*`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`.
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
