In the recommendation system training scenario, the in-depth customized version of TenorFlow (TF) within Meituan supports a large number of businesses within Meituan through CPU computing power. This article mainly describes the design, implementation, performance optimization and business implementation of the Booster architecture, hoping to help or inspire students who are engaged in related development. Ultimately, its cost-effectiveness is 2 to 4 times that of CPU tasks. The overall design of the architecture fully considers the characteristics of algorithms, architectures, and new hardware, and has been deeply optimized from multiple perspectives such as data, computing, and communication. The Meituan machine learning platform developed the Booster GPU training architecture based on the in-house deeply customized TensorFlow.
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