Large-scale training is inherently fragile. Spot instances terminate, networking blips corrupt writes, and hyperparameter sweeps multiply the surfaces where state can be lost.
Checkpoint discipline is not optional overhead — it is the difference between a one-hour resume and a two-week rerun. Teams that treat snapshots as first-class artifacts ship experiments faster and waste less compute.
Checkpoint.codes automates cadence, deduplication, and cross-region replication so engineers focus on model quality, not artifact babysitting.
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