Motivation

Currently, the EarlyStopping handler maintains its internal state (counter and best_score) across multiple calls of engine.run(). While this is ideal for resuming training from checkpoints, it creates a friction point for users in interactive environments (e.g., Jupyter Notebooks or Google Colab).

In these settings, a user might:

1. Run a training session that triggers early stopping.
2. Adjust a hyperparameter in the same notebook.
3. Call trainer.run() again using the same engine/handler instance.

Because the state is preserved, the engine immediately terminates because the counter is already at the limit. The user must manually re-instantiate the handler or manually reset the attributes, which is not intuitive for experiment-heavy workflows.

Proposed Solution

I propose adding an optional boolean parameter reset_on_start (defaulting to False) to the EarlyStopping constructor.

If set to True, the handler will attach a listener to Events.STARTED on the provided trainer. This listener calls a internal reset() method to clear self.counter and self.best_score at the start of every run.

The feature is also backwards compatible as the default is set to False which preserve existing behavior for distributed/long-running jobs where state must persist across crashes/resumes.

Example

# The handler will now automatically "refresh" every time trainer.run() is called.
handler = EarlyStopping(
    patience=10, 
    score_function=score_function, 
    trainer=trainer, 
    reset_on_start=True
)

evaluator.add_event_handler(Events.COMPLETED, handler)