added loggers

pytorch_lightning/logging/__init__.py

@@ -77,25 +77,8 @@ from os import environ
 from .base import LightningLoggerBase, rank_zero_only
 from .tensorboard import TensorBoardLogger
 
-all = []
+all = ['TensorBoardLogger']
 
-try:
-    from .test_tube import TestTubeLogger
-    all.append('TestTubeLogger')
-except ImportError:
-    pass
-
-try:
-    from .mlflow import MLFlowLogger
-    all.append('MLFlowLogger')
-except ImportError:
-    pass
-
-try:
-    from .wandb import WandbLogger
-    all.append('WandbLogger')
-except ImportError:
-    pass
 try:
     # needed to prevent ImportError and duplicated logs.
     environ["COMET_DISABLE_AUTO_LOGGING"] = "1"
@@ -105,10 +88,30 @@ try:
 except ImportError:
     del environ["COMET_DISABLE_AUTO_LOGGING"]
 
+try:
+    from .mlflow import MLFlowLogger
+    all.append('MLFlowLogger')
+except ImportError:
+    pass
+
 try:
     from .neptune import NeptuneLogger
     all.append('NeptuneLogger')
 except ImportError:
     pass
 
+try:
+    from .test_tube import TestTubeLogger
+    all.append('TestTubeLogger')
+except ImportError:
+    pass
+
+try:
+    from .wandb import WandbLogger
+    all.append('WandbLogger')
+except ImportError:
+    pass
+
+
+
 __all__ = all

pytorch_lightning/logging/comet.py

@@ -1,12 +1,11 @@
 """
-Log using `comet <https://www.comet.ml>`_
-
-Comet logger can be used in either online or offline mode.
-To log in online mode, CometLogger requries an API key:
-
 .. code-block:: python
 
+    # -------------------
+    # ONLINE MODE
+    # -------------------
     from pytorch_lightning.logging import CometLogger
+
     # arguments made to CometLogger are passed on to the comet_ml.Experiment class
     comet_logger = CometLogger(
         api_key=os.environ["COMET_KEY"],
@@ -17,10 +16,9 @@ To log in online mode, CometLogger requries an API key:
     )
     trainer = Trainer(logger=comet_logger)
 
-To log in offline mode, CometLogger requires a path to a local directory:
-
-.. code-block:: python
-
+    # -------------------
+    # OFFLINE MODE
+    # -------------------
     from pytorch_lightning.logging import CometLogger
     # arguments made to CometLogger are passed on to the comet_ml.Experiment class
     comet_logger = CometLogger(
@@ -71,18 +69,54 @@ logger = getLogger(__name__)
 class CometLogger(LightningLoggerBase):
     def __init__(self, api_key=None, save_dir=None, workspace=None,
                  rest_api_key=None, project_name=None, experiment_name=None, **kwargs):
-        """Initialize a Comet.ml logger.
+        r"""
+
+        Log using `comet <https://www.comet.ml>`_.
+
         Requires either an API Key (online mode) or a local directory path (offline mode)
 
-        :param str api_key: Required in online mode. API key, found on Comet.ml
-        :param str save_dir: Required in offline mode. The path for the directory to save local comet logs
-        :param str workspace: Optional. Name of workspace for this user
-        :param str project_name: Optional. Send your experiment to a specific project.
-        Otherwise will be sent to Uncategorized Experiments.
-        If project name does not already exists Comet.ml will create a new project.
-        :param str rest_api_key: Optional. Rest API key found in Comet.ml settings.
-        This is used to determine version number
-        :param str experiment_name: Optional. String representing the name for this particular experiment on Comet.ml
+        .. code-block:: python
+
+            # ONLINE MODE
+            from pytorch_lightning.logging import CometLogger
+
+            # arguments made to CometLogger are passed on to the comet_ml.Experiment class
+            comet_logger = CometLogger(
+                api_key=os.environ["COMET_KEY"],
+                workspace=os.environ["COMET_WORKSPACE"], # Optional
+                project_name="default_project", # Optional
+                rest_api_key=os.environ["COMET_REST_KEY"], # Optional
+                experiment_name="default" # Optional
+            )
+            trainer = Trainer(logger=comet_logger)
+
+
+        .. code-block:: python
+
+            # OFFLINE MODE
+            from pytorch_lightning.logging import CometLogger
+
+            # arguments made to CometLogger are passed on to the comet_ml.Experiment class
+            comet_logger = CometLogger(
+                save_dir=".",
+                workspace=os.environ["COMET_WORKSPACE"], # Optional
+                project_name="default_project", # Optional
+                rest_api_key=os.environ["COMET_REST_KEY"], # Optional
+                experiment_name="default" # Optional
+            )
+            trainer = Trainer(logger=comet_logger)
+
+        Args:
+            api_key (str): Required in online mode. API key, found on Comet.ml
+            save_dir (str): Required in offline mode. The path for the directory to save local comet logs
+            workspace (str): Optional. Name of workspace for this user
+            project_name (str): Optional. Send your experiment to a specific project.
+            Otherwise will be sent to Uncategorized Experiments.
+            If project name does not already exists Comet.ml will create a new project.
+            rest_api_key (str): Optional. Rest API key found in Comet.ml settings.
+                This is used to determine version number
+            experiment_name (str): Optional. String representing the name for this particular experiment on Comet.ml
+
         """
         super().__init__()
         self._experiment = None
@@ -124,6 +158,15 @@ class CometLogger(LightningLoggerBase):
 
     @property
     def experiment(self):
+        r"""
+
+        Actual comet object. To use comet features do the following.
+
+        Example::
+
+            self.logger.experiment.some_comet_function()
+
+        """
         if self._experiment is not None:
             return self._experiment
 

pytorch_lightning/logging/mlflow.py

@@ -47,6 +47,15 @@ class MLFlowLogger(LightningLoggerBase):
 
     @property
     def experiment(self):
+        r"""
+
+        Actual mlflow object. To use mlflow features do the following.
+
+        Example::
+
+            self.logger.experiment.some_mlflow_function()
+
+        """
         return self._mlflow_client
 
     @property

pytorch_lightning/logging/neptune.py

@@ -111,6 +111,16 @@ class NeptuneLogger(LightningLoggerBase):
 
     @property
     def experiment(self):
+        r"""
+
+        Actual neptune object. To use neptune features do the following.
+
+        Example::
+
+            self.logger.experiment.some_neptune_function()
+
+        """
+
         if self._experiment is not None:
             return self._experiment
         else:

pytorch_lightning/logging/tensorboard.py

@@ -45,10 +45,15 @@ class TensorBoardLogger(LightningLoggerBase):
 
     @property
     def experiment(self):
-        """The underlying :class:`torch.utils.tensorboard.SummaryWriter`.
+        r"""
 
-        :rtype: torch.utils.tensorboard.SummaryWriter
-        """
+         Actual tensorboard object. To use tensorboard features do the following.
+
+         Example::
+
+             self.logger.experiment.some_tensorboard_function()
+
+         """
         if self._experiment is not None:
             return self._experiment
 

pytorch_lightning/logging/test_tube.py

@@ -55,6 +55,16 @@ class TestTubeLogger(LightningLoggerBase):
 
     @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
 

pytorch_lightning/logging/wandb.py

@@ -68,6 +68,15 @@ class WandbLogger(LightningLoggerBase):
 
     @property
     def experiment(self):
+        r"""
+
+          Actual wandb object. To use wandb features do the following.
+
+          Example::
+
+              self.logger.experiment.some_wandb_function()
+
+          """
         if self._experiment is None:
             if self._offline:
                 os.environ["WANDB_MODE"] = "dryrun"