MindSpore Recommender Documents
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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.

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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>

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   :maxdepth: 1
   :caption: Installation

   install

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   :maxdepth: 1
   :caption: Guide

   offline_learning
   online_learning

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   :caption: API References

   recommender

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   :caption: RELEASE NOTES

   RELEASE