Distributed Deep Learning in Open Collaborations

Abstract

Modern deep learning applications require increasingly more compute to train state-of-the-art models. To address this demand, large corporations and institutions use dedicated High-Performance Computing clusters, whose construction and maintenance are both environmentally costly and well beyond the budget of most organizations. As a result, some research directions become the exclusive domain of a few large industrial and even fewer academic actors. To alleviate this disparity, smaller groups may pool their computational resources and run collaborative experiments that benefit all participants. This paradigm, known as grid-or volunteer computing, has seen successful applications in numerous scientific areas. However, using this approach for machine learning is difficult due to high latency, asymmetric bandwidth, and several challenges unique to volunteer computing. In this work, we carefully analyze these constraints and propose a novel algorithmic framework designed specifically for collaborative training. We demonstrate the effectiveness of our approach for SwAV and ALBERT pretraining in realistic conditions and achieve performance comparable to traditional setups at a fraction of the cost. Finally, we provide a detailed report of successful collaborative language model pretraining with 40 participants.

On the Opportunities and Risks of Foundation Models

“…Building and maintaining a cluster of thousands of accelerators also requires tremendous effort. New training paradigms like Learning@Home [Ryabinin and Gusev 2020;Diskin et al 2021] explore leveraging volunteer compute over the internet to train foundation models collaboratively. Such fundamentally new execution models can decrease the cost of training for any one entity, but require collaboration across a number of different areas like security (to ensure that a malicious volunteer cannot significantly alter the training process), distributed systems (to deal with fault tolerance issues as volunteers drop), and crowdsourcing.…”