Launchpad: A Programming Model for Distributed Machine Learning Research
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Abstract (arXiv)
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.
Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 9 of its 25 references that are in the map. First posted on arXiv 7 Jun 2021.
Builds on
9 of 25 references.
References · 9
- 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,609
- 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,218
- 2018IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architecturescited by 866
- 2016TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systemscited by 660
- 2017Ray: A Distributed Framework for Emerging AI Applicationscited by 516
- 2014Caffe: Convolutional Architecture for Fast Feature Embeddingcited by 287
- 2020Acme: A Research Framework for Distributed Reinforcement Learningcited by 146
- 2021Reverb: A Framework For Experience Replaycited by 27
- 2018RLgraph: Modular Computation Graphs for Deep Reinforcement Learningcited by 1
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Citing papers per year: 2021: 1; 2022: 6; 2023: 5; 2024: 1; 2025: 0; 2026: 1.
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- 2021Temporally Abstract Partial Modelscited by 11
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- 2026A Reference Architecture of Reinforcement Learning Frameworks6 Mar 2026
- 2024Sample-Efficient Alignment for LLMs3 Nov 2024
- 2023Bad Students Make Great Teachers: Active Learning Accelerates Large-Scale Visual Understanding8 Dec 2023
- 2023Reinforced Self-Training (ReST) for Language Modeling17 Aug 2023
- 2023SRL: Scaling Distributed Reinforcement Learning to Over Ten Thousand Cores29 Jun 2023
- 2023Co-Learning Empirical Games and World Models23 May 2023
- 2023marl-jax: Multi-Agent Reinforcement Leaning Framework24 Mar 2023
- 2022VeLO: Training Versatile Learned Optimizers by Scaling Up17 Nov 2022
- 2022Phantom -- A RL-driven multi-agent framework to model complex systems12 Oct 2022
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- 2022The Frost Hollow Experiments: Pavlovian Signalling as a Path to Coordination and Communication Between Agents17 Mar 2022
- 2022Learning Robust Real-Time Cultural Transmission without Human Data1 Mar 2022
2 more
- 2023Reinforced Self-Training (ReST) for Language Modelingcited by 486
- 2024Sample-Efficient Alignment for LLMscited by 16
- 2024Sample-Efficient Alignment for LLMs3 Nov 2024
- 2023Reinforced Self-Training (ReST) for Language Modeling17 Aug 2023
- 2023Co-Learning Empirical Games and World Modelscited by 4
- 2023Co-Learning Empirical Games and World Models23 May 2023
- 2022VeLO: Training Versatile Learned Optimizers by Scaling Upcited by 57
- 2023Bad Students Make Great Teachers: Active Learning Accelerates Large-Scale Visual Understandingcited by 28
- 2021Temporally Abstract Partial Modelscited by 11
- 2023SRL: Scaling Distributed Reinforcement Learning to Over Ten Thousand Corescited by 10
- 2022Learning Robust Real-Time Cultural Transmission without Human Datacited by 9
- 2022MAD for Robust Reinforcement Learning in Machine Translationcited by 7
- 2022Phantom -- A RL-driven multi-agent framework to model complex systemscited by 5
- 2026A Reference Architecture of Reinforcement Learning Frameworkscited by 3
- 2022Pavlovian Signalling with General Value Functions in Agent-Agent Temporal Decision Makingcited by 2
- 2022The Frost Hollow Experiments: Pavlovian Signalling as a Path to Coordination and Communication Between Agentscited by 2
- 2023marl-jax: Multi-Agent Reinforcement Leaning Frameworkcited by 1
- 2026A Reference Architecture of Reinforcement Learning Frameworks6 Mar 2026
- 2023Bad Students Make Great Teachers: Active Learning Accelerates Large-Scale Visual Understanding8 Dec 2023
- 2023SRL: Scaling Distributed Reinforcement Learning to Over Ten Thousand Cores29 Jun 2023
- 2023marl-jax: Multi-Agent Reinforcement Leaning Framework24 Mar 2023
- 2022VeLO: Training Versatile Learned Optimizers by Scaling Up17 Nov 2022
- 2022Phantom -- A RL-driven multi-agent framework to model complex systems12 Oct 2022
- 2022MAD for Robust Reinforcement Learning in Machine Translation18 Jul 2022
- 2022The Frost Hollow Experiments: Pavlovian Signalling as a Path to Coordination and Communication Between Agents17 Mar 2022
- 2022Learning Robust Real-Time Cultural Transmission without Human Data1 Mar 2022
- 2022Pavlovian Signalling with General Value Functions in Agent-Agent Temporal Decision Making11 Jan 2022
- 2021Temporally Abstract Partial Models6 Aug 2021