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arXiv · 2407.07852

OpenDiLoCo: An Open-Source Framework for Globally Distributed Low-Communication Training

Sami Jaghouar·Jack Min Ong·Johannes Hagemann
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Abstract

OpenDiLoCo is an open-source implementation and replication of the Distributed Low-Communication (DiLoCo) training method for large language models. We provide a reproducible implementation of the DiLoCo experiments, offering it within a scalable, decentralized training framework using the Hivemind library. We demonstrate its effectiveness by training a model across two continents and three countries, while maintaining 90-95% compute utilization. Additionally, we conduct ablations studies focusing on the algorithm's compute efficiency, scalability in the number of workers and show that its gradients can be all-reduced using FP16 without any performance degradation. Furthermore, we scale OpenDiLoCo to 3x the size of the original work, demonstrating its effectiveness for billion parameter models.

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Sami Jaghouar, Jack Min Ong, Johannes Hagemann. 2024-07-10. OpenDiLoCo: An Open-Source Framework for Globally Distributed Low-Communication Training. https://arxiv.org/abs/2407.07852

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