Add on_exception hook to documentation

docs/source/common/lightning_module.rst

@@ -80,7 +80,7 @@ Notice a few things.
         out = net(x)
 
 Thus, to use Lightning, you just need to organize your code which takes about 30 minutes,
-(and let's be real, you probably should do anyhow).
+(and let's be real, you probably should do anyway).
 
 ------------
 
@@ -267,8 +267,8 @@ The matching pseudocode is:
 
 Training with DataParallel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~
-When training using a `accelerator` that splits data from each batch across GPUs, sometimes you might
-need to aggregate them on the master GPU for processing (dp, or ddp2).
+When training using an `accelerator` that splits data from each batch across GPUs, sometimes you might
+need to aggregate them on the main GPU for processing (dp, or ddp2).
 
 In this case, implement the `training_step_end` method
 
@@ -379,8 +379,8 @@ If you need to do something with all the outputs of each `validation_step`, over
 
 Validating with DataParallel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-When training using a `accelerator` that splits data from each batch across GPUs, sometimes you might
-need to aggregate them on the master GPU for processing (dp, or ddp2).
+When training using an `accelerator` that splits data from each batch across GPUs, sometimes you might
+need to aggregate them on the main GPU for processing (dp, or ddp2).
 
 In this case, implement the `validation_step_end` method
 

docs/source/extensions/callbacks.rst

@@ -395,6 +395,12 @@ on_keyboard_interrupt
 .. automethod:: pytorch_lightning.callbacks.Callback.on_keyboard_interrupt
     :noindex:
 
+on_exception
+^^^^^^^^^^^^
+
+.. automethod:: pytorch_lightning.callbacks.Callback.on_exception
+    :noindex:
+
 on_save_checkpoint
 ^^^^^^^^^^^^^^^^^^