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Techniques for merging multiple individual requests into batches to reduce communication overhead and hotspots.
Distinct from Server-Side Request Parameter Accessors: Focuses on combining requests for efficiency rather than simply accessing request parameters
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This project is a distributed machine learning platform and sparse deep learning framework designed for training and serving models with high-dimensional sparse data. It functions as an online model serving infrastructure and recommendation system engine, enabling real-time item retrieval and scoring using deep tree matching and neural networks. The system distinguishes itself through a multi-task learning framework that optimizes multiple objective functions within a shared representation space. It features a specialized online serving infrastructure that supports dynamic model hot-loading a
Combines multiple parameter requests into single batches to eliminate communication hotspots between workers and servers.