pytorch_lightning.loggers.tensorboard module

TensorBoard

class pytorch_lightning.loggers.tensorboard.TensorBoardLogger(save_dir, name='default', version=None, log_graph=True, **kwargs)

Bases: pytorch_lightning.loggers.base.LightningLoggerBase

Log to local file system in TensorBoard format. Implemented using SummaryWriter. Logs are saved to os.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

• save_dir (str) – Save directory

• 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.

• **kwargs – Other arguments are passed directly to the SummaryWriter constructor.

_get_next_version()

finalize(status)

Do any processing that is necessary to finalize an experiment.

Parameters

status (str) – Status that the experiment finished with (e.g. success, failed, aborted)

Return type

None

log_graph(model, input_array=None)

Record model graph

Parameters

• model (LightningModule) – lightning model

• input_array – input passes to model.forward

log_hyperparams(params, metrics=None)

Record hyperparameters.

Parameters

params (Union[Dict[str, Any], Namespace]) – Namespace containing the hyperparameters

Return type

None

log_metrics(metrics, step=None)

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.

Parameters

• metrics (Dict[str, float]) – Dictionary with metric names as keys and measured quantities as values

• step (Optional[int]) – Step number at which the metrics should be recorded

Return type

None

save()

Save log data.

Return type

None

NAME_HPARAMS_FILE = 'hparams.yaml'

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 of None or an int.

Return type

str

property name

Return the experiment name.

Return type

str

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

str

property save_dir

Return the root directory where experiment logs get saved, or None if the logger does not save data locally.

Return type

Optional[str]

property version

Return the experiment version.

Return type

int