How to Survive a Spot GPU Reclaim Without Losing Your Training Run

Spot GPU instances can be reclaimed with little warning—typically two minutes on AWS and about 30 seconds on Azure or Google Cloud—so training workloads must be designed to resume rather than restart. The recommended approach is to save complete, atomic checkpoints to durable regional object storage on a time-based schedule, including model, optimizer, scheduler, scaler, random-number-generator, and dataloader states, while retaining recent valid versions. Training applications should catch SIGTERM, set a shutdown flag, finish a safe point, write an emergency checkpoint within the available grace period, and exit cleanly; Kubernetes Jobs can then recreate failed pods and restore from the latest checkpoint. For distributed workloads, sharded and asynchronous checkpointing, elastic frameworks such as torchrun or Ray, gang scheduling, diversified spot pools across instance types and availability zones, and on-demand or reserved fallback capacity can reduce interruption impact. The discussion also emphasizes measuring actual checkpoint duration and local interruption rates, since GPU spot volatility differs by provider, region, and hardware type, and presents Qovery as a platform for operating Kubernetes clusters in a customer’s cloud account while external tools manage training orchestration and recovery.

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