DeepRec-AI/HybridBackend
A high-performance framework for training wide-and-deep recommender systems on heterogeneous cluster
This framework helps machine learning engineers efficiently train large-scale recommender systems. It takes in diverse user interaction data (like purchase history or clicks) and item features, then outputs a high-quality recommendation model. The primary users are ML engineers or data scientists building recommendation engines for e-commerce, content platforms, or other personalized services.
161 stars. No commits in the last 6 months.
Use this if you are building complex recommender systems with vast amounts of categorical data and need to train them efficiently on GPU clusters.
Not ideal if you are working with small datasets, do not require GPU acceleration, or are not building recommender systems.
Stars
161
Forks
30
Language
C++
License
Apache-2.0
Category
Last pushed
Apr 20, 2024
Commits (30d)
0
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