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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.07482 |
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| _version_ | 1866929532801384448 |
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| author | Lu, Ning Xie, Qian Zhang, Hao Fang, Wenyi Zheng, Yang Hu, Zheng Ma, Jiantao |
| author_facet | Lu, Ning Xie, Qian Zhang, Hao Fang, Wenyi Zheng, Yang Hu, Zheng Ma, Jiantao |
| contents | Large Language Models (LLMs) are revolutionizing the AI industry with their superior capabilities. Training these models requires large-scale GPU clusters and significant computing time, leading to frequent failures that significantly increase training costs. Despite its significance, this field lacks a metric for evaluating reliability. In this work, we introduce a novel reliability metric called \emph{Training Overhead Ratio} (TOR) to evaluate the reliability of fault-tolerant LLM training systems. TOR is defined as the ratio of optimal training time to the observed training time of a system, serving as a practical tool for users to estimate the actual time required to train an LLM on a given system. Furthermore, our investigation identifies the key factor for enhancing reliability and present TOR equations for various types of failures encountered in practice. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_07482 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Training Overhead Ratio: A Practical Reliability Metric for Large Language Model Training Systems Lu, Ning Xie, Qian Zhang, Hao Fang, Wenyi Zheng, Yang Hu, Zheng Ma, Jiantao Distributed, Parallel, and Cluster Computing Artificial Intelligence Large Language Models (LLMs) are revolutionizing the AI industry with their superior capabilities. Training these models requires large-scale GPU clusters and significant computing time, leading to frequent failures that significantly increase training costs. Despite its significance, this field lacks a metric for evaluating reliability. In this work, we introduce a novel reliability metric called \emph{Training Overhead Ratio} (TOR) to evaluate the reliability of fault-tolerant LLM training systems. TOR is defined as the ratio of optimal training time to the observed training time of a system, serving as a practical tool for users to estimate the actual time required to train an LLM on a given system. Furthermore, our investigation identifies the key factor for enhancing reliability and present TOR equations for various types of failures encountered in practice. |
| title | Training Overhead Ratio: A Practical Reliability Metric for Large Language Model Training Systems |
| topic | Distributed, Parallel, and Cluster Computing Artificial Intelligence |
| url | https://arxiv.org/abs/2408.07482 |