Documentation Fixes [skip ci] (#3955)

* Documentation Fixes

Just did some scanning for errors. Fixed indentation spelling, and grammar changes.

* Apply suggestions from code review

Co-authored-by: Rohit Gupta <[email protected]>

Author: captain695

docs/source/bolts.rst

@@ -11,21 +11,21 @@ In bolts we have:
 
 - A collection of pretrained state-of-the-art models.
 - A collection of models designed to bootstrap your research.
-- A collection of Callbacks, transforms, full datasets.
+- A collection of callbacks, transforms, full datasets.
 - All models work on CPUs, TPUs, GPUs and 16-bit precision.
 
 -----------------
 
 Quality control
 ---------------
-Bolts are built-by the Lightning community and contributed to bolts.
+The Lightning community builds bolts and contributes them to Bolts.
 The lightning team guarantees that contributions are:
 
-- Rigorously Tested (CPUs, GPUs, TPUs)
-- Rigorously Documented
-- Standardized via PyTorch Lightning
-- Optimized for speed
-- Checked for correctness
+- Rigorously Tested (CPUs, GPUs, TPUs).
+- Rigorously Documented.
+- Standardized via PyTorch Lightning.
+- Optimized for speed.
+- Checked for correctness.
 
 ---------
 

docs/source/early_stopping.rst

@@ -52,7 +52,7 @@ To enable it:
     )
     trainer = Trainer(callbacks=[early_stop_callback])
 
-In case you need early stopping in a different part of training, subclass EarlyStopping
+In case you need early stopping in a different part of training, subclass :class:`~pytorch_lightning.callbacks.early_stopping.EarlyStopping`
 and change where it is called:
 
 .. testcode::
@@ -68,10 +68,11 @@ and change where it is called:
             self._run_early_stopping_check(trainer, pl_module)
 
 .. note::
-   The EarlyStopping callback runs at the end of every validation epoch,
+   The :class:`~pytorch_lightning.callbacks.early_stopping.EarlyStopping` callback runs
+   at the end of every validation epoch,
    which, under the default configuration, happen after every training epoch.
    However, the frequency of validation can be modified by setting various parameters
-   on the :class:`~pytorch_lightning.trainer.trainer.Trainer`,
+   in the :class:`~pytorch_lightning.trainer.trainer.Trainer`,
    for example :paramref:`~pytorch_lightning.trainer.trainer.Trainer.check_val_every_n_epoch`
    and :paramref:`~pytorch_lightning.trainer.trainer.Trainer.val_check_interval`.
    It must be noted that the `patience` parameter counts the number of

docs/source/fast_training.rst

@@ -40,7 +40,7 @@ Set validation check frequency within 1 training epoch
 ------------------------------------------------------
 For large datasets it's often desirable to check validation multiple times within a training loop.
 Pass in a float to check that often within 1 training epoch. Pass in an int k to check every k training batches.
-Must use an int if using an IterableDataset.
+Must use an `int` if using an `IterableDataset`.
 
 .. testcode::
 
@@ -50,7 +50,7 @@ Must use an int if using an IterableDataset.
     # check every .25 of an epoch
     trainer = Trainer(val_check_interval=0.25)
 
-    # check every 100 train batches (ie: for IterableDatasets or fixed frequency)
+    # check every 100 train batches (ie: for `IterableDatasets` or fixed frequency)
     trainer = Trainer(val_check_interval=100)
 
 ----------------

docs/source/logging.rst

@@ -66,7 +66,7 @@ Use the :func:`~~pytorch_lightning.core.lightning.LightningModule.log` method to
     def training_step(self, batch, batch_idx):
         self.log('my_metric', x)
 
-Depending on where log is called from, Lightning auto-determines the correct logging mode for you.\
+Depending on where log is called from, Lightning auto-determines the correct logging mode for you. \
 But of course you can override the default behavior by manually setting the :func:`~~pytorch_lightning.core.lightning.LightningModule.log` parameters.
 
 .. code-block:: python
@@ -76,16 +76,16 @@ But of course you can override the default behavior by manually setting the :fun
 
 The :func:`~~pytorch_lightning.core.lightning.LightningModule.log` method has a few options:
 
-* on_step: Logs the metric at the current step. Defaults to True in :func:`~~pytorch_lightning.core.lightning.LightningModule.training_step`, and :func:`~pytorch_lightning.core.lightning.LightningModule.training_step_end`.
+* `on_step`: Logs the metric at the current step. Defaults to `True` in :func:`~~pytorch_lightning.core.lightning.LightningModule.training_step`, and :func:`~pytorch_lightning.core.lightning.LightningModule.training_step_end`.
 
-* on_epoch: Automatically accumulates and logs at the end of the epoch. Defaults to True anywhere in validation or test loops, and in :func:`~~pytorch_lightning.core.lightning.LightningModule.training_epoch_end`.
+* `on_epoch`: Automatically accumulates and logs at the end of the epoch. Defaults to True anywhere in validation or test loops, and in :func:`~~pytorch_lightning.core.lightning.LightningModule.training_epoch_end`.
 
-* prog_bar: Logs to the progress bar.
+* `prog_bar`: Logs to the progress bar.
 
-* logger: Logs to the logger like Tensorboard, or any other custom logger passed to the :class:`~pytorch_lightning.trainer.trainer.Trainer`.
+* `logger`: Logs to the logger like Tensorboard, or any other custom logger passed to the :class:`~pytorch_lightning.trainer.trainer.Trainer`.
 
 
-.. note:: Setting on_epoch=True will accumulate your logged values over the full training epoch.
+.. note:: Setting `on_epoch=True` will accumulate your logged values over the full training epoch.
 
 
 Manual logging