pytorch_lightning.loggers.tensorboard module¶
TensorBoard¶
-
class
pytorch_lightning.loggers.tensorboard.
TensorBoardLogger
(save_dir, name='default', version=None, **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_${version}'
is used.**kwargs¶ – Other arguments are passed directly to the
SummaryWriter
constructor.
-
log_metrics
(metrics, step=None)[source]¶ Records metrics. This method logs metrics as as soon as it received them. If you want to aggregate metrics for one specific step, use the
agg_and_log_metrics()
method.
-
property
experiment
[source]¶ Actual tensorboard object. To use TensorBoard features in your
LightningModule
do the following.Example:
self.logger.experiment.some_tensorboard_function()
- Return type
SummaryWriter
-
property
log_dir
[source]¶ The directory for this run’s tensorboard checkpoint. By default, it is named
'version_${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