Core Module Documentation#

class neuralprophet.logger.MetricsLogger(**kwargs: Any)#
after_save_checkpoint(checkpoint_callback) → None#

Called after model checkpoint callback saves a new checkpoint.

Parameters

checkpoint_callback – the model checkpoint callback instance

log_metrics(metrics: Mapping[str, float], step: Optional[int] = None) → None#

Records metrics. This method logs metrics as soon as it received them.

Parameters

  • metrics – Dictionary with metric names as keys and measured quantities as values

  • step – Step number at which the metrics should be recorded

class neuralprophet.logger.ProgressBar(*args, **kwargs)#

Custom progress bar for PyTorch Lightning for only update every epoch, not every batch.

on_train_batch_end(trainer: pytorch_lightning.trainer.trainer.Trainer, pl_module: pytorch_lightning.core.module.LightningModule, *_) → None#

Called when the train batch ends.

Note

The value outputs["loss"] here will be the normalized value w.r.t accumulate_grad_batches of the loss returned from training_step.

on_train_epoch_start(trainer: pytorch_lightning.trainer.trainer.Trainer, *_) → None#

Called when the train epoch begins.