.. testsetup:: * from pytorch_lightning.core.lightning import LightningModule from pytorch_lightning.trainer.trainer import Trainer from pytorch_lightning import loggers as pl_loggers .. role:: hidden :class: hidden-section .. _logging: ####### Logging ####### Lightning supports the most popular logging frameworks (TensorBoard, Comet, etc...). To use a logger, simply pass it into the :class:`~pytorch_lightning.trainer.trainer.Trainer`. Lightning uses TensorBoard by default. .. testcode:: from pytorch_lightning import loggers as pl_loggers tb_logger = pl_loggers.TensorBoardLogger('logs/') trainer = Trainer(logger=tb_logger) Choose from any of the others such as MLflow, Comet, Neptune, WandB, ... .. testcode:: comet_logger = pl_loggers.CometLogger(save_dir='logs/') trainer = Trainer(logger=comet_logger) To use multiple loggers, simply pass in a ``list`` or ``tuple`` of loggers ... .. testcode:: tb_logger = pl_loggers.TensorBoardLogger('logs/') comet_logger = pl_loggers.CometLogger(save_dir='logs/') trainer = Trainer(logger=[tb_logger, comet_logger]) .. note:: By default, lightning logs every 50 steps. Use Trainer flags to :ref:`logging_frequency`. .. note:: All loggers log by default to `os.getcwd()`. To change the path without creating a logger set `Trainer(default_root_dir='/your/path/to/save/checkpoints')` ---------- ****************************** Logging from a LightningModule ****************************** Lightning offers automatic log functionalities for logging scalars, or manual logging for anything else. Automatic logging ================= Use the :func:`~~pytorch_lightning.core.lightning.LightningModule.log` method to log from anywhere in a :ref:`lightning_module`. .. code-block:: python def training_step(self, batch, batch_idx): self.log('my_metric', x) Depending on where log is called from, Lightning auto-determines the correct logging mode for you. \ But of course you can override the default behavior by manually setting the :func:`~~pytorch_lightning.core.lightning.LightningModule.log` parameters. .. code-block:: python def training_step(self, batch, batch_idx): self.log('my_loss', loss, on_step=True, on_epoch=True, prog_bar=True, logger=True) The :func:`~~pytorch_lightning.core.lightning.LightningModule.log` method has a few options: * `on_step`: Logs the metric at the current step. Defaults to `True` in :func:`~~pytorch_lightning.core.lightning.LightningModule.training_step`, and :func:`~pytorch_lightning.core.lightning.LightningModule.training_step_end`. * `on_epoch`: Automatically accumulates and logs at the end of the epoch. Defaults to True anywhere in validation or test loops, and in :func:`~~pytorch_lightning.core.lightning.LightningModule.training_epoch_end`. * `prog_bar`: Logs to the progress bar. * `logger`: Logs to the logger like Tensorboard, or any other custom logger passed to the :class:`~pytorch_lightning.trainer.trainer.Trainer`. .. note:: Setting `on_epoch=True` will accumulate your logged values over the full training epoch. Manual logging ============== If you want to log anything that is not a scalar, like histograms, text, images, etc... you may need to use the logger object directly. .. code-block:: python def training_step(...): ... # the logger you used (in this case tensorboard) tensorboard = self.logger.experiment tensorboard.add_image() tensorboard.add_histogram(...) tensorboard.add_figure(...) Access your logs ================ Once your training starts, you can view the logs by using your favorite logger or booting up the Tensorboard logs: .. code-block:: bash tensorboard --logdir ./lightning_logs ---------- ******************** Make a custom logger ******************** You can implement your own logger by writing a class that inherits from :class:`LightningLoggerBase`. Use the :func:`~pytorch_lightning.loggers.base.rank_zero_only` decorator to make sure that only the first process in DDP training logs data. .. testcode:: from pytorch_lightning.utilities import rank_zero_only from pytorch_lightning.loggers import LightningLoggerBase class MyLogger(LightningLoggerBase): def name(self): return 'MyLogger' def experiment(self): # Return the experiment object associated with this logger. pass def version(self): # Return the experiment version, int or str. return '0.1' @rank_zero_only def log_hyperparams(self, params): # params is an argparse.Namespace # your code to record hyperparameters goes here pass @rank_zero_only def log_metrics(self, metrics, step): # metrics is a dictionary of metric names and values # your code to record metrics goes here pass def save(self): # Optional. Any code necessary to save logger data goes here # If you implement this, remember to call `super().save()` # at the start of the method (important for aggregation of metrics) super().save() @rank_zero_only def finalize(self, status): # Optional. Any code that needs to be run after training # finishes goes here pass If you write a logger that may be useful to others, please send a pull request to add it to Lightning! ---------- .. _logging_frequency: ************************* Control logging frequency ************************* Logging frequency ================= It may slow training down to log every single batch. By default, Lightning logs every 50 rows, or 50 training steps. To change this behaviour, set the `log_every_n_steps` :class:`~pytorch_lightning.trainer.trainer.Trainer` flag. .. testcode:: k = 10 trainer = Trainer(log_every_n_steps=k) Log writing frequency ===================== Writing to a logger can be expensive, so by default Lightning write logs to disc or to the given logger every 100 training steps. To change this behaviour, set the interval at which you wish to flush logs to the filesystem using `log_every_n_steps` :class:`~pytorch_lightning.trainer.trainer.Trainer` flag. .. testcode:: k = 100 trainer = Trainer(flush_logs_every_n_steps=k) Unlike the `log_every_n_steps`, this argument does not apply to all loggers. The example shown here works with :class:`~pytorch_lightning.loggers.tensorboard.TensorBoardLogger`, which is the default logger in Lightning. ---------- ************ Progress Bar ************ You can add any metric to the progress bar using :func:`~~pytorch_lightning.core.lightning.LightningModule.log` method, setting `prog_bar=True`. .. code-block:: python def training_step(self, batch, batch_idx): self.log('my_loss', loss, prog_bar=True) Modifying the progress bar ========================== The progress bar by default already includes the training loss and version number of the experiment if you are using a logger. These defaults can be customized by overriding the :func:`~pytorch_lightning.core.lightning.LightningModule.get_progress_bar_dict` hook in your module. .. code-block:: python def get_progress_bar_dict(self): # don't show the version number items = super().get_progress_bar_dict() items.pop("v_num", None) return items ---------- ************************* Configure console logging ************************* Lightning logs useful information about the training process and user warnings to the console. You can retrieve the Lightning logger and change it to your liking. For example, increase the logging level to see fewer messages like so: .. code-block:: python import logging logging.getLogger("lightning").setLevel(logging.ERROR) Read more about custom Python logging `here `_. ---------- *********************** Logging hyperparameters *********************** When training a model, it's useful to know what hyperparams went into that model. When Lightning creates a checkpoint, it stores a key "hparams" with the hyperparams. .. code-block:: python lightning_checkpoint = torch.load(filepath, map_location=lambda storage, loc: storage) hyperparams = lightning_checkpoint['hparams'] Some loggers also allow logging the hyperparams used in the experiment. For instance, when using the TestTubeLogger or the TensorBoardLogger, all hyperparams will show in the `hparams tab `_. ---------- ************* Snapshot code ************* Loggers also allow you to snapshot a copy of the code used in this experiment. For example, TestTubeLogger does this with a flag: .. code-block:: python from pytorch_lightning.loggers import TestTubeLogger logger = TestTubeLogger('.', create_git_tag=True) ---------- ***************** Supported Loggers ***************** The following are loggers we support Comet ===== .. autoclass:: pytorch_lightning.loggers.comet.CometLogger :noindex: CSVLogger ========= .. autoclass:: pytorch_lightning.loggers.csv_logs.CSVLogger :noindex: MLFlow ====== .. autoclass:: pytorch_lightning.loggers.mlflow.MLFlowLogger :noindex: Neptune ======= .. autoclass:: pytorch_lightning.loggers.neptune.NeptuneLogger :noindex: Tensorboard ============ .. autoclass:: pytorch_lightning.loggers.tensorboard.TensorBoardLogger :noindex: Test-tube ========= .. autoclass:: pytorch_lightning.loggers.test_tube.TestTubeLogger :noindex: Weights and Biases ================== .. autoclass:: pytorch_lightning.loggers.wandb.WandbLogger :noindex: