This release is a pre-release and may not be stable for production use.
WARNING: THIS PROJECT IS CURRENTLY IN MAINTENANCE MODE, DUE TO COMPANY REORGANIZATION.
PERSIA (Parallel rEcommendation tRaining System with hybrId Acceleration) is developed by AI platform@Kuaishou Technology, 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
- 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 (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 (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)
- 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)
Links
- GitHub Repository
- Tutorials
- API documentation (Under Construction)
Discussion
Feel free to join our Telegram Group for discussion!
References
-
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.
-
Ji Liu & Ce Zhang. (2021). Distributed Learning Systems with First-order Methods.
License
This source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree.
1 maintainer
Metadata
1 maintainer
Release files for persia-cuda111 0.1.dev249
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distributions (wheels)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| persia_cuda111-0.1.dev249-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl | CPython 3.9 | CPython 3.9 | Linux glibc 2.17+ x86-64 | Details |
| persia_cuda111-0.1.dev249-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl | CPython 3.8 | CPython 3.8 | Linux glibc 2.17+ x86-64 | Details |
| persia_cuda111-0.1.dev249-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl | CPython 3.7 | CPython 3.7 pymalloc | Linux glibc 2.17+ x86-64 | Details |
Total release size: 112.5 MB
Release files / persia_cuda111-0.1.dev249-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
| Download URL | persia_cuda111-0.1.dev249-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl |
|---|---|
| Size | 37.5 MB |
| Tags | CPython 3.9 Linux glibc 2.17+ x86-64 |
|
SHA-256 checksum How to use checksums |
3f8dd01e37aebceedec71270434c44128c1489c3eeef5fb2e3ef9526224d9811
|
|
BLAKE2b-256 checksum How to use checksums |
c39f2805975f5a16ec67a5bfe733de0ef2280fa439be437a601aa49f3e7dd943
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.0 CPython/3.9.12
|
Release files / persia_cuda111-0.1.dev249-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
| Download URL | persia_cuda111-0.1.dev249-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl |
|---|---|
| Size | 37.5 MB |
| Tags | CPython 3.8 Linux glibc 2.17+ x86-64 |
|
SHA-256 checksum How to use checksums |
4e89b0959c81671c56b43a22033cd4e815bc131a3514ce972ed99fa2f810e0be
|
|
BLAKE2b-256 checksum How to use checksums |
5920def1f79a1f9df0c9aeea9fd4654b686b3f4f45689a9b727953a9c3a4fdc5
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.0 CPython/3.9.12
|
Release files / persia_cuda111-0.1.dev249-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
| Download URL | persia_cuda111-0.1.dev249-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl |
|---|---|
| Size | 37.5 MB |
| Tags | CPython 3.7 CPython 3.7 pymalloc Linux glibc 2.17+ x86-64 |
|
SHA-256 checksum How to use checksums |
10251210b7f8943abfea36f8d5d1242801e5177b23bf9f81ad023efddb999a50
|
|
BLAKE2b-256 checksum How to use checksums |
c033fbf6cd8f2229f4f65bac41d8bb4fe8135c2628367f3eefd664a6b46520cc
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.0 CPython/3.9.12
|
Security for persia-cuda111 0.1.dev249
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
