rename logging -> loggers

pytorch_lightning/loggers/__init__.py

@@ -0,0 +1,113 @@
+"""
+Lightning supports most popular logging frameworks (Tensorboard, comet, weights and biases, etc...).
+To use a logger, simply pass it into the trainer.
+
+.. code-block:: python
+
+    from pytorch_lightning import loggers
+
+    # lightning uses tensorboard by default
+    tb_logger = loggers.TensorBoardLogger()
+    trainer = Trainer(logger=tb_logger)
+
+    # or choose from any of the others such as MLFlow, Comet, Neptune, Wandb
+    comet_logger = loggers.CometLogger()
+    trainer = Trainer(logger=comet_logger)
+
+.. note:: All loggers log by default to `os.getcwd()`. To change the path without creating a logger set
+    Trainer(default_save_path='/your/path/to/save/checkpoints')
+
+Custom logger
+-------------
+
+You can implement your own logger by writing a class that inherits from
+`LightningLoggerBase`. Use the `rank_zero_only` decorator to make sure that
+only the first process in DDP training logs data.
+
+.. code-block:: python
+
+    from pytorch_lightning.loggers import LightningLoggerBase, rank_zero_only
+
+    class MyLogger(LightningLoggerBase):
+
+        @rank_zero_only
+        def log_hyperparams(self, params):
+            # params is an argparse.Namespace
+            # your code to record hyperparameters goes here
+            pass
+
+        @rank_zero_only
+        def log_metrics(self, metrics, step):
+            # metrics is a dictionary of metric names and values
+            # your code to record metrics goes here
+            pass
+
+        def save(self):
+            # Optional. Any code necessary to save logger data goes here
+            pass
+
+        @rank_zero_only
+        def finalize(self, status):
+            # Optional. Any code that needs to be run after training
+            # finishes goes here
+
+
+If you write a logger than may be useful to others, please send
+a pull request to add it to Lighting!
+
+Using loggers
+-------------
+
+Call the logger anywhere from your LightningModule by doing:
+
+.. 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(...)
+
+Supported Loggers
+-----------------
+"""
+from os import environ
+
+from .base import LightningLoggerBase, rank_zero_only
+from .tensorboard import TensorBoardLogger
+
+__all__ = ['TensorBoardLogger']
+
+try:
+    # needed to prevent ImportError and duplicated logs.
+    environ["COMET_DISABLE_AUTO_LOGGING"] = "1"
+
+    from .comet import CometLogger
+    __all__.append('CometLogger')
+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

pytorch_lightning/logging/comet.py → pytorch_lightning/loggers/comet.py

@@ -31,7 +31,7 @@ def __init__(self, api_key=None, save_dir=None, workspace=None,
         .. code-block:: python
 
             # ONLINE MODE
-            from pytorch_lightning.logging import CometLogger
+            from pytorch_lightning.loggers import CometLogger
 
             # arguments made to CometLogger are passed on to the comet_ml.Experiment class
             comet_logger = CometLogger(
@@ -47,7 +47,7 @@ def __init__(self, api_key=None, save_dir=None, workspace=None,
         .. code-block:: python
 
             # OFFLINE MODE
-            from pytorch_lightning.logging import CometLogger
+            from pytorch_lightning.loggers import CometLogger
 
             # arguments made to CometLogger are passed on to the comet_ml.Experiment class
             comet_logger = CometLogger(

pytorch_lightning/logging/mlflow.py → pytorch_lightning/loggers/mlflow.py

@@ -3,7 +3,7 @@
 
 .. code-block:: python
 
-    from pytorch_lightning.logging import MLFlowLogger
+    from pytorch_lightning.loggers import MLFlowLogger
     mlf_logger = MLFlowLogger(
         experiment_name="default",
         tracking_uri="file:/."

pytorch_lightning/logging/neptune.py → pytorch_lightning/loggers/neptune.py

@@ -6,7 +6,7 @@
 
 .. code-block:: python
 
-    from pytorch_lightning.logging import NeptuneLogger
+    from pytorch_lightning.loggers import NeptuneLogger
     # arguments made to NeptuneLogger are passed on to the neptune.experiments.Experiment class
 
     neptune_logger = NeptuneLogger(
@@ -66,7 +66,7 @@ def __init__(self, api_key=None, project_name=None, offline_mode=False,
         .. code-block:: python
 
             # ONLINE MODE
-            from pytorch_lightning.logging import NeptuneLogger
+            from pytorch_lightning.loggers import NeptuneLogger
             # arguments made to NeptuneLogger are passed on to the neptune.experiments.Experiment class
 
             neptune_logger = NeptuneLogger(
@@ -81,7 +81,7 @@ def __init__(self, api_key=None, project_name=None, offline_mode=False,
         .. code-block:: python
 
             # OFFLINE MODE
-            from pytorch_lightning.logging import NeptuneLogger
+            from pytorch_lightning.loggers import NeptuneLogger
             # arguments made to NeptuneLogger are passed on to the neptune.experiments.Experiment class
 
             neptune_logger = NeptuneLogger(

pytorch_lightning/logging/test_tube.py → pytorch_lightning/loggers/test_tube.py

@@ -6,7 +6,7 @@
 
 .. code-block:: python
 
-    from pytorch_lightning.logging import TestTubeLogger
+    from pytorch_lightning.loggers import TestTubeLogger
     tt_logger = TestTubeLogger(
         save_dir=".",
         name="default",

pytorch_lightning/logging/wandb.py → pytorch_lightning/loggers/wandb.py

@@ -25,7 +25,7 @@ class WandbLogger(LightningLoggerBase):
     --------
     .. code-block:: python
 
-        from pytorch_lightning.logging import WandbLogger
+        from pytorch_lightning.loggers import WandbLogger
         from pytorch_lightning import Trainer
 
         wandb_logger = WandbLogger()

pytorch_lightning/logging/__init__.py

@@ -1,115 +1,10 @@
 """
-Lightning supports most popular logging frameworks (Tensorboard, comet, weights and biases, etc...).
-To use a logger, simply pass it into the trainer.
-
-.. code-block:: python
-
-    from pytorch_lightning import logging
-
-    # lightning uses tensorboard by default
-    tb_logger = logging.TensorBoardLogger()
-    trainer = Trainer(logger=tb_logger)
-
-    # or choose from any of the others such as MLFlow, Comet, Neptune, Wandb
-    comet_logger = logging.CometLogger()
-    trainer = Trainer(logger=comet_logger)
-
-.. note:: All loggers log by default to `os.getcwd()`. To change the path without creating a logger set
-    Trainer(default_save_path='/your/path/to/save/checkpoints')
-
-Custom logger
--------------
-
-You can implement your own logger by writing a class that inherits from
-`LightningLoggerBase`. Use the `rank_zero_only` decorator to make sure that
-only the first process in DDP training logs data.
-
-.. code-block:: python
-
-    from pytorch_lightning.logging import LightningLoggerBase, rank_zero_only
-
-    class MyLogger(LightningLoggerBase):
-
-        @rank_zero_only
-        def log_hyperparams(self, params):
-            # params is an argparse.Namespace
-            # your code to record hyperparameters goes here
-            pass
-
-        @rank_zero_only
-        def log_metrics(self, metrics, step):
-            # metrics is a dictionary of metric names and values
-            # your code to record metrics goes here
-            pass
-
-        def save(self):
-            # Optional. Any code necessary to save logger data goes here
-            pass
-
-        @rank_zero_only
-        def finalize(self, status):
-            # Optional. Any code that needs to be run after training
-            # finishes goes here
-
-
-If you write a logger than may be useful to others, please send
-a pull request to add it to Lighting!
-
-Using loggers
--------------
-
-Call the logger anywhere from your LightningModule by doing:
-
-.. 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(...)
-
-Supported Loggers
------------------
+.. warning:: `logging` package has been renamed to `loggers` since v0.6.1 and will be removed in v0.8.0
 """
-from os import environ
-
-from .base import LightningLoggerBase, rank_zero_only
-from .tensorboard import TensorBoardLogger
-
-loggers = ['TensorBoardLogger']
-
-try:
-    # needed to prevent ImportError and duplicated logs.
-    environ["COMET_DISABLE_AUTO_LOGGING"] = "1"
-
-    from .comet import CometLogger
-    loggers.append('CometLogger')
-except ImportError:
-    del environ["COMET_DISABLE_AUTO_LOGGING"]
-
-try:
-    from .mlflow import MLFlowLogger
-    loggers.append('MLFlowLogger')
-except ImportError:
-    pass
-
-try:
-    from .neptune import NeptuneLogger
-    loggers.append('NeptuneLogger')
-except ImportError:
-    pass
 
-try:
-    from .test_tube import TestTubeLogger
-    loggers.append('TestTubeLogger')
-except ImportError:
-    pass
+import warnings
 
-try:
-    from .wandb import WandbLogger
-    loggers.append('WandbLogger')
-except ImportError:
-    pass
+warnings.warn("`logging` package has been renamed to `loggers` since v0.6.1"
+              " and will be removed in v0.8.0", DeprecationWarning)
 
-__all__ = loggers
+from pytorch_lightning.loggers import *  # noqa: E402

pytorch_lightning/trainer/trainer.py

@@ -97,7 +97,7 @@ def __init__(
             logger (:class:`.Logger`): Logger for experiment tracking.
                 Example::
 
-                    from pytorch_lightning.logging import TensorBoardLogger
+                    from pytorch_lightning.loggers import TensorBoardLogger
 
                     # default logger used by trainer
                     logger = TensorBoardLogger(

pytorch_lightning/trainer/training_io.py

@@ -39,7 +39,7 @@
 .. code-block:: python
 
     from pytorch_lightning import Trainer
-    from pytorch_lightning.logging import TestTubeLogger
+    from pytorch_lightning.loggers import TestTubeLogger
 
     logger = TestTubeLogger(
         save_dir='./savepath',