The run was finally moving. Then the session limit showed up before the job finished, and half a day of patience turned into another restart.
Why Kaggle starts breaking the workflow
- session limits are fine until your work stops being toy-sized
- checkpointing helps, but it does not remove the interruption tax
- the slower the GPU, the more painful the time cap becomes
- you spend too much energy fitting the platform instead of finishing the run
What people do when the time cap becomes the real problem
They move to a rented GPU they control.
The important upgrade is not luxury. It is continuity: one session, one machine, one full run.
The trap
A lot of people think they just need better checkpointing. Sometimes that helps. But if the job regularly outlives the session, the real problem is that the platform stopped matching the work.
Practical rule
- start with RTX 4090 for notebook-style work and manageable fine-tunes
- move to A100 80GB when the run is memory-heavy and restart-prone
- only evaluate H100 when the workload is already obviously huge
If Kaggle is timing out before the run finishes, stop optimizing around the timeout. Put the job on compute that can actually finish in one go.
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