In large language model (LLM) training, time and computational costs are high, making resilience crucial. Fault recovery relies on frequent checkpoints, but traditional methods face a conflict between high time and expense costs and the risk of losing results with reduced frequency. Preemptible resources offer cost advantages but risk reclamation and inefficient resource switching limits cost optimization.
To tackle these, this talk dives into to address both training interruptions and resource supply disruptions. We will explore the elastic fault tolerance and recovery mechanisms in LLM training and how to enhance the flexibility of resource switching. Key points include:
1.Efficient Fault Recovery:Ensures rapid training task recovery during fault occurs and resource interruptions.
2.Elastic Architecture: Reduces interruptions via dynamic resource adjustments and seamless transitions.
3.Cost Optimization: Flexibly replace cost-effective resources based on resource supply conditions.