Lightning-AI / Lightning-AI/pytorch-lightning
Add `start_from_epoch` parameter to `EarlyStopping`
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- Dominant language
- Python
- Stars
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- Avg merge
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- Merged PRs (30d)
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Description
### Description & Motivation
It would be great if EarlyStopping callback could have an additional parameter, start_from_epoch/star_from_iteration.
The parameter would represent the number of epochs/iterations to wait before starting to monitor improvement.
This would allow a warm-up period (in which no improvement is expected) and thus training will not be stopped in this period, and the patience can still be as short as the user would want it to be.
Keras has this feature: https://keras.io/api/callbacks/early_stopping/
### Pitch
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### Alternatives
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### Additional context
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cc @borda @carmocca @awaelchli
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start at the EarlyStopping callback implementation and its existing tests. Compare the requested start_from_epoch or start_from_iteration behavior with the current monitoring and patience flow, then verify that monitoring is skipped during the warm-up period while existing stopping behavior remains unchanged.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 45/100