Launchpad: A Programming Model for Distributed Machine Learning Research

TL;DR

Launchpad is a programming model designed to simplify the development and deployment of distributed machine learning algorithms, making it more accessible for practitioners without deep expertise in distributed systems.

Contribution

The paper introduces Launchpad, a novel programming model that streamlines the creation of distributed machine learning systems tailored for researchers and practitioners.

Findings

Simplifies implementation of distributed algorithms

Reduces complexity for machine learning practitioners

Enables easy deployment of common learning algorithms

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.