diff --git a/pytorch_lightning/core/hooks.py b/pytorch_lightning/core/hooks.py
index c2ea544dd0..7056da598f 100644
--- a/pytorch_lightning/core/hooks.py
+++ b/pytorch_lightning/core/hooks.py
@@ -250,18 +250,6 @@ class ModelHooks(Module):
         """
         # do something when the epoch ends
 
-    def on_pre_performance_check(self) -> None:
-        """
-        Called at the very beginning of the validation loop.
-        """
-        # do something before validation starts
-
-    def on_post_performance_check(self) -> None:
-        """
-        Called at the very end of the validation loop.
-        """
-        # do something before validation end
-
     def on_before_zero_grad(self, optimizer: Optimizer) -> None:
         """
         Called after optimizer.step() and before optimizer.zero_grad().
diff --git a/pytorch_lightning/trainer/evaluation_loop.py b/pytorch_lightning/trainer/evaluation_loop.py
index 650d55f9a4..6d18222e71 100644
--- a/pytorch_lightning/trainer/evaluation_loop.py
+++ b/pytorch_lightning/trainer/evaluation_loop.py
@@ -312,9 +312,6 @@ class TrainerEvaluationLoopMixin(ABC):
         # enable eval mode + no grads
         model = self.get_model()
 
-        # TODO: deprecate
-        model.on_pre_performance_check()
-
         # select dataloaders
         dataloaders, max_batches = self.evaluation_loop.get_evaluation_dataloaders()
 
@@ -389,9 +386,6 @@ class TrainerEvaluationLoopMixin(ABC):
         # log the final eval loop metrics
         eval_loop_results = self.__log_evaluation_epoch_metrics(eval_results, test_mode)
 
-        # hook
-        model.on_post_performance_check()
-
         # user may want to reload every epoch
         if self.reload_dataloaders_every_epoch:
             self.evaluation_loop.reload_evaluation_dataloaders()