drop duplicated guides (#864)

* drop duplicated guides

* prevent copy to git

docs/source/debugging.rst

@@ -1,9 +1,9 @@
 Debugging
-==========
+=========
 The following are flags that make debugging much easier.
 
 Fast dev run
--------------------
+------------
 This flag runs a "unit test" by running 1 training batch and 1 validation batch.
 The point is to detect any bugs in the training/validation loop without having to wait for
 a full epoch to crash.
@@ -13,7 +13,7 @@ a full epoch to crash.
     trainer = pl.Trainer(fast_dev_run=True)
 
 Inspect gradient norms
------------------------------------
+----------------------
 Logs (to a logger), the norm of each weight matrix.
 
 .. code-block:: python
@@ -22,7 +22,7 @@ Logs (to a logger), the norm of each weight matrix.
     trainer = pl.Trainer(track_grad_norm=2)
 
 Log GPU usage
------------------------------------
+-------------
 Logs (to a logger) the GPU usage for each GPU on the master machine.
 
 (See: :ref:`trainer`)
@@ -32,7 +32,7 @@ Logs (to a logger) the GPU usage for each GPU on the master machine.
     trainer = pl.Trainer(log_gpu_memory=True)
 
 Make model overfit on subset of data
------------------------------------
+------------------------------------
 
 A good debugging technique is to take a tiny portion of your data (say 2 samples per class),
 and try to get your model to overfit. If it can't, it's a sign it won't work with large datasets.
@@ -44,7 +44,7 @@ and try to get your model to overfit. If it can't, it's a sign it won't work wit
     trainer = pl.Trainer(overfit_pct=0.01)
 
 Print the parameter count by layer
------------------------------------
+----------------------------------
 Whenever the .fit() function gets called, the Trainer will print the weights summary for the lightningModule.
 To disable this behavior, turn off this flag:
 
@@ -55,7 +55,7 @@ To disable this behavior, turn off this flag:
     trainer = pl.Trainer(weights_summary=None)
 
 Print which gradients are nan
-------------------------------
+-----------------------------
 Prints the tensors with nan gradients.
 
 (See: :meth:`trainer.print_nan_grads`)
@@ -65,7 +65,7 @@ Prints the tensors with nan gradients.
     trainer = pl.Trainer(print_nan_grads=False)
 
 Set the number of validation sanity steps
--------------------------------------
+-----------------------------------------
 Lightning runs a few steps of validation in the beginning of training.
 This avoids crashing in the validation loop sometime deep into a lengthy training loop.