The training scale of large language models (LLMs) has reached tens of thousands of GPUs and is still continuously expanding, enabling faster learning of larger models. Accompanying the expansion of the resource scale is the prevalence of failures (CUDA error, NaN values, job hang, etc.), which poses significant challenges to training stability. Any large-scale LLM training infrastructure should strive for minimal training interruption, efficient fault diagnosis, and effective failure tolerance to enable highly efficient continuous training. This paper presents ByteRobust, a large-scale GPU infrastructure management system tailored for robust and stable training of LLMs. It exploits the uniqueness of LLM training process and gives top priorities to detecting and recovering failures in a routine manner. Leveraging parallelisms and characteristics of LLM training, ByteRobust enables high-capacity fault tolerance, prompt fault demarcation, and localization with an effective data-driven approach, comprehensively ensuring continuous and efficient training of LLM tasks. ByteRobust is deployed on a production GPU platform with over 200,000 GPUs and advances the state of the art in training robustness by achieving 97% ETTR for a three-month training job on 9,600 GPUs.

Organizations often build separate training and inference clusters for deep learning, and use separate schedulers to manage them. This leads to problems for both: inference clusters have low utilization when the traffic load is low; training jobs often experience long queuing due to a lack of resources. We introduce Lyra, a new cluster scheduler to address these problems. Lyra introduces capacity loaning to loan idle inference servers for training jobs. It further exploits elastic scaling that scales a training job's resource allocation to better utilize loaned servers. Capacity loaning and elastic scaling create new challenges to cluster management. When the loaned servers need to be returned, we need to minimize job preemptions; when more GPUs become available, we need to allocate them to elastic jobs and minimize the job completion time (JCT). Lyra addresses these combinatorial problems with principled heuristics. It introduces the notion of server preemption cost, which it greedily reduces during server reclaiming. It further relies on the JCT reduction value defined for each additional worker of an elastic job to solve the scheduling problem as a multiple-choice knapsack problem. Prototype implementation on a 64-GPU testbed and large-scale simulation with 15-day traces of over 50,000 production jobs show that Lyra brings 1.53x and 1.48x reductions in average queuing time and JCT, and improves cluster usage by up to 25%.

Graphics processing units (GPUs) are the de facto standard for processing deep learning (DL) tasks. In large-scale GPU clusters, GPU failures are inevitable and may cause severe consequences. For example, GPU failures disrupt distributed training, crash inference services, and result in service level agreement violations. In this paper, we study the problem of predicting GPU failures using machine learning (ML) models to mitigate their damages.