Skip to main content

PersiaML Python Library

Project description


tutorials Documentation Status PyPI version PyPI downloads Docker Pulls license

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

Links

Discussion

Feel free to join our Telegram Group for discussion!

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.

  2. 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.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

persia_cuda111-0.1.dev243-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (38.5 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ x86-64

persia_cuda111-0.1.dev243-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (38.5 MB view details)

Uploaded CPython 3.8manylinux: glibc 2.17+ x86-64

persia_cuda111-0.1.dev243-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (38.5 MB view details)

Uploaded CPython 3.7mmanylinux: glibc 2.17+ x86-64

persia_cuda111-0.1.dev243-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (38.5 MB view details)

Uploaded CPython 3.6mmanylinux: glibc 2.17+ x86-64

File details

Details for the file persia_cuda111-0.1.dev243-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for persia_cuda111-0.1.dev243-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 3027a0ee6dbf9667dd1ac8e2b5b0fe1e9d6203d2e4035a877c6a03b2391c86be
MD5 2a227b481861fb4773a54af191fb13f4
BLAKE2b-256 32a936e190ef084fecf9ea19cacf84dcc5e08963c9ff80d9f48fc11c0919f34b

See more details on using hashes here.

File details

Details for the file persia_cuda111-0.1.dev243-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for persia_cuda111-0.1.dev243-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 4d7ae98f2fc311423153a532cdb0ddf3373a18c7c9f4b3d8cbe8d3d558e61990
MD5 ded2b42656e846ea0500bf91b2ff74a5
BLAKE2b-256 cfceb6ec4f75e0add188cc0cf8016f50bfdd635980071f63df2c47d3b173cf51

See more details on using hashes here.

File details

Details for the file persia_cuda111-0.1.dev243-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for persia_cuda111-0.1.dev243-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 59b1498076258cdfe26a80300cb55cb6b2834379c75f1dfa022b643bd21cbf93
MD5 5b95d36e8001f219b121d8a9896f4e24
BLAKE2b-256 10ac807a122591061778cd3a6d876b56e4c9704f47191df82c84bed1fd1f1e36

See more details on using hashes here.

File details

Details for the file persia_cuda111-0.1.dev243-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for persia_cuda111-0.1.dev243-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 81b08d57f82bd0829bc16828e09648991c48dbb2f8e7d70b9600cc90d934bd96
MD5 3c34c4f0a61ae708fbb3666aef0692c3
BLAKE2b-256 66a6d86dd6c837ba700abb3d7de68cbf379d9f4e8434142333627e5916096688

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page