Fix loggers and update docs (Lightning-AI#964)

* Fix loggers and update docs

* Update trainer.py

Author: ethanwharris

docs/source/experiment_logging.rst

@@ -22,13 +22,13 @@ To use CometLogger as your logger do the following.
     )
    trainer = Trainer(logger=comet_logger)
 
-The CometLogger is available anywhere in your LightningModule
+The CometLogger is available anywhere except ``__init__`` in your LightningModule
 
 .. code-block:: python
 
    class MyModule(pl.LightningModule):
 
-      def __init__(self, ...):
+      def any_lightning_module_function_or_hook(self, ...):
          some_img = fake_image()
          self.logger.experiment.add_image('generated_images', some_img, 0)
 
@@ -52,13 +52,13 @@ To use Neptune.ai as your logger do the following.
     )
    trainer = Trainer(logger=neptune_logger)
 
-The Neptune.ai is available anywhere in your LightningModule
+The Neptune.ai is available anywhere except ``__init__`` in your LightningModule
 
 .. code-block:: python
 
    class MyModule(pl.LightningModule):
 
-      def __init__(self, ...):
+      def any_lightning_module_function_or_hook(self, ...):
          some_img = fake_image()
          self.logger.experiment.add_image('generated_images', some_img, 0)
 
@@ -76,13 +76,13 @@ To use `Tensorboard <https://pytorch.org/docs/stable/tensorboard.html>`_ as your
    logger = TensorBoardLogger("tb_logs", name="my_model")
    trainer = Trainer(logger=logger)
 
-The TensorBoardLogger is available anywhere in your LightningModule
+The TensorBoardLogger is available anywhere except ``__init__`` in your LightningModule
 
 .. code-block:: python
 
    class MyModule(pl.LightningModule):
 
-      def __init__(self, ...):
+      def any_lightning_module_function_or_hook(self, ...):
          some_img = fake_image()
          self.logger.experiment.add_image('generated_images', some_img, 0)
 
@@ -102,13 +102,13 @@ To use TestTube as your logger do the following.
    logger = TestTubeLogger("tb_logs", name="my_model")
    trainer = Trainer(logger=logger)
 
-The TestTubeLogger is available anywhere in your LightningModule
+The TestTubeLogger is available anywhere except ``__init__`` in your LightningModule
 
 .. code-block:: python
 
    class MyModule(pl.LightningModule):
 
-      def __init__(self, ...):
+      def any_lightning_module_function_or_hook(self, ...):
          some_img = fake_image()
          self.logger.experiment.add_image('generated_images', some_img, 0)
 
@@ -127,13 +127,13 @@ To use Wandb as your logger do the following.
    wandb_logger = WandbLogger()
    trainer = Trainer(logger=wandb_logger)
 
-The Wandb logger is available anywhere in your LightningModule
+The Wandb logger is available anywhere except ``__init__`` in your LightningModule
 
 .. code-block:: python
 
    class MyModule(pl.LightningModule):
 
-      def __init__(self, ...):
+      def any_lightning_module_function_or_hook(self, ...):
          some_img = fake_image()
          self.logger.experiment.add_image('generated_images', some_img, 0)
 
@@ -151,12 +151,17 @@ PyTorch-Lightning supports use of multiple loggers, just pass a list to the `Tra
    logger2 = TestTubeLogger("tt_logs", name="my_model")
    trainer = Trainer(logger=[logger1, logger2])
    
-The loggers are available as a list anywhere in your LightningModule
+The loggers are available as a list anywhere except ``__init__`` in your LightningModule
 
 .. code-block:: python
 
    class MyModule(pl.LightningModule):
 
-      def __init__(self, ...):
+      def any_lightning_module_function_or_hook(self, ...):
          some_img = fake_image()
+
+         # Option 1
          self.logger.experiment[0].add_image('generated_images', some_img, 0)
+
+         # Option 2
+         self.logger[0].experiment.add_image('generated_images', some_img, 0)

pytorch_lightning/loggers/__init__.py

@@ -1,6 +1,7 @@
 """
 Lightning supports most popular logging frameworks (Tensorboard, comet, weights and biases, etc...).
-To use a logger, simply pass it into the trainer.
+To use a logger, simply pass it into the trainer. To use multiple loggers, simply pass in a ``list``
+or ``tuple`` of loggers.
 
 .. code-block:: python
 
@@ -14,14 +15,19 @@
     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')
+    # or pass a list
+    tb_logger = loggers.TensorBoardLogger()
+    comet_logger = loggers.CometLogger()
+    trainer = Trainer(logger=[tb_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
+``LightningLoggerBase``. Use the ``rank_zero_only`` decorator to make sure that
 only the first process in DDP training logs data.
 
 .. code-block:: python
@@ -52,13 +58,13 @@ def finalize(self, status):
             # finishes goes here
 
 
-If you write a logger than may be useful to others, please send
+If you write a logger that 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:
+Call the logger anywhere except ``__init__`` in your LightningModule by doing:
 
 .. code-block:: python
 
@@ -69,6 +75,8 @@ def train_step(...):
     def any_lightning_module_function_or_hook(...):
         self.logger.experiment.add_histogram(...)
 
+Read more in the `Experiment Logging use case <./experiment_logging.html>`_.
+
 Supported Loggers
 -----------------
 """
@@ -77,7 +85,7 @@ def any_lightning_module_function_or_hook(...):
 from .base import LightningLoggerBase, LoggerCollection, rank_zero_only
 from .tensorboard import TensorBoardLogger
 
-__all__ = ['TensorBoardLogger', 'LoggerCollection']
+__all__ = ['TensorBoardLogger']
 
 try:
     # needed to prevent ImportError and duplicated logs.

pytorch_lightning/loggers/base.py

@@ -100,6 +100,9 @@ def __init__(self, logger_iterable: Iterable[LightningLoggerBase]):
         super().__init__()
         self._logger_iterable = logger_iterable
 
+    def __getitem__(self, index: int) -> LightningLoggerBase:
+        return [logger for logger in self._logger_iterable][index]
+
     @property
     def experiment(self) -> List[Any]:
         return [logger.experiment() for logger in self._logger_iterable]

pytorch_lightning/trainer/trainer.py

@@ -937,6 +937,9 @@ def fit(
             # feed to .fit()
 
         """
+        # bind logger
+        model.logger = self.logger
+
         # Fit begin callbacks
         self.on_fit_start()
 
@@ -1065,10 +1068,8 @@ def run_pretrain_routine(self, model: LightningModule):
         # set local properties on the model
         self.copy_trainer_model_properties(ref_model)
 
-        # link up experiment object
+        # log hyper-parameters
         if self.logger is not None:
-            ref_model.logger = self.logger
-
             # save exp to get started
             if hasattr(ref_model, "hparams"):
                 self.logger.log_hyperparams(ref_model.hparams)