Sacred logger

docs/source/conf.py

@@ -295,7 +295,8 @@ def setup(app):
             MOCK_REQUIRE_PACKAGES.append(pkg.rstrip())
 
 # TODO: better parse from package since the import name and package name may differ
-MOCK_MANUAL_PACKAGES = ['torch', 'torchvision', 'sklearn', 'test_tube', 'mlflow', 'comet_ml', 'wandb', 'neptune']
+MOCK_MANUAL_PACKAGES = ['torch', 'torchvision', 'sklearn', 'test_tube', 'mlflow', 'comet_ml', 'wandb', 'neptune',
+                        'sacred']
 autodoc_mock_imports = MOCK_REQUIRE_PACKAGES + MOCK_MANUAL_PACKAGES
 # for mod_name in MOCK_REQUIRE_PACKAGES:
 #     sys.modules[mod_name] = mock.Mock()

pytorch_lightning/logging/__init__.py

@@ -114,4 +114,10 @@ def any_lightning_module_function_or_hook(...):
 except ImportError:
     pass
 
+try:
+    from .sacred import SacredLogger
+    all.append("SacredLogger")
+except ImportError:
+    pass
+
 __all__ = all

pytorch_lightning/logging/sacred.py

@@ -0,0 +1,78 @@
+"""
+Log using `sacred <https://sacred.readthedocs.io/en/stable/index.html>'_
+.. code-block:: python
+    from pytorch_lightning.logging import SacredLogger
+    ex = Experiment() # initialize however you like
+    ex.main(your_main_fct)
+    ex.observers.append(
+        # add any observer you like
+    )
+    sacred_logger = SacredLogger(ex)
+    trainer = Trainer(logger=sacred_logger)
+Use the logger anywhere in you LightningModule as follows:
+.. code-block:: python
+    def train_step(...):
+        # example
+        self.logger.experiment.whatever_sacred_supports(...)
+    def any_lightning_module_function_or_hook(...):
+        self.logger.experiment.whatever_sacred_supports(...)
+"""
+
+from logging import getLogger
+
+try:
+    import sacred
+except ImportError:
+    raise ImportError('Missing sacred package.  Run `pip install sacred`')
+
+from pytorch_lightning.logging.base import LightningLoggerBase, rank_zero_only
+
+logger = getLogger(__name__)
+
+
+class SacredLogger(LightningLoggerBase):
+    def __init__(self, sacred_experiment):
+        """Initialize a sacred logger.
+
+        :param sacred.experiment.Experiment sacred_experiment: Required. Experiment object with desired observers
+        already appended.
+        """
+        super().__init__()
+        self.sacred_experiment = sacred_experiment
+        self.experiment_name = sacred_experiment.path
+        self._run_id = None
+
+    @property
+    def experiment(self):
+        return self.sacred_experiment
+
+    @property
+    def run_id(self):
+        if self._run_id is not None:
+            return self._run_id
+
+        self._run_id = self.sacred_experiment.current_run._id
+        return self._run_id
+
+    @rank_zero_only
+    def log_hyperparams(self, params):
+        # probably not needed bc. it is dealt with by sacred
+        pass
+
+    @rank_zero_only
+    def log_metrics(self, metrics, step=None):
+        for k, v in metrics.items():
+            if isinstance(v, str):
+                logger.warning(
+                    f"Discarding metric with string value {k}={v}"
+                )
+                continue
+            self.experiment.log_scalar(k, v, step)
+
+    @property
+    def name(self):
+        return self.experiment_name
+
+    @property
+    def version(self):
+        return self.run_id

tests/test_logging.py

@@ -386,3 +386,45 @@ def version(self):
     assert logger.hparams_logged == hparams
     assert logger.metrics_logged != {}
     assert logger.finalized_status == "success"
+
+
+def test_sacred_logger(tmpdir):
+    """Verify that basic functionality of sacred logger works."""
+    tutils.reset_seed()
+
+    try:
+        from pytorch_lightning.logging import SacredLogger
+    except ModuleNotFoundError:
+        return
+
+    try:
+        from sacred import Experiment
+    except ModuleNotFoundError:
+        return
+
+    hparams = tutils.get_hparams()
+    model = LightningTestModel(hparams)
+    sacred_dir = os.path.join(tmpdir, "sacredruns")
+
+    ex = Experiment()
+    ex_config = vars(hparams)
+    ex.add_config(ex_config)
+
+    @ex.main
+    def run_fct():
+        logger = SacredLogger(ex)
+
+        trainer_options = dict(
+            default_save_path=sacred_dir,
+            max_epochs=1,
+            train_percent_check=0.01,
+            logger=logger
+        )
+        trainer = Trainer(**trainer_options)
+        result = trainer.fit(model)
+        return result
+
+    result = ex.run()
+
+    print('result finished')
+    assert result.status == "COMPLETED", "Training failed"