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.dev246-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (38.6 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ x86-64

persia_cuda111-0.1.dev246-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (38.6 MB view details)

Uploaded CPython 3.8manylinux: glibc 2.17+ x86-64

persia_cuda111-0.1.dev246-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (38.6 MB view details)

Uploaded CPython 3.7mmanylinux: glibc 2.17+ x86-64

File details

Details for the file persia_cuda111-0.1.dev246-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

  • Download URL: persia_cuda111-0.1.dev246-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
  • Upload date:
  • Size: 38.6 MB
  • Tags: CPython 3.9, manylinux: glibc 2.17+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.8.0 pkginfo/1.8.2 readme-renderer/32.0 requests/2.27.1 requests-toolbelt/0.9.1 urllib3/1.26.8 tqdm/4.62.3 importlib-metadata/4.11.1 keyring/23.5.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.9.10

File hashes

Hashes for persia_cuda111-0.1.dev246-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 38d03f8eba048ee9bfa223183789f3f6d9b5eac5197ac6e9c3fba0c008e2e7e0
MD5 2fe26f2fe58d5b32bbbfe2ea69274ac8
BLAKE2b-256 384f0091d11d887c1a7c8688f238fe8894034121b157d382c04822e890fdb597

See more details on using hashes here.

File details

Details for the file persia_cuda111-0.1.dev246-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

  • Download URL: persia_cuda111-0.1.dev246-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
  • Upload date:
  • Size: 38.6 MB
  • Tags: CPython 3.8, manylinux: glibc 2.17+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.8.0 pkginfo/1.8.2 readme-renderer/32.0 requests/2.27.1 requests-toolbelt/0.9.1 urllib3/1.26.8 tqdm/4.62.3 importlib-metadata/4.11.1 keyring/23.5.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.9.10

File hashes

Hashes for persia_cuda111-0.1.dev246-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 67ca40d9bed73208f041e4dcbfdad1dae3cd7ec0f8e7734387190e040a01861f
MD5 40d77046bfd443cb2359a6f4a5d4c80c
BLAKE2b-256 11eb14a2e64b96de9cc4906e82158c2d91a29012ba3256f0b35d01f0f3d3e469

See more details on using hashes here.

File details

Details for the file persia_cuda111-0.1.dev246-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

  • Download URL: persia_cuda111-0.1.dev246-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
  • Upload date:
  • Size: 38.6 MB
  • Tags: CPython 3.7m, manylinux: glibc 2.17+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.8.0 pkginfo/1.8.2 readme-renderer/32.0 requests/2.27.1 requests-toolbelt/0.9.1 urllib3/1.26.8 tqdm/4.62.3 importlib-metadata/4.11.1 keyring/23.5.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.9.10

File hashes

Hashes for persia_cuda111-0.1.dev246-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 4f17d9e1272d026581e92d0bd6d4a40bd0cfbc27868c6869509bc2e820e91b87
MD5 1f9191e884badc71f6a442deab75c838
BLAKE2b-256 61e640af36a932d46ebf2958f361e153d5ace2c18ce1817d8e09a49df4892a3c

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