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https://github.com/PersiaML/PERSIA

High performance distributed framework for training deep learning recommendation models based on PyTorch.
https://github.com/PersiaML/PERSIA

bagua deep-learning distributed-computing machine-learning persia pytorch recommender-system rust-lang

Last synced: 3 months ago
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High performance distributed framework for training deep learning recommendation models based on PyTorch.

Host: GitHub
URL: https://github.com/PersiaML/PERSIA
Owner: PersiaML
License: mit
Created: 2021-06-24T11:31:07.000Z (over 3 years ago)
Default Branch: main
Last Pushed: 2024-08-01T23:28:37.000Z (3 months ago)
Last Synced: 2024-08-02T01:02:39.124Z (3 months ago)
Topics: bagua, deep-learning, distributed-computing, machine-learning, persia, pytorch, recommender-system, rust-lang
Language: Rust
Homepage:
Size: 1.18 MB
Stars: 392
Watchers: 8
Forks: 52
Open Issues: 5
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff

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README

        






























*WARNING: THIS PROJECT IS CURRENTLY NOT MAINTAINED, DUE TO COMPANY REORGANIZATION.*

  

**PERSIA** (**P**arallel r**E**commendation t**R**aining **S**ystem with hybr**I**d **A**cceleration)  is developed by [AI platform@Kuaishou Technology](https://www.kuaishou.com/en), collaborating with ETH. It is a PyTorch-based (the first public one to our best knowledge) system for training large scale deep learning recommendation models on commodity hardwares. It is capable of training recommendation models with up to 100 trillion parameters. To the best of our knowledge, this is the largest model size in recommendation systems so far. Empirical study on public datasets indicate PERSIA's significant advantage over several other existing training systems in recommendation [1]. Its efficiency and robustness have also been validated by multiple applications with 100 million level DAU at Kuaishou. 

*Disclaimer: The program is usable and has served several important businesses. However, the official English documentation and tutorials are still under heavy construction and they are a bit raw now. We encourage adventurers to try out PERSIA and contribute!*

## News

* [Training Deep Learning-based recommender models of 100 trillion parameters over Google Cloud](https://archive.ph/8ay0C)

* [突破百万亿参数规模，追求极致的效率和性价比：华人团队开源首个异构并行推荐系统训练框架 PERSIA](https://archive.ph/Mixk0) (In Chinese. Title: Breaking through the trillion parameter scale in pursuit of ultimate efficiency and cost effectiveness: Chinese team open source PERSIA, the first heterogeneous parallel recommendation system)

* [参数量卷到一百万亿！华人团队开源史上最大的推荐训练系统 PERSIA](https://archive.md/FbocB) (In Chinese. Title: PERSIA, the Largest Recommended Training System in the History of Open Source by Far)

* AI Engines in the "Short-video" Era: Eating 100 Trillion Parameters, Invited talk, Facebook, 2021.

* 单机训练速度提升 640 倍！独家解读快手商业广告模型 GPU 训练平台 PERSIA (In Chinese. Title: 640x Faster GPU Based Learning System for Ad Recommendation)

   * [[AI Front]](https://archive.is/2ii2L) [[中国日报]](https://archive.is/N8fK2) [[InfoQ]](https://archive.is/JESDU) [[CSDN]](https://archive.is/tpvkN) [[Tencent Cloud News]](https://archive.is/kLuaT) [[AcFun]](https://archive.md/vuPmb)

* 创新、平衡与大格局：快手商业化的慢与快 (In Chinese. Title: Innovation, Balance, and Big Picture: The Speed of Kwai Commercialization)

   * [[TechSir]](https://archive.is/EOQ18) [[China Daily]](https://archive.is/L2VJE) [[Sohu]](https://archive.is/aY66U)

## Links

* [GitHub Repository](https://github.com/PersiaML/PERSIA)

* [Tutorials](https://persiaml-tutorials.pages.dev/)

* [API documentation](https://persiaml.pages.dev/) (Under Construction)

## References

1. Xiangru Lian, Binhang Yuan, Xuefeng Zhu, Yulong Wang, Yongjun He, Honghuan Wu, Lei Sun, Haodong Lyu, Chengjun Liu, Xing Dong, Yiqiao Liao, Mingnan Luo, Congfei Zhang, Jingru Xie, Haonan Li, Lei Chen, Renjie Huang, Jianying Lin, Chengchun Shu, Xuezhong Qiu, Zhishan Liu, Dongying Kong, Lei Yuan, Hai Yu, Sen Yang, Ce Zhang, & Ji Liu. (2021). [Persia: A Hybrid System Scaling Deep Learning Based Recommenders up to 100 Trillion Parameters.](https://arxiv.org/abs/2111.05897)

2. Ji Liu & Ce Zhang. (2021). [Distributed Learning Systems with First-order Methods](https://arxiv.org/pdf/2104.05245).

## License

This source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree.