clinicadl.callbacks.TrainingCheckpointCallback

class clinicadl.callbacks.TrainingCheckpointCallback(every_n_epochs: int = 10, enabled: bool = True)

To save checkpoints of a training phase.

The user can then resume a training from the last saved checkpoint when calling Trainer.resume.

The checkpoints will be deleted when the training is completed. To save permanently checkpoints of your neural network, use instead ModelCheckpointCallback.

Parameters:

• every_n_epochs (int, default=10) – Interval (in epochs) for saving checkpoints.

• enabled (bool, default=True) – Whether to activate checkpointing.

on_train_start(*, metrics: MetricsHandler, callbacks: CallbacksHandler, optimizers: dict[str, Optimizer], grad_scaler: GradScaler, **kwargs) → None

Called once at the beginning of Trainer.train if resume=False.

If resuming a training, on_resume() will be called instead.

Parameters:

• model (Model) – The model associated to the Trainer.

• maps (Maps) – The MAPS associated to the Trainer.

• state (TrainerState) – The current state of the Trainer.

• split (Split) – The clinicadl.split.Split on which training is performed.

• optimizers (dict[str, torch.optim.Optimizer]) – The current torch.optim.Optimizer, as returned by by Model.backward_step.

• grad_scaler (torch.amp.GradScaler) – The torch.amp.GradScaler used to scale gradients.

• optimization (OptimizationConfig) – The optimization specifications of the training phase.

• metrics (MetricsHandler) – The validation metrics to compute.

• callbacks (CallbacksHandler) – The callbacks passed to the Trainer.

• computational (ComputationalConfig) – The clinicadl.train.ComputationalConfig defining the computational specifications of the training phase.

on_exception(*, maps: Maps, state: TrainerState, **kwargs) → None

Called when an exception interrupts an execution of the Trainer.

Parameters:

• model (Model) – The model associated to the Trainer.

• maps (Maps) – The MAPS associated to the Trainer.

• state (TrainerState) – The current state of the Trainer.

• exception (Exception) – The exception that has been raised.

on_resume(*, model: Model, maps: Maps, state: TrainerState, metrics: MetricsHandler, callbacks: CallbacksHandler, optimizers: dict[str, Optimizer], grad_scaler: GradScaler, **kwargs) → None

Called once when Trainer.train is resuming a training.

More precisely, this method will be called just before loading the checkpoints.

Parameters:

• model (Model) – The model associated to the Trainer.

• maps (Maps) – The MAPS associated to the Trainer.

• state (TrainerState) – The current state of the Trainer.

• split (Split) – The clinicadl.split.Split on which training is performed.

• optimizers (dict[str, torch.optim.Optimizer]) – The current torch.optim.Optimizer, as returned by by Model.backward_step.

• grad_scaler (torch.amp.GradScaler) – The torch.amp.GradScaler used to scale gradients.

• optimization (OptimizationConfig) – The optimization specifications of the training phase.

• metrics (MetricsHandler) – The validation metrics to compute.

• callbacks (CallbacksHandler) – The callbacks passed to the Trainer.

• computational (ComputationalConfig) – The clinicadl.train.ComputationalConfig defining the computational specifications of the training phase.

on_epoch_end(*, model: Model, maps: Maps, state: TrainerState) → None

Called at the end of an epoch in Trainer.train.

Parameters:

• model (Model) – The model associated to the Trainer.

• maps (Maps) – The MAPS associated to the Trainer.

• state (TrainerState) – The current state of the Trainer.

on_train_end(*, maps: Maps, state: TrainerState, **kwargs) → None

Called once at the end of Trainer.train.

Parameters:

• model (Model) – The model associated to the Trainer.

• maps (Maps) – The MAPS associated to the Trainer.

• state (TrainerState) – The current state of the Trainer.