| 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 | |