pytorch_lightning.loggers.test_tube module¶
Test Tube¶
-
class
pytorch_lightning.loggers.test_tube.
TestTubeLogger
(save_dir, name='default', description=None, debug=False, version=None, create_git_tag=False)[source]¶ 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
Example
>>> 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
description¶ (
Optional
[str
]) – A short snippet about this experimentversion¶ (
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
) – IfTrue
creates a git tag to save the code used in this experiment.
-
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 TestTube object. To use TestTube features in your
LightningModule
do the following.Example:
self.logger.experiment.some_test_tube_function()
- Return type
Experiment