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- # 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
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