did you ever reach a bottleneck in your computational experiments? Are you tired of selecting suitable parameters for a chosen technique? If yes, Opytimizer is the real deal! This package provides an easy-to-go implementation of meta-heuristic optimizations. From agents to search space, from internal functions to external communication, we will foster all research related to optimizing stuff. Use Opytimizer if you need a library or wish to: - Create your optimization algorithm; - Design or use pre-loaded optimization tasks; -…
2021
21FM
Flow Matching model inference in C▲5
As the title and description of the GitHub repo suggest, I’m working on a small project for purely educational purposes, with the goal of implementing generative model inference (small models capable of modeling 2D distributions) based on the Flow Matching paradigm in C. I’ve worked on generative AI models based on Flow Matching from a more “abstract” perspective, using frameworks like PyTorch, and I wanted to understand what goes on behind the scenes. The repository is still a work in progress and is also one of my first "serious" projects in C.
Jul 2026 · github.com
22TA
Torchmtl a pytorch library for multi-task learning▲6
2020 · github.com
23MA
Minigrad – a small neural network lib in Golang▲9
I built a neural network library in golang on an autograd engine. Faster and easy to use like PyTorch. Please give it a try and share your feedback. if you like it, a github star will be appreciated.
2024 · github.com
24AH
A Higher-Order Graph Convolutional Layer, NeurIPS 2018 (PyTorch)▲9
2019 · github.com

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