The GPU cutover trades scarce capacity against proof that interrupted training resumes faithfully.
Keeping the old path protects current training continuity, while moving now protects the customer’s GPU commitment and launch date.
Who you’d be doing this for
“I restarted the run after preemption, and now my research lead is asking why the curve moved.”
Jing Zhang · ML Platform Engineer
Runs multi-node fine-tuning jobs for an enterprise team preparing a regulated model release.
What is at stake
Some resumed multi-node runs now diverge from their uninterrupted controls after preemption. You must weigh a customer’s GPU deadline against evidence that checkpoint restores preserve training progress.
Why it isn’t already fixed
Every obvious fix costs something else. That’s the part you’d have to decide.
- GPU capacity release vs training continuity
- checkpoint completion vs restore fidelity
- cutover urgency vs reviewable lineage
- scheduler efficiency vs reproducible experiments
Why Databricks
Databricks AI Runtime must make long-running distributed training reproducible when customers depend on managed GPU scheduling and checkpoint restore.
Written with these in mind
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This is the setup. The work is inside.
Running it puts you in the room: the full situation and its constraints, stakeholders who push back in their own words, and the decisions that are yours to make. What you produce becomes a Day One Plan — work you can show someone instead of describing.