Summary of Ravnest: Decentralized Asynchronous Training on Heterogeneous Devices, by Anirudh Rajiv Menon et al.
Ravnest: Decentralized Asynchronous Training on Heterogeneous Devices
by Anirudh Rajiv Menon, Unnikrishnan Menon, Kailash Ahirwar
First submitted to arxiv on: 3 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed decentralized training paradigm for large modern deep learning models leverages the compute power of regular heterogeneous PCs connected across the internet, overcoming memory constraints by efficiently organizing nodes into clusters. The Zero-Bubble Asynchronous Model Parallel training and Parallel Multi-Ring All-Reduce method enable global parameter averaging across all clusters, achieving favourable performance metrics. The asynchronous SGD loss function is framed as a block structured optimization problem with delayed updates, deriving an optimal convergence rate of O(1/√K). This architecture demonstrates linear speedup with respect to the number of participating clusters and the bound on the staleness parameter. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way for computers to work together to train large deep learning models. Usually, these big models are trained on powerful computers or in specialized data centers. But what if we could use lots of regular computers, connected over the internet, to help with this training? That’s exactly what this paper proposes. By grouping these regular computers into teams and giving each team a part of the model to work on, we can overcome memory constraints and train these big models more efficiently. |
Keywords
* Artificial intelligence * Deep learning * Loss function * Optimization
