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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.01628 |
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| _version_ | 1866915369466200064 |
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| author | He, Zilong Chen, Pengfei Zhang, Hongyu Li, Xiaoyun Yu, Guangba Chen, Hongyang Zheng, Zibin |
| author_facet | He, Zilong Chen, Pengfei Zhang, Hongyu Li, Xiaoyun Yu, Guangba Chen, Hongyang Zheng, Zibin |
| contents | Deep learning (DL) systems have been widely adopted in many areas, and are becoming even more popular with the emergence of large language models. However, due to the complex software stacks involved in their development and execution, crashes are unavoidable and common. Crashes severely waste computing resources and hinder development productivity, so efficient crash recovery is crucial. Existing solutions, such as checkpoint-retry, are too heavyweight for fast recovery from crashes caused by minor programming errors or transient runtime errors. Therefore, we present DaiFu, an in-situ recovery framework for DL systems. Through a lightweight code transformation to a given DL system, DaiFu augments it to intercept crashes in situ and enables dynamic and instant updates to its program running context (e.g., code, configurations, and other data) for agile crash recovery. Our evaluation shows that DaiFu helps reduce the restore time for crash recovery, achieving a 1372x speedup compared with state-of-the-art solutions. Meanwhile, the overhead of DaiFu is negligible (under 0.40%). We also construct a benchmark spanning 7 distinct crash scenarios in DL systems, and show the effectiveness of DaiFu in diverse situations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_01628 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DaiFu: In-Situ Crash Recovery for Deep Learning Systems He, Zilong Chen, Pengfei Zhang, Hongyu Li, Xiaoyun Yu, Guangba Chen, Hongyang Zheng, Zibin Software Engineering Deep learning (DL) systems have been widely adopted in many areas, and are becoming even more popular with the emergence of large language models. However, due to the complex software stacks involved in their development and execution, crashes are unavoidable and common. Crashes severely waste computing resources and hinder development productivity, so efficient crash recovery is crucial. Existing solutions, such as checkpoint-retry, are too heavyweight for fast recovery from crashes caused by minor programming errors or transient runtime errors. Therefore, we present DaiFu, an in-situ recovery framework for DL systems. Through a lightweight code transformation to a given DL system, DaiFu augments it to intercept crashes in situ and enables dynamic and instant updates to its program running context (e.g., code, configurations, and other data) for agile crash recovery. Our evaluation shows that DaiFu helps reduce the restore time for crash recovery, achieving a 1372x speedup compared with state-of-the-art solutions. Meanwhile, the overhead of DaiFu is negligible (under 0.40%). We also construct a benchmark spanning 7 distinct crash scenarios in DL systems, and show the effectiveness of DaiFu in diverse situations. |
| title | DaiFu: In-Situ Crash Recovery for Deep Learning Systems |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2507.01628 |