lightning/pytorch_lightning/loggers/test_tube.py

160 lines
4.7 KiB
Python

try:
from test_tube import Experiment
except ImportError:
raise ImportError('Missing test-tube package.')
from .base import LightningLoggerBase, rank_zero_only
class TestTubeLogger(LightningLoggerBase):
r"""
Log to local file system in TensorBoard format but using a nicer folder structure.
(see `full docs <https://williamfalcon.github.io/test-tube>`_).
"""
__test__ = False
def __init__(
self, save_dir, name="default", description=None, debug=False,
version=None, create_git_tag=False
):
r"""
.. _testTube:
Example
----------
.. code-block:: python
logger = TestTubeLogger("tt_logs", name="my_exp_name")
trainer = Trainer(logger=logger)
trainer.train(model)
Use the logger anywhere in you LightningModule as follows:
.. code-block:: python
def train_step(...):
# example
self.logger.experiment.whatever_method_summary_writer_supports(...)
def any_lightning_module_function_or_hook(...):
self.logger.experiment.add_histogram(...)
Args:
save_dir (str): Save directory
name (str): Experiment name. Defaults to "default".
description (str): A short snippet about this experiment
debug (bool): If True, it doesn't log anything
version (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
"""
super().__init__()
self.save_dir = save_dir
self._name = name
self.description = description
self.debug = debug
self._version = version
self.create_git_tag = create_git_tag
self._experiment = None
@property
def experiment(self):
r"""
Actual test-tube object. To use test-tube features do the following.
Example::
self.logger.experiment.some_test_tube_function()
"""
if self._experiment is not None:
return self._experiment
self._experiment = Experiment(
save_dir=self.save_dir,
name=self._name,
debug=self.debug,
version=self.version,
description=self.description,
create_git_tag=self.create_git_tag,
rank=self.rank,
)
return self._experiment
@rank_zero_only
def log_hyperparams(self, params):
# TODO: HACK figure out where this is being set to true
self.experiment.debug = self.debug
self.experiment.argparse(params)
@rank_zero_only
def log_metrics(self, metrics, step=None):
# TODO: HACK figure out where this is being set to true
self.experiment.debug = self.debug
self.experiment.log(metrics, global_step=step)
@rank_zero_only
def save(self):
# TODO: HACK figure out where this is being set to true
self.experiment.debug = self.debug
self.experiment.save()
@rank_zero_only
def finalize(self, status):
# TODO: HACK figure out where this is being set to true
self.experiment.debug = self.debug
self.save()
self.close()
@rank_zero_only
def close(self):
# TODO: HACK figure out where this is being set to true
self.experiment.debug = self.debug
if not self.debug:
exp = self.experiment
exp.close()
@property
def rank(self):
return self._rank
@rank.setter
def rank(self, value):
self._rank = value
if self._experiment is not None:
self.experiment.rank = value
@property
def name(self):
if self._experiment is None:
return self._name
else:
return self.experiment.name
@property
def version(self):
if self._experiment is None:
return self._version
else:
return self.experiment.version
# Test tube experiments are not pickleable, so we need to override a few
# methods to get DDP working. See
# https://docs.python.org/3/library/pickle.html#handling-stateful-objects
# for more info.
def __getstate__(self):
state = self.__dict__.copy()
state["_experiment"] = self.experiment.get_meta_copy()
return state
def __setstate__(self, state):
self._experiment = state["_experiment"].get_non_ddp_exp()
del state["_experiment"]
self.__dict__.update(state)