TestTubeLogger

class pytorch_lightning.loggers.TestTubeLogger(save_dir, name='default', description=None, debug=False, version=None, create_git_tag=False, log_graph=False, prefix='')

Bases: pytorch_lightning.loggers.base.LightningLoggerBase

Log to local file system in TensorBoard format but using a nicer folder structure (see full docs).

Install it with pip:

pip install test_tube

from pytorch_lightning import Trainer
from pytorch_lightning.loggers import TestTubeLogger

logger = TestTubeLogger("tt_logs", name="my_exp_name")
trainer = Trainer(logger=logger)

Use the logger anywhere in your LightningModule as follows:

from pytorch_lightning import LightningModule


class LitModel(LightningModule):
    def training_step(self, batch, batch_idx):
        # example
        self.logger.experiment.whatever_method_summary_writer_supports(...)

    def any_lightning_module_function_or_hook(self):
        self.logger.experiment.add_histogram(...)

Parameters

• save_dir (str) – Save directory

• name (str) – Experiment name. Defaults to 'default'.

• description (Optional[str]) – A short snippet about this experiment

• debug (bool) – If True, it doesn’t log anything.

• version (Optional[int]) – Experiment version. If version is not specified the logger inspects the save directory for existing versions, then automatically assigns the next available version.

• create_git_tag (bool) – If True creates a git tag to save the code used in this experiment.

• 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.

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

Raises

ImportError – If required TestTube package is not installed on the device.

close()

Do any cleanup that is necessary to close an experiment.

Return type

None

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)

Record hyperparameters.

Parameters

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

• args – Optional positional arguments, depends on the specific logger being used

• kwargs – Optional keywoard arguments, depends on the specific logger being used

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: test_tube.Experiment

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

Example:

self.logger.experiment.some_test_tube_function()

property name: str

Return the experiment name.

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.