update docs

Author: rohitgr7

docs/source/common/debugging.rst

@@ -79,10 +79,10 @@ argument of :class:`~pytorch_lightning.trainer.trainer.Trainer`)
 
 .. testcode::
 
-    # use only 1% of training data (and use the same training dataloader (with shuffle off) in val and test)
+    # use only 1% of training data (and turn-off validation)
     trainer = Trainer(overfit_batches=0.01)
 
-    # similar, but with a fixed 10 batches no matter the size of the dataset
+    # similar, but with a fixed 10 batches
     trainer = Trainer(overfit_batches=10)
 
 With this flag, the train, val, and test sets will all be the same train set. We will also replace the sampler

docs/source/common/trainer.rst

@@ -1074,7 +1074,7 @@ overfit_batches
 
 |
 
-Uses this much data of the training set. If nonzero, will use the same training set for validation and testing.
+Uses this much data of the training set. If nonzero, will turn-off validation.
 If the training dataloaders have `shuffle=True`, Lightning will automatically disable it.
 
 Useful for quickly debugging or trying to overfit on purpose.
@@ -1084,7 +1084,7 @@ Useful for quickly debugging or trying to overfit on purpose.
     # default used by the Trainer
     trainer = Trainer(overfit_batches=0.0)
 
-    # use only 1% of the train set (and use the train set for val and test)
+    # use only 1% of the train set
     trainer = Trainer(overfit_batches=0.01)
 
     # overfit on 10 of the same batches

docs/source/guides/speed.rst

@@ -336,7 +336,7 @@ If you don't want to check 100% of the training/validation/test set set these fl
 
 If you also pass ``shuffle=True`` to the dataloader, a different random subset of your dataset will be used for each epoch; otherwise the same subset will be used for all epochs.
 
-.. note:: ``limit_train_batches``, ``limit_val_batches`` and ``limit_test_batches`` will be overwritten by ``overfit_batches`` if ``overfit_batches`` > 0. ``limit_val_batches`` will be ignored if ``fast_dev_run=True``.
+.. note:: ``limit_train_batches`` will be overwritten by ``overfit_batches`` if ``overfit_batches > 0`` and will turn-off validation.
 
 .. note:: If you set ``limit_val_batches=0``, validation will be disabled.