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arXiv · 2408.07482

Training Overhead Ratio: A Practical Reliability Metric for Large Language Model Training Systems

Ning Lu·Qian Xie·Hao Zhang·Wenyi Fang·Yang Zheng·Zheng Hu·Jiantao Ma
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Abstract

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.

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Ning Lu, Qian Xie, Hao Zhang, Wenyi Fang, Yang Zheng, Zheng Hu, Jiantao Ma. 2024-08-14. Training Overhead Ratio: A Practical Reliability Metric for Large Language Model Training Systems. https://arxiv.org/abs/2408.07482

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