TensorBoardLogger¶
-
class
pytorch_lightning.loggers.
TensorBoardLogger
(save_dir, name='default', version=None, log_graph=False, default_hp_metric=True, **kwargs)[source]¶ Bases:
pytorch_lightning.loggers.base.LightningLoggerBase
Log to local file system in TensorBoard format.
Implemented using
SummaryWriter
. Logs are saved toos.path.join(save_dir, name, version)
. This is the default logger in Lightning, it comes preinstalled.Example
>>> from pytorch_lightning import Trainer >>> from pytorch_lightning.loggers import TensorBoardLogger >>> logger = TensorBoardLogger("tb_logs", name="my_model") >>> trainer = Trainer(logger=logger)
- Parameters
name¶ (
Optional
[str
]) – Experiment name. Defaults to'default'
. If it is the empty string then no per-experiment subdirectory is used.version¶ (
Union
[int
,str
,None
]) – Experiment version. If version is not specified the logger inspects the save directory for existing versions, then automatically assigns the next available version. If it is a string then it is used as the run-specific subdirectory name, otherwise'version_${version}'
is used.log_graph¶ (
bool
) – Adds the computational graph to tensorboard. This requires that the user has defined the self.example_input_array attribute in their model.default_hp_metric¶ (
bool
) – Enables a placeholder metric with key hp_metric when log_hyperparams is called without a metric (otherwise calls to log_hyperparams without a metric are ignored).**kwargs¶ – Additional arguments like comment, filename_suffix, etc. used by
SummaryWriter
can be passed as keyword arguments in this logger.
-
log_graph
(model, input_array=None)[source]¶ Record model graph
- Parameters
model¶ (
LightningModule
) – lightning modelinput_array¶ – input passes to model.forward
-
log_metrics
(metrics, step=None)[source]¶ Records metrics. This method logs metrics as as soon as it received them. If you want to aggregate metrics for one specific step, use the
agg_and_log_metrics()
method.
-
property
experiment
¶ Actual tensorboard object. To use TensorBoard features in your
LightningModule
do the following.Example:
self.logger.experiment.some_tensorboard_function()
- Return type
SummaryWriter
-
property
log_dir
¶ The directory for this run’s tensorboard checkpoint. By default, it is named
'version_${self.version}'
but it can be overridden by passing a string value for the constructor’s version parameter instead ofNone
or an int.- Return type
-
property
root_dir
¶ Parent directory for all tensorboard checkpoint subdirectories. If the experiment name parameter is
None
or the empty string, no experiment subdirectory is used and the checkpoint will be saved in “save_dir/version_dir”- Return type
-
property
save_dir
¶ Return the root directory where experiment logs get saved, or None if the logger does not save data locally.