exact-training-resume-guard

To ensure exact resume for distributed training jobs, you must preserve the full training state, including model, optimizer, scheduler, RNG, and distributed states, and restore from the latest complete checkpoint on durable storage.

A fault-tolerant Hugging Face Trainer checkpoint requires saving the model, optimizer, scheduler, RNG, and distributed trainer state. This full state preservation guarantees continuity from the last saved step upon resumption.

To safely handle termination signals, your training launcher must trap these signals and immediately flush a resumable checkpoint containing the full training state to durable storage before the process exits.

Yes, DeepSpeed requires saving the full distributed state alongside the model, optimizer, scheduler, and RNG states to achieve exact training resume and verify required framework conditions before resuming.

The best way to automatically resume a long-running training task is to configure the entrypoint to select the latest complete checkpoint, verify the required framework state, and automatically restore the full training state.