feat: add NoImprovementHandler#3760
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Hi, I think the issue meant this handler should take a custom function or method and execute it in case of no improvement. Currently the implementation looks a loot like early stopping instead of a modular handler. |
Author
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Thanks for the feedback! I've updated the PR to add a `custom_action`
parameter. Users can now pass any callable to execute on no improvement
(e.g., reducing LR, checkpointing, logging, or terminating) instead of
being restricted to just trainer termination. The trainer parameter is now
optional when custom_action is provided.
Here's the commit:
Aanushka001@9da7116
Would appreciate a re-review when you get a chance!
…On Tue, Jun 02, 2026 at 07:15 AM, Aaishwarya Mishra < ***@***.***> wrote:
*aaishwarymishra* left a comment (pytorch/ignite#3760)
<#3760 (comment)>
Hi, I think the issue meant this handler should take a custom function or
method and execute it in case of no improvement. Currently the
implementation looks a loot like early stopping instead of a modular
handler.
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Collaborator
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@Aanushka001 have you checked the implementation of the previous PR #2574 ? It seems the issue and the PR points towards also changing |
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Description
Adds a
NoImprovementHandlerthat stops training when metrics stop improving for a set number of epochs. This is a simpler alternative to the existingEarlyStoppinghandler.Key Features
patience,score_function,trainer, and optionalmodeparameter'max', default) and minimization ('min') objectivesstate_dict()andload_state_dict()for checkpointingreset()method for reuse across training runsattach()method for easy integration with ignite enginesChanges
ignite/handlers/no_improvement_handler.pyignite/handlers/__init__.pyto exportNoImprovementHandlerCloses #2314