Uber’s Contribution to Faster Deep Learning: A Case Study in Distributed Model Training

Hamid Mahmoodabadi
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Hamid Mahmoodabadi: Securities and Exchange Organization of Iran

Chapter Chapter 7 in Advances in Data Clustering, 2024, pp 117-127 from Springer

Abstract: Abstract This chapter delves into the fascinating realm of deep learning and its practical implications. It offers valuable insights that blend the scientific and technical aspects of distributed model training using the HOROVOD library in Python. This chapter’s significance lies in its ability to address a crucial need within the overarching theme of “data clustering.” With the explosive growth of data in today’s world, efficient and scalable deep learning methods are indispensable for clustering, processing, and deriving meaningful insights from massive datasets. HOROVOD’s role in enabling distributed model training not only accelerates the speed of deep learning but also opens up new horizons for data clustering, making it a pivotal tool for researchers, data scientists, and engineers seeking to harness the full potential of their data-driven endeavors.

Keywords: Distributed deep learning; Distributed model training; HOROVOD; Distributed computing; Distributed training; Parallel computing; Gradient aggregation; Fault tolerance; Ring-Allreduce algorithm (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-97-7679-5_7

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DOI: 10.1007/978-981-97-7679-5_7

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