arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2607.01409cs.SEcs.AIcs.LG
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GPUAlert: 一种零侵入的进程边界监控器,用于诊断GPU训练任务故障

GPUAlert: A Zero-Instrumentation Process-Boundary Monitor for Diagnosing GPU Training-Job Failures

AI-NLP-ML Research Laboratory(人工智能-自然语言处理-机器学习研究实验室);Department of Computer Science and Engineering, Indian Institute of Technology Patna(计算机科学与工程系,印度理工学院帕坦)

Parv Agarwal, Asif Ekbal

AI总结:

提出GPUAlert,一种零侵入的进程边界监控工具,通过预启动日志保证、通知器隔离和非静默工件预算三个可靠性原语,在无需修改训练脚本的情况下,发送结构化故障通知,并附带分类原因、持久日志和输出工件。

摘要:

GPU训练任务经常失败,在大型生产集群中大约五分之二的任务会失败,但操作员通常只能在数小时后重新连接时才得知故障。实验跟踪器需要编辑训练脚本并保持云连接;调度器的邮件钩子只传递一行状态信息,没有原因和日志。GPUAlert是一个命令行包装器,它在进程边界监控任何训练命令,无需修改该命令,在完成时发送结构化通知,包含分类的故障原因、持久日志和输出工件。该工具围绕三个可靠性原语组织:预启动日志保证,在子进程崩溃前建立持久目标;通知器隔离,使包装器的退出代码成为子进程状态的纯函数,无论邮件是否成功;以及非静默工件预算,限制附件大小而不会静默丢弃输出。我们发布了一个包含474个GPU训练日志的标注语料库,涵盖15个故障类别,以及一个可复现的评估框架。在十二个硬件可复现的类别上,有序规则分类器达到了0.997的宏F1分数,而无序关键词匹配为0.830,退出代码检查为0.133。包装器开销恒定约为每个任务3毫秒;预启动保证保留了日志,而shell重定向则无输出;在所有15种故障模式下,即使SMTP中继不可达,包装器也返回子进程的原始退出代码。

英文摘要:

GPU training jobs fail often, roughly two in five on large production clusters, yet the operator typically learns of a failure only by reconnecting hours later. Experiment trackers require editing the training script and maintaining a cloud connection; the scheduler's mail hook delivers a single status line with no cause and no logs. GPUAlert is a command-line wrapper that monitors any training command at the process boundary, and with no change to that command, emails a structured notification on completion carrying a classified failure cause, durable logs, and output artifacts. The tool is organized around three reliability primitives: a pre-launch log guarantee that establishes the durable destination before the child process can crash, notifier isolation that makes the wrapper's exit code a pure function of the child's status regardless of whether the email succeeds, and a non-silent artifact budget that bounds attachment size without ever dropping output silently. We release a labelled corpus of 474 GPU training logs across 15 failure classes and a reproducible evaluation harness. On the twelve hardware-reproduced classes, the ordered-rule classifier reaches 0.997 macro-F1, against 0.830 for unordered keyword matching and 0.133 for exit-code inspection. Wrapper overhead is a constant approximately 3ms per job; the pre-launch guarantee preserves a log where a shell redirect yields nothing; and across all 15 failure modes the wrapper returns the child's exit code unchanged even when the SMTP relay is unreachable.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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