Disable validation when val_percent_check=0 (#1251)

* fix disable validation

* add test

* update changelog

* update docs for val_percent_check

* make "fast training" docs consistent

docs/source/fast_training.rst

@@ -1,10 +1,10 @@
 Fast Training
-================
+=============
 There are multiple options to speed up different parts of the training by choosing to train
 on a subset of data. This could be done for speed or debugging purposes.
 
 Check validation every n epochs
--------------------------------------
+-------------------------------
 If you have a small dataset you might want to check validation every n epochs
 
 .. code-block:: python
@@ -13,7 +13,7 @@ If you have a small dataset you might want to check validation every n epochs
     trainer = Trainer(check_val_every_n_epoch=1)
 
 Force training for min or max epochs
--------------------------------------
+------------------------------------
 It can be useful to force training for a minimum number of epochs or limit to a max number.
 
 .. seealso::
@@ -26,7 +26,7 @@ It can be useful to force training for a minimum number of epochs or limit to a
 
 
 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.
@@ -43,7 +43,7 @@ Must use an int if using an IterableDataset.
     trainer = Trainer(val_check_interval=100)
 
 Use training data subset
-----------------------------------
+------------------------
 If you don't want to check 100% of the training set (for debugging or if it's huge), set this flag.
 
 .. code-block:: python
@@ -54,12 +54,11 @@ If you don't want to check 100% of the training set (for debugging or if it's hu
    # check 10% only
    trainer = Trainer(train_percent_check=0.1)
 
-.. note:: train_percent_check will be overwritten by overfit_pct if overfit_pct > 0
+.. note:: ``train_percent_check`` will be overwritten by ``overfit_pct`` if ``overfit_pct`` > 0.
 
 Use test data subset
--------------------------------------
-If you don't want to check 100% of the test set (for debugging or if it's huge), set this flag
-test_percent_check will be overwritten by overfit_pct if overfit_pct > 0.
+--------------------
+If you don't want to check 100% of the test set (for debugging or if it's huge), set this flag.
 
 .. code-block:: python
 
@@ -69,10 +68,11 @@ test_percent_check will be overwritten by overfit_pct if overfit_pct > 0.
     # check 10% only
     trainer = Trainer(test_percent_check=0.1)
 
+.. note:: ``test_percent_check`` will be overwritten by ``overfit_pct`` if ``overfit_pct`` > 0.
+
 Use validation data subset
---------------------------------------------
-If you don't want to check 100% of the validation set (for debugging or if it's huge), set this flag
-val_percent_check will be overwritten by overfit_pct if overfit_pct > 0
+--------------------------
+If you don't want to check 100% of the validation set (for debugging or if it's huge), set this flag.
 
 .. code-block:: python
 
@@ -80,4 +80,7 @@ val_percent_check will be overwritten by overfit_pct if overfit_pct > 0
     trainer = Trainer(val_percent_check=1.0)
 
     # check 10% only
-    trainer = Trainer(val_percent_check=0.1)
+    trainer = Trainer(val_percent_check=0.1)
+
+.. note:: ``val_percent_check`` will be overwritten by ``overfit_pct`` if ``overfit_pct`` > 0 and ignored if
+    ``fast_dev_run=True``.