Nodus offers method to preserve GPU training progress during spot preemption

Nodus provides a technical pattern to protect machine learning training runs from interruptions when using interruptible cloud GPUs. The method involves saving model checkpoints, optimizer states, and training progress to a dedicated directory using atomic file operations. These saved states can be automatically restored when a new machine instance launches after a preemption event. This approach minimizes lost work to minutes rather than entire training runs. The technique is particularly valuable for cost-effective spot or interruptible GPU instances that can be reclaimed by providers with little notice.