enhance docs for val_check_interval

Author: rohitgr7

docs/source/common/trainer.rst

@@ -1532,7 +1532,8 @@ How often within one training epoch to check the validation set.
 Can specify as float or int.
 
 - pass a ``float`` in the range [0.0, 1.0] to check after a fraction of the training epoch.
-- pass an ``int`` to check after a fixed number of training batches.
+- pass an ``int`` to check after a fixed number of training batches. An ``int`` value can only be higher than the number of training
+  batches when ``check_val_every_n_epoch=None``, which validates after every ``N`` training batches across complete training.
 
 .. testcode::
 
@@ -1542,10 +1543,13 @@ Can specify as float or int.
     # check validation set 4 times during a training epoch
     trainer = Trainer(val_check_interval=0.25)
 
-    # check validation set every 1000 training batches
+    # check validation set every 1000 training batches in the current epoch
+    trainer = Trainer(val_check_interval=1000)
+
+    # check validation set every 1000 training batches across complete training
     # use this when using iterableDataset and your dataset has no length
     # (ie: production cases with streaming data)
-    trainer = Trainer(val_check_interval=1000)
+    trainer = Trainer(val_check_interval=1000, check_val_every_n_epoch=None)
 
 
 .. code-block:: python

pytorch_lightning/trainer/trainer.py

@@ -405,7 +405,8 @@ def __init__(
             val_check_interval: How often to check the validation set. Pass a ``float`` in the range [0.0, 1.0] to check
                 after a fraction of the training epoch. Pass an ``int`` to check after a fixed number of training
                 batches. An ``int`` value can only be higher than the number of training batches when
-                ``check_val_every_n_epoch=None``.
+                ``check_val_every_n_epoch=None``, which validates after every ``N`` training batches across complete
+                training.
                 Default: ``1.0``.
 
             enable_model_summary: Whether to enable model summarization by default.