| # rich_progress_bar: | |
| # _target_: pytorch_lightning.callbacks.RichProgressBar | |
| rich_model_summary: | |
| _target_: pytorch_lightning.callbacks.RichModelSummary | |
| model_checkpoint: | |
| _target_: pytorch_lightning.callbacks.ModelCheckpoint | |
| monitor: "val/acc" # name of the logged metric which determines when model is improving | |
| mode: "max" # can be "max" or "min" | |
| save_top_k: 1 # save k best models (determined by above metric) | |
| save_last: True # additionally always save model from last epoch | |
| verbose: False | |
| dirpath: ${oc.env:CHECKPOINT_DIR,checkpoints}/${oc.select:name,''} | |
| filename: "epoch_{epoch:03d}" | |
| auto_insert_metric_name: False | |
| early_stopping: | |
| _target_: pytorch_lightning.callbacks.EarlyStopping | |
| monitor: "val/acc" # name of the logged metric which determines when model is improving | |
| mode: "max" # can be "max" or "min" | |
| patience: 100 # how many epochs 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_: pytorch_lightning.callbacks.LearningRateMonitor | |
| logging_interval: step | |
| speed_monitor: | |
| _target_: src.callbacks.speed_monitor.SpeedMonitor | |
| intra_step_time: True | |
| inter_step_time: True | |
| epoch_time: True | |
| loss_scale_monitor: | |
| _target_: src.callbacks.loss_scale_monitor.LossScaleMonitor | |
| params_log: | |
| _target_: src.callbacks.params_log.ParamsLog | |
| total_params_log: True | |
| trainable_params_log: True | |
| non_trainable_params_log: True | |
| gpu_affinity: | |
| _target_: src.callbacks.gpu_affinity.GpuAffinity | |