2021/06/07 by Fan Yang, Yang, Fan, Gabriel Barth-Maron +12 · 1 citation
Computer Science · #Artificial Intelligence (cs.AI) #Cloud Computing and Resource Management #Data Stream Mining Techniques #Distributed #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Algorithms #Parallel #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2106.04516
openalex publication_date 2021/06/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A major driver behind the success of modern machine learning algorithms has been their ability to process ever-larger amounts of data. As a result, the use of distributed systems in both research and production has become increasingly prevalent as a means to scale to this growing data. At the same time, however, distributing the learning process can drastically complicate the implementation of even simple algorithms. This is especially problematic as many machine learning practitioners are not well-versed in the design of distributed systems, let alone those that have complicated communication topologies. In this work we introduce Launchpad, a programming model that simplifies the process of defining and launching distributed systems that is specifically tailored towards a machine learning audience. We describe our framework, its design philosophy and implementation, and give a number of examples of common learning algorithms whose designs are greatly simplified by this approach.