test_tube¶

Classes

TestTubeLogger

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

Test Tube Logger¶

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

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()[source]¶

Do any cleanup that is necessary to close an experiment.

Return type: None

finalize(status)[source]¶

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)[source]¶

Record model graph

Parameters

model¶ (LightningModule) – lightning model
input_array¶ – input passes to model.forward

log_hyperparams(params)[source]¶

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)[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.

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()[source]¶

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()

Return type: Experiment

property name: str¶

Gets the experiment name.

Return type: str
Returns: The experiment name if the experiment exists, else the name specified in the constructor.

property save_dir: Optional[str]¶

Gets the save directory.

Return type: Optional[str]
Returns: The path to the save directory.

property version: int¶

Gets the experiment version.

Return type: int
Returns: The experiment version if the experiment exists, else the next version.