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arXiv · 2106.04516

Launchpad: A Programming Model for Distributed Machine Learning Research

Fan Yang·Gabriel Barth-Maron·Piotr Stańczyk·Matthew Hoffman·Siqi Liu·Manuel Kroiss·Aedan Pope·Alban Rrustemi
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Abstract

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.

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Fan Yang, Gabriel Barth-Maron, Piotr Stańczyk, Matthew Hoffman, Siqi Liu, Manuel Kroiss, Aedan Pope, Alban Rrustemi. 2021-06-07. Launchpad: A Programming Model for Distributed Machine Learning Research. https://arxiv.org/abs/2106.04516

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