Skip to main content

PersiaML Python Library

Project description


tutorials Documentation Status PyPI version 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 (there are some materials on the tutorials website, but they are pretty raw). We encourage adventurers to try out PERSIA and contribute!

News

  • 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

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_cuda113-0.1.dev224-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (38.4 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ x86-64

persia_cuda113-0.1.dev224-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (38.4 MB view details)

Uploaded CPython 3.8manylinux: glibc 2.17+ x86-64

persia_cuda113-0.1.dev224-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (38.4 MB view details)

Uploaded CPython 3.7mmanylinux: glibc 2.17+ x86-64

persia_cuda113-0.1.dev224-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (38.4 MB view details)

Uploaded CPython 3.6mmanylinux: glibc 2.17+ x86-64

File details

Details for the file persia_cuda113-0.1.dev224-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for persia_cuda113-0.1.dev224-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 3596680fd52c3212ad7c6d9b9512d8e8a48cfcce919fe40a106eb0f2377ac8c5
MD5 33f506110fb947ab1c4d383bcec3edc4
BLAKE2b-256 8652c84384350b92f62b130009238ee55babf4c35b048872280d79ac2852fc30

See more details on using hashes here.

File details

Details for the file persia_cuda113-0.1.dev224-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for persia_cuda113-0.1.dev224-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 fa7486f908c258f37634e64ee5e2d5a2aaa129834e36e10f7fb188e6b0893e0a
MD5 5ac6e89a463d8bc48707b64539114f4b
BLAKE2b-256 829655eff42c5e4dc9ff70705be987a684be8ad2b4c6123e0beab466a301fba7

See more details on using hashes here.

File details

Details for the file persia_cuda113-0.1.dev224-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for persia_cuda113-0.1.dev224-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 c459f97eb3bd08884b078e25aacabc9cd9b6b9e3ba08e4292e8df30e82dae9ab
MD5 7fa2e690f98b3dbafb1122a009911f78
BLAKE2b-256 56fa47a57b3073c86a0ae085e2398ec994b676370fa250bafd15f89aa60abdad

See more details on using hashes here.

File details

Details for the file persia_cuda113-0.1.dev224-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for persia_cuda113-0.1.dev224-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 bd303bb658802bdc57593a51566f64f2628f10027fa7c80284505ce067ad12af
MD5 fb4c2274e5af4a1ae621c0d95adc68c6
BLAKE2b-256 ec7e119c3925fc3528d40ac8e2d7fe85b5a49ce4edd033165a464abb5806a7cc

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