diff --git a/pytorch_lightning/trainer/training_loop.py b/pytorch_lightning/trainer/training_loop.py
index 484983d7b62be..df9101910dc8e 100644
--- a/pytorch_lightning/trainer/training_loop.py
+++ b/pytorch_lightning/trainer/training_loop.py
@@ -353,12 +353,9 @@ def train(self):
                 # stop training
                 stop = should_stop and met_min_epochs
                 if stop:
-                    self.main_progress_bar.close()
-                    model.on_train_end()
-                    return
+                    break
 
         self.main_progress_bar.close()
-
         model.on_train_end()
 
         if self.logger is not None:
diff --git a/tests/test_cpu_models.py b/tests/test_cpu_models.py
index fa643c64ac5fc..2f39dbd25e124 100644
--- a/tests/test_cpu_models.py
+++ b/tests/test_cpu_models.py
@@ -21,6 +21,7 @@ def test_early_stopping_cpu_model(tmpdir):
     stopping = EarlyStopping(monitor='val_loss', min_delta=0.1)
     trainer_options = dict(
         default_save_path=tmpdir,
+        min_epochs=2,
         early_stop_callback=stopping,
         gradient_clip_val=1.0,
         overfit_pct=0.20,
@@ -33,7 +34,7 @@ def test_early_stopping_cpu_model(tmpdir):
     )
 
     model, hparams = tutils.get_model()
-    tutils.run_model_test(trainer_options, model, on_gpu=False)
+    tutils.run_model_test(trainer_options, model, on_gpu=False, early_stop=True)
 
     # test freeze on cpu
     model.freeze()
diff --git a/tests/utils.py b/tests/utils.py
index 70ecccb28d7a0..33da9b6228939 100644
--- a/tests/utils.py
+++ b/tests/utils.py
@@ -45,7 +45,7 @@ def run_model_test_no_loggers(trainer_options, model, min_acc=0.50):
         trainer.optimizers, trainer.lr_schedulers = pretrained_model.configure_optimizers()
 
 
-def run_model_test(trainer_options, model, on_gpu=True):
+def run_model_test(trainer_options, model, on_gpu=True, early_stop=False):
     save_dir = trainer_options['default_save_path']
 
     # logger file to get meta
@@ -65,6 +65,10 @@ def run_model_test(trainer_options, model, on_gpu=True):
     # correct result and ok accuracy
     assert result == 1, 'amp + ddp model failed to complete'
 
+    if early_stop:
+        assert trainer.current_epoch >= trainer.min_epochs, 'amp + ddp model failed to complete'
+        assert trainer.current_epoch < trainer.max_epochs-1, 'amp + ddp model failed to stop early'
+
     # test model loading
     pretrained_model = load_model(logger, trainer.checkpoint_callback.filepath)