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model_checkpoint:
_target_: pytorch_lightning.callbacks.ModelCheckpoint
monitor: "val/mae" # name of the logged metric which determines when model is improving
mode: "min" # "max" means higher metric value is better, can be also "min"
save_top_k: 1 # save k best models (determined by above metric)
save_last: True # additionaly always save model from last epoch
verbose: False
dirpath: "checkpoints/"
filename: "epoch_{epoch:03d}"
auto_insert_metric_name: False
early_stopping:
_target_: pytorch_lightning.callbacks.EarlyStopping
monitor: "val/mae" # name of the logged metric which determines when model is improving
mode: "min" # "max" means higher metric value is better, can be also "min"
patience: 100 # how many validation epochs of not improving until training stops
min_delta: 0 # minimum change in the monitored metric needed to qualify as an improvement
model_summary:
_target_: pytorch_lightning.callbacks.RichModelSummary
max_depth: -1
rich_progress_bar:
_target_: pytorch_lightning.callbacks.RichProgressBar
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