MindSpore Recommender Documents

MindSpore Recommender is an open source training acceleration library based on the MindSpore framework for the recommendation domain. With MindSpore’s large-scale heterogeneous computing acceleration capability, MindSpore Recommender supports efficient training of large-scale dynamic features for online and offline scenarios.

The MindSpore Recommender acceleration library consists of the following components:

  • online training: implements online training of real-time data and incremental model updates by streaming data from real-time data sources (e.g., Kafka) and online real-time data processing to support business scenarios that require real-time model updates.

  • offline training: for the traditional offline dataset training scenario, it supports the training of recommendation models containing large-scale feature vectors through automatic parallelism, distributed feature caching, heterogeneous acceleration and other technical solutions.

  • data processing: MindSpore Pandas and MindData provide the ability to read and process data online and offline, saving the overhead of multiple languages and frameworks through full-Python expression support, and opening up efficient data flow links for data processing and model training.

  • model library: includes continuous rich training of typical recommendation models. After rigorous validation for accuracy and performance, it can be used right after installation.

Code repository address: <https://github.com/mindspore-lab/mindrec>

Installation

Guide

API References

RELEASE NOTES