Cloud training discipline
Pod lifecycle decoupling, exit-path audits, watchdog cadences — don’t lose a 14-hour run.
Why training runs vanish
Lost training runs are the single most expensive class of incident in agentic ML work. A 14-hour run that vanishes is 14 hours of compute paid for, 14 hours of wall-clock waiting, and an unknown amount of operator confidence destroyed. The same incident class repeats in different forms — laptop slept, network dropped, terminal closed, atexit fired — and each time the symptom is the same: the pod is gone, the artifacts are gone, the run has to start over.
The structural cause is almost always the same. Pod lifecycle is coupled to a process on the operator’s machine. Anything that kills that process kills the pod. The cloud platform is not at fault — the launcher is.
The discipline below decouples pod lifecycle from launcher process. After the discipline lands, none of the laptop-side failures can kill a run. The pod runs to completion or to a paid-for time threshold; the operator knows within 15 minutes if anything’s wrong; artifacts are pulled by independent infrastructure.
Decoupling pod lifecycle from launcher
The single biggest leverage. Three properties to enforce, in order.
| Property | Why it matters | Implementation |
|---|---|---|
| Auto-destroy threshold on pod | Worst-case bound on lifetime if everything else fails | Set at pod creation, e.g., auto_destroy_after=24h. Generous but finite. |
| Launcher exits after creation | Removes the launcher process as a failure point | Print pod ID + dashboard URL; exit cleanly. No wait_for_completion(). |
| Cleanup conditional on positive completion | Pod destroys only when training succeeds, not on signals | Training script writes a “done” signal; pod self-destroys on the signal. No atexit. |
The auto-destroy threshold is the safety net. If the positive-completion signal never arrives (training crashed silently, network partition, anything), the pod runs to threshold and then dies. You pay for the full threshold, but the pod is bounded — not running for a week racking up charges.
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Cloud training discipline — postmortem-driven SOPs
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