328 citations · 362 across the 3 of their papers we have counts for
3 papers
AlpaServe: Statistical Multiplexing with Model Parallelism for Deep Learning Serving
Zhuohan Li, Lianmin Zheng, Yinmin Zhong +8
Model parallelism is conventionally viewed as a method to scale a single large deep learning model beyond the memory limits of a single device. In this paper, we demonstrate that m…
RLlib: Abstractions for Distributed Reinforcement Learning
Eric Liang, Richard Liaw, Philipp Moritz +6
Reinforcement learning (RL) algorithms involve the deep nesting of highly irregular computation patterns, each of which typically exhibits opportunities for distributed computation…
Real-Time Machine Learning: The Missing Pieces
Robert Nishihara, Philipp Moritz, Stephanie Wang +7
Machine learning applications are increasingly deployed not only to serve predictions using static models, but also as tightly-integrated components of feedback loops involving dyn…