Checkpoint recovery confidence is pulling against the cost of keeping completed data on the shared file system.
ML engineers need space for the next run, while archived checkpoints must remain dependable enough for a restart.
Who you’d be doing this for
“I cleared old checkpoints once, then spent half a day proving the copy was usable when the run crashed.”
Minh Hlaing · ML Engineer
Runs distributed training jobs that write checkpoints to the shared file system and must restart jobs after failures.
What is at stake
Completed checkpoints keep consuming shared file capacity because ML engineers do not trust an archived copy until a restart proves it. You have to weigh faster capacity release against the recovery evidence a training owner needs.
Why it isn’t already fixed
Every obvious fix costs something else. That’s the part you’d have to decide.
- capacity reclamation vs. recovery confidence
- archive throughput vs. active training latency
- simple first release vs. operational evidence
- user control vs. repeatable recovery
Why Crusoe
Crusoe Cloud Storage serves GPU training workloads whose useful work depends on both shared file throughput and dependable recovery of model state.
Written with these in mind
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