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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A Lighting Pytorch Framework for Recommendation Models, Easy-to-use and Easy-to-extend.
| Date | Stars |
|---|---|
| 2026-07-24 | 1187 |
| 2026-07-25 | 1187 |
| 2026-07-28 | 1187 |
| 2026-07-30 | 1187 |
| 2026-08-06 | 1187 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
15.0
growth rate 0.00%/day
<div align="center">
# Torch-RecHub: A Lightweight, Efficient, and Easy-to-use PyTorch Recommender Framework
[](https://pypi.org/project/torch-rechub/)
[](https://pepy.tech/projects/torch-rechub)
[](LICENSE)
[](https://www.python.org/)
[](https://pytorch.org/)
[](https://github.com/mert-kurttutan/torchview)
English | [简体中文](README_zh.md)
</div>
**Online Documentation:** https://datawhalechina.github.io/torch-rechub/
**Torch-RecHub** —— **Build production-grade recommender systems in 10 lines of code**. 30+ mainstream models out-of-the-box, one-click ONNX deployment, letting you focus on business instead of engineering.
## ✨ Features
* **Modular Design:** Easy to add new models, datasets, and evaluation metrics.
* **Based on PyTorch:** Leverages PyTorch's dynamic graph and hardware acceleration capabilities. Supports CPU, NVIDIA CUDA GPU, AMD ROCm GPU, and Huawei Ascend NPU.
* **Rich Model Library:** Covers **30+** classic and cutting-edge recommendation algorithms (Matching, Ranking, Multi-task, Generative Recommendation, etc.).
* **Standardized Pipeline:** Provides unified data loading, training, and evaluation workflows.
* **Easy Configuration:** Adjust experiment settings via config files or command-line arguments.
* **Reproducibility:** Designed to ensure reproducible experimental results.
* **ONNX Export:** Export trained models to ONNX format for seamless production deployment.
* **Cross-engine Data Processing:** Support for PySpark-based data processing and transformation, facilitating deployment in big data pipelines.
* **Experiment Visualization & Tracking:** Built-in unified integration for WandB, SwanLab, and TensorBoardX.
## 📖 Table of Contents
- [🔥 Torch-RecHub - A Lightweight, Efficient, and Easy-to-use PyTorch Recommender Framework](#-torch-rechub---a-lightweight-efficient-and-easy-to-use-pytorch-recommender-framework)
- [✨ Features](#-features)
- [📖 Table of Contents](#-table-of-contents)
- [🔧 Installation](#-installation)
- [Requirements](#requirements)
- [Installation Steps](#installation-steps)
- [🚀 Quick Start](#-quick-start)
- [📂 Project Structure](#-project-structure)
- [💡 Supported Models](#-supported-models)
- [📊 Supported Datasets](#-supported-datasets)
- [🧪 Examples](#-examples)
- [Ranking (CTR Prediction)](#ranking-ctr-prediction)
- [Multi-Task Ranking](#multi-task-ranking)
- [Matching Models](#matching-models)
- [Model Visualization](#model-visualization)
- [👨💻 Contributors](#-contributors)
- [🤝 Contributing](#-contributing)
- [📜 License](#-license)
- [📚 Citation](#-citation)
- [📫 Contact](#-contact)
- [⭐️ Star History](#️-star-history)
## 🔧 Installation
### Requirements
* Python 3.9+
* PyTorch 1.10+ (choose the CPU, NVIDIA CUDA, AMD ROCm, or Huawei Ascend NPU build for your device)
* NumPy
* Pandas
* SciPy
* Scikit-learn
### Installation Excerpt of 18,733 characters
Read on GitHub
柯慕灵 · ShanghaiTech University · China
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Mincai Lai · ShanghaiTech University · China
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:c95bea6790ac69bd, topic:deep-learning, topic:pytorch
matched fp:c95bea6790ac69bd, topic:onnx
matched fp:c95bea6790ac69bd, topic:llm