Sibylla: To Retry or Not To Retry on Deep Learning Job Failure


Taeyoon Kim, Suyeon Jeong, Jongseop Lee, Soobee Lee, and Myeongjae Jeon, UNIST


GPUs are highly contended resources in shared clusters for deep learning (DL) training. However, our analysis with a real-world trace reveals that a non-negligible number of jobs running on the cluster undergo failures and are blindly retried by the job scheduler. Unfortunately, these job failures often repeat and waste GPU resources, limiting effective GPU utilization across the cluster. In this paper, we introduce Sibylla which informs whether an observed failure of DL training will repeat or not upon retry on the failure. Sibylla employs a machine learning model based on RNNs that trains on stdout and stderr logs of failed jobs and can continuously update the model on new log messages without hand-constructing labels for the new training samples. With Sibylla, the job scheduler is learning-enhanced, performing a retry for a failed job only when it is highly likely to succeed with the retry. We evaluate the effectiveness of Sibylla under a variety of scenarios using trace-driven simulations. Sibylla improves cluster utilization and reduces job completion time (JCT) by up to 15%.

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@inproceedings {280764,
author = {Taeyoon Kim and Suyeon Jeong and Jongseop Lee and Soobee Lee and Myeongjae Jeon},
title = {Sibylla: To Retry or Not To Retry on Deep Learning Job Failure},
booktitle = {2022 USENIX Annual Technical Conference (USENIX ATC 22)},
year = {2022},
isbn = {978-1-939133-29-51},
address = {Carlsbad, CA},
pages = {263--270},
url = {},
publisher = {USENIX Association},
month = jul

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