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* changes * deleating logs * update notebooks * add progress bar callback * update first notebook with new progress bar * update remaining notebooks --------- Co-authored-by: simsalabim1 <[email protected]> Co-authored-by: dennisbader <[email protected]>
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| Original file line number | Diff line number | Diff line change |
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| import sys | ||
|
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| from pytorch_lightning.callbacks import TQDMProgressBar | ||
| from pytorch_lightning.callbacks.progress.tqdm_progress import Tqdm | ||
|
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|
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| class TFMProgressBar(TQDMProgressBar): | ||
| def __init__( | ||
| self, | ||
| enable_sanity_check_bar: bool = True, | ||
| enable_train_bar: bool = True, | ||
| enable_validation_bar: bool = True, | ||
| enable_prediction_bar: bool = True, | ||
| enable_train_bar_only: bool = False, | ||
| **kwargs | ||
| ): | ||
| """Darts' Progress Bar for `TorchForecastingModels`. | ||
| Allows to customize for which model stages (sanity checks, training, validation, prediction) to display a | ||
| progress bar. | ||
| This class is a PyTorch Lightning `Callback` and can be passed to the `TorchForecastingModel` constructor | ||
| through the `pl_trainer_kwargs` parameter. | ||
| Examples | ||
| -------- | ||
| >>> from darts.models import NBEATSModel | ||
| >>> from darts.utils.callbacks import TFMProgressBar | ||
| >>> # only display the training bar and not the validation, prediction, and sanity check bars | ||
| >>> prog_bar = TFMProgressBar(enable_train_bar_only=True) | ||
| >>> model = NBEATSModel(1, 1, pl_trainer_kwargs={"callbacks": [prog_bar]}) | ||
| Parameters | ||
| ---------- | ||
| enable_sanity_check_bar | ||
| Whether to enable to progress bar for sanity checks. | ||
| enable_train_bar | ||
| Whether to enable to progress bar for training. | ||
| enable_validation_bar | ||
| Whether to enable to progress bar for validation. | ||
| enable_prediction_bar | ||
| Whether to enable to progress bar for prediction. | ||
| enable_train_bar_only | ||
| Whether to disable all progress bars except the bar for training. | ||
| **kwargs | ||
| Arguments passed to the PyTorch Lightning's `TQDMProgressBar | ||
| <https://scikit-learn.org/stable/glossary.html#term-random_state>`_. | ||
| """ | ||
| super().__init__(**kwargs) | ||
| self.enable_sanity_check_bar = enable_sanity_check_bar | ||
| self.enable_train_bar = enable_train_bar | ||
| self.enable_validation_bar = enable_validation_bar | ||
| self.enable_prediction_bar = enable_prediction_bar | ||
| self.enable_train_bar_only = enable_train_bar_only | ||
|
|
||
| def init_sanity_tqdm(self) -> Tqdm: | ||
| """Override this to customize the tqdm bar for the validation sanity run.""" | ||
| return Tqdm( | ||
| desc=self.sanity_check_description, | ||
| position=(2 * self.process_position), | ||
| disable=not self.enable_sanity_check_bar or self.enable_train_bar_only, | ||
| leave=False, | ||
| dynamic_ncols=True, | ||
| file=sys.stdout, | ||
| ) | ||
|
|
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| def init_predict_tqdm(self) -> Tqdm: | ||
| """Override this to customize the tqdm bar for predicting.""" | ||
| return Tqdm( | ||
| desc=self.predict_description, | ||
| position=(2 * self.process_position), | ||
| disable=not self.enable_prediction_bar or self.enable_train_bar_only, | ||
| leave=True, | ||
| dynamic_ncols=True, | ||
| file=sys.stdout, | ||
| smoothing=0, | ||
| ) | ||
|
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||
| def init_train_tqdm(self) -> Tqdm: | ||
| """Override this to customize the tqdm bar for training.""" | ||
| return Tqdm( | ||
| desc=self.train_description, | ||
| position=(2 * self.process_position), | ||
| disable=not self.enable_train_bar, | ||
| leave=True, | ||
| dynamic_ncols=True, | ||
| file=sys.stdout, | ||
| smoothing=0, | ||
| ) | ||
|
|
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| def init_validation_tqdm(self) -> Tqdm: | ||
| """Override this to customize the tqdm bar for validation.""" | ||
| # The train progress bar doesn't exist in `trainer.validate()` | ||
| has_main_bar = self.trainer.state.fn != "validate" | ||
| return Tqdm( | ||
| desc=self.validation_description, | ||
| position=(2 * self.process_position + has_main_bar), | ||
| disable=not self.enable_validation_bar or self.enable_train_bar_only, | ||
| leave=not has_main_bar, | ||
| dynamic_ncols=True, | ||
| file=sys.stdout, | ||
| ) |
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