:orphan: .. _logging_expert: ######################################## Track and Visualize Experiments (expert) ######################################## **Audience:** Users who want to make their own progress bars or integrate new experiment managers. ---- *********************** Change the progress bar *********************** If you'd like to change the way the progress bar displays information you can use some of our built-in progress bard or build your own. ---- Use the TQDMProgressBar ======================= To use the TQDMProgressBar pass it into the *callbacks* :class:`~lightning.pytorch.trainer.trainer.Trainer` argument. .. code-block:: python from lightning.pytorch.callbacks import TQDMProgressBar trainer = Trainer(callbacks=[TQDMProgressBar()]) ---- Use the RichProgressBar ======================= The RichProgressBar can add custom colors and beautiful formatting for your progress bars. First, install the *`rich `_* library .. code-block:: bash pip install rich Then pass the callback into the callbacks :class:`~lightning.pytorch.trainer.trainer.Trainer` argument: .. code-block:: python from lightning.pytorch.callbacks import RichProgressBar trainer = Trainer(callbacks=[RichProgressBar()]) The rich progress bar can also have custom themes .. code-block:: python from lightning.pytorch.callbacks import RichProgressBar from lightning.pytorch.callbacks.progress.rich_progress import RichProgressBarTheme # create your own theme! theme = RichProgressBarTheme(description="green_yellow", progress_bar="green1") # init as normal progress_bar = RichProgressBar(theme=theme) trainer = Trainer(callbacks=progress_bar) ---- ************************ Customize a progress bar ************************ To customize either the :class:`~lightning.pytorch.callbacks.TQDMProgressBar` or the :class:`~lightning.pytorch.callbacks.RichProgressBar`, subclass it and override any of its methods. .. code-block:: python from lightning.pytorch.callbacks import TQDMProgressBar class LitProgressBar(TQDMProgressBar): def init_validation_tqdm(self): bar = super().init_validation_tqdm() bar.set_description("running validation...") return bar ---- *************************** Build your own progress bar *************************** To build your own progress bar, subclass :class:`~lightning.pytorch.callbacks.ProgressBar` .. code-block:: python from lightning.pytorch.callbacks import ProgressBar class LitProgressBar(ProgressBar): def __init__(self): super().__init__() # don't forget this :) self.enable = True def disable(self): self.enable = False def on_train_batch_end(self, trainer, pl_module, outputs, batch_idx): super().on_train_batch_end(trainer, pl_module, outputs, batch_idx) # don't forget this :) percent = (self.train_batch_idx / self.total_train_batches) * 100 sys.stdout.flush() sys.stdout.write(f"{percent:.01f} percent complete \r") bar = LitProgressBar() trainer = Trainer(callbacks=[bar]) ---- ******************************* Integrate an experiment manager ******************************* To create an integration between a custom logger and Lightning, subclass :class:`~lightning.pytorch.loggers.Logger` .. code-block:: python from lightning.pytorch.loggers import Logger class LitLogger(Logger): @property def name(self) -> str: return "my-experiment" @property def version(self): return "version_0" def log_metrics(self, metrics, step=None): print("my logged metrics", metrics) def log_hyperparams(self, params, *args, **kwargs): print("my logged hyperparameters", params)