Anthony Hsu, Keqiu Hu, Jonathan Hung, Arun Suresh, and Zhe Zhang, LinkedIn
Training machine learning (ML) models on large datasets requires considerable computing power. To speed up training, it is typical to distribute training across several machines, often with specialized hardware like GPUs or TPUs. Managing a distributed training job is complex and requires dealing with resource contention, distributed configurations, monitoring, and fault tolerance. In this paper, we describe TonY, an open-source orchestrator for distributed ML jobs built at LinkedIn to address these challenges.
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author = {Anthony Hsu and Keqiu Hu and Jonathan Hung and Arun Suresh and Zhe Zhang},
title = {{TonY}: An Orchestrator for Distributed Machine Learning Jobs},
booktitle = {2019 USENIX Conference on Operational Machine Learning (OpML 19)},
year = {2019},
isbn = {978-1-939133-00-7},
address = {Santa Clara, CA},
pages = {39--41},
url = {https://www.usenix.org/conference/opml19/presentation/hsu},
publisher = {USENIX Association},
month = may
}
