Launchpad: A Programming Model for Distributed Machine Learning Research
By Fan Yang, Gabriel Barth-Maron, Piotr Sta'nczyk, Matthew J. Hoffman, Siqi Liu, M. Kroiss, Aedan Pope, Alban Rrustemi. 2021
25 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Information Systems
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Sergey Levine
- Pieter Abbeel
- Nando de Freitas
- Ziyun Wang
- Yoshua Bengio
- Chelsea Finn
- Quoc V. Le
- Ruslan Salakhutdinov
In Information Systems
Citations in the corpus
- Acme: A Research Framework for Distributed Reinforcement Learning (2020, methodology): “Roughly speaking, Launchpad provides a mechanism for creating a distributed program as a graph consisting of nodes and edges.”