default.yaml

early_stopping:
  _target_: pvnet.callbacks.MainEarlyStopping
  # name of the logged metric which determines when model is improving
  monitor: "${resolve_monitor_loss:${model.output_quantiles}}"
  mode: "min" # can be "max" or "min"
  patience: 10 # how many epochs (or val check periods) of not improving until training stops
  min_delta: 0 # minimum change in the monitored metric needed to qualify as an improvement

learning_rate_monitor:
  _target_: lightning.pytorch.callbacks.LearningRateMonitor
  logging_interval: "epoch"

model_summary:
  _target_: lightning.pytorch.callbacks.ModelSummary
  max_depth: 3

model_checkpoint:
  _target_: lightning.pytorch.callbacks.ModelCheckpoint
  # name of the logged metric which determines when model is improving
  monitor: "${resolve_monitor_loss:${model.output_quantiles}}"
  mode: "min" # can be "max" or "min"
  save_top_k: 1 # save k best models (determined by above metric)
  save_last: True # additionaly always save model from last epoch
  every_n_epochs: 1
  verbose: False
  filename: "epoch={epoch}-step={step}"
  # The path to where the model checkpoints will be stored
  dirpath: "PLACEHOLDER/${model_name}" #${..model_name}
  auto_insert_metric_name: False
  save_on_train_epoch_end: False