configs/callbacks/early_stopping.yaml

# https://pytorch-lightning.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.EarlyStopping.html

# Monitor a metric and stop training when it stops improving.
# Look at the above link for more detailed information.
early_stopping:
  _target_: lightning.pytorch.callbacks.EarlyStopping
  monitor: ${oc.select:callbacks.model_checkpoint.monitor,"val/loss"} # quantity to be monitored, must be specified!!!
  min_delta: 0. # minimum change in the monitored quantity to qualify as an improvement
  patience: 50 # number of checks with no improvement after which training will be stopped
  verbose: False # verbosity mode
  mode: ${callbacks.model_checkpoint.mode} # "max" means higher metric value is better, can be also "min"
  strict: True # whether to crash the training if monitor is not found in the validation metrics
  check_finite: True # when set True, stops training when the monitor becomes NaN or infinite
  stopping_threshold: null # stop training immediately once the monitored quantity reaches this threshold
  divergence_threshold: null # stop training as soon as the monitored quantity becomes worse than this threshold
  check_on_train_epoch_end: False # whether to run early stopping at the end of the training epoch
  log_rank_zero_only: False  # logs the status of the early stopping callback only for rank 0 process