Boost the Performance of Model Training with the Ray Framework for Emerging AI Applications

Ray is a high-performance distributed execution framework aimed at large-scale machine learning and reinforcement learning applications. We propose a method to alleviate the heavy burden of parameter servers in Ray, leading to a reduction of 50% of training time with almost the same model accuracy.