mattertune.callbacks.early_stopping
Classes
|
- class mattertune.callbacks.early_stopping.EarlyStoppingConfig(*, monitor='val/total_loss', min_delta=0.0, patience=3, verbose=False, mode='min', strict=True, check_finite=True, stopping_threshold=None, divergence_threshold=None, check_on_train_epoch_end=None, log_rank_zero_only=False)[source]
- Parameters:
monitor (str)
min_delta (float)
patience (int)
verbose (bool)
mode (Literal['min', 'max'])
strict (bool)
check_finite (bool)
stopping_threshold (float | None)
divergence_threshold (float | None)
check_on_train_epoch_end (bool | None)
log_rank_zero_only (bool)
- monitor: str
Quantity to be monitored.
- min_delta: float
Minimum change in monitored quantity to qualify as an improvement. Changes of less than or equal to min_delta will count as no improvement. Default:
0.0.
- patience: int
3.- Type:
Number of validation checks with no improvement after which training will be stopped. Default
- verbose: bool
False.- Type:
Whether to print messages when improvement is found or early stopping is triggered. Default
- mode: Literal['min', 'max']
One of ‘min’ or ‘max’. In ‘min’ mode, training stops when monitored quantity stops decreasing; in ‘max’ mode it stops when the quantity stops increasing. Default:
'min'.
- strict: bool
True.- Type:
Whether to raise an error if monitored metric is not found in validation metrics. Default
- check_finite: bool
True.- Type:
Whether to stop training when the monitor becomes NaN or infinite. Default
- stopping_threshold: float | None
None.- Type:
Stop training immediately once the monitored quantity reaches this threshold. Default
- divergence_threshold: float | None
None.- Type:
Stop training as soon as the monitored quantity becomes worse than this threshold. Default
- check_on_train_epoch_end: bool | None
Whether to run early stopping at the end of training epoch. If False, check runs at validation end. Default:
None.
- log_rank_zero_only: bool
False.- Type:
Whether to log the status of early stopping only for rank 0 process. Default