diff --git a/CHANGELOG.md b/CHANGELOG.md
index 40d57aab3d..5bdaf6c95f 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -141,6 +141,10 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Changed
 
+
+- Changed `EarlyStopping` callback from by default running `EarlyStopping.on_validation_end` if only training is run. Set `check_on_train_epoch_end` to run the callback at the end of the train epoch instead of at the end of the validation epoch ([#7069](https://github.com/PyTorchLightning/pytorch-lightning/pull/7069))
+
+
 - Renamed `pytorch_lightning.callbacks.swa` to `pytorch_lightning.callbacks.stochastic_weight_avg` ([#6259](https://github.com/PyTorchLightning/pytorch-lightning/pull/6259))
 
 
@@ -224,6 +228,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 ### Removed
 
 
+- Removed training loop explicitly calling `EarlyStopping.on_validation_end` if no validation is run ([#7069](https://github.com/PyTorchLightning/pytorch-lightning/pull/7069))
+
+
 - Removed `automatic_optimization` as a property from the training loop in favor of `LightningModule.automatic_optimization` ([#7130](https://github.com/PyTorchLightning/pytorch-lightning/pull/7130))
 
 
diff --git a/pytorch_lightning/trainer/training_loop.py b/pytorch_lightning/trainer/training_loop.py
index 9284c75879..8a007086fb 100644
--- a/pytorch_lightning/trainer/training_loop.py
+++ b/pytorch_lightning/trainer/training_loop.py
@@ -19,7 +19,6 @@ from typing import Dict, List, Optional, Union
 import numpy as np
 import torch
 
-from pytorch_lightning.callbacks import EarlyStopping
 from pytorch_lightning.core.optimizer import LightningOptimizer
 from pytorch_lightning.core.step_result import Result
 from pytorch_lightning.plugins import ParallelPlugin
@@ -148,15 +147,6 @@ class TrainLoop:
             for cb in callbacks:
                 cb.on_validation_end(self.trainer, model)
 
-    def check_early_stopping_callback(self, should_update):
-        # TODO bake this logic into the EarlyStopping callback
-        if should_update and self.trainer.checkpoint_connector.has_trained:
-            callbacks = [c for c in self.trainer.callbacks if isinstance(c, EarlyStopping)]
-            model = self.trainer.lightning_module
-
-            for cb in callbacks:
-                cb.on_validation_end(self.trainer, model)
-
     def on_train_epoch_start(self, epoch):
 
         # update training progress in trainer
@@ -556,7 +546,6 @@ class TrainLoop:
 
         if should_train_only:
             self.check_checkpoint_callback(True)
-            self.check_early_stopping_callback(True)
 
         if should_check_val:
             self.trainer.validating = True
diff --git a/tests/callbacks/test_early_stopping.py b/tests/callbacks/test_early_stopping.py
index d330955580..b4a2424f92 100644
--- a/tests/callbacks/test_early_stopping.py
+++ b/tests/callbacks/test_early_stopping.py
@@ -169,7 +169,9 @@ def test_early_stopping_patience_train(
     if validation_step_none:
         model.validation_step = None
 
-    early_stop_callback = EarlyStopping(monitor="train_loss", patience=patience, verbose=True)
+    early_stop_callback = EarlyStopping(
+        monitor="train_loss", patience=patience, verbose=True, check_on_train_epoch_end=validation_step_none
+    )
     trainer = Trainer(
         default_root_dir=tmpdir,
         callbacks=[early_stop_callback],
@@ -200,7 +202,7 @@ def test_early_stopping_no_val_step(tmpdir):
     model.validation_step = None
     model.val_dataloader = None
 
-    stopping = EarlyStopping(monitor='train_loss', min_delta=0.1, patience=0)
+    stopping = EarlyStopping(monitor='train_loss', min_delta=0.1, patience=0, check_on_train_epoch_end=True)
     trainer = Trainer(
         default_root_dir=tmpdir,
         callbacks=[stopping],
diff --git a/tests/trainer/test_trainer.py b/tests/trainer/test_trainer.py
index 508c7243c8..7997d1efdf 100644
--- a/tests/trainer/test_trainer.py
+++ b/tests/trainer/test_trainer.py
@@ -548,7 +548,7 @@ def test_trainer_min_steps_and_min_epochs_not_reached(tmpdir, caplog):
             return output
 
     model = TestModel()
-    early_stop = EarlyStopping(monitor="loss", patience=0)
+    early_stop = EarlyStopping(monitor="loss", patience=0, check_on_train_epoch_end=True)
     min_epochs = 5
     trainer = Trainer(
         default_root_dir=tmpdir,