tensorboard

Classes

TensorBoardLogger

Log to local file system in TensorBoard format.

TensorBoard Logger

class pytorch_lightning.loggers.tensorboard.TensorBoardLogger(save_dir, name='default', version=None, log_graph=False, default_hp_metric=True, prefix='', sub_dir=None, **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.

• sub_dir (Optional[str]) – Sub-directory to group TensorBoard logs. If a sub_dir argument is passed then logs are saved in /save_dir/version/sub_dir/. Defaults to None in which logs are saved in /save_dir/version/.

• 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).

• prefix (str) – A string to put at the beginning of metric keys.

• **kwargs – Additional arguments like comment, filename_suffix, etc. used by SummaryWriter can be passed as keyword arguments in this logger.

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. TensorBoard logs with and without saved hyperparameters are incompatible, the hyperparameters are then not displayed in the TensorBoard. Please delete or move the previously saved logs to display the new ones with hyperparameters.

Parameters

• params (Union[Dict[str, Any], Namespace]) – a dictionary-like container with the hyperparameters

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

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

property experiment: torch.utils.tensorboard.SummaryWriter

Actual tensorboard object. To use TensorBoard features in your LightningModule do the following.

Example:

self.logger.experiment.some_tensorboard_function()

property log_dir: str

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.

property name: str

Return the experiment name.

property root_dir: str

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”

property save_dir: Optional[str]

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

property version: int

Return the experiment version.