diff --git a/pytorch_lightning/trainer/evaluation_loop.py b/pytorch_lightning/trainer/evaluation_loop.py
index bca62836..a62d318a 100644
--- a/pytorch_lightning/trainer/evaluation_loop.py
+++ b/pytorch_lightning/trainer/evaluation_loop.py
@@ -257,7 +257,6 @@ class TrainerEvaluationLoopMixin(ABC):
                 dataloader = dataloader.per_device_loader(device)
 
             for batch_idx, batch in enumerate(dataloader):
-
                 if batch is None:  # pragma: no cover
                     continue
 
diff --git a/pytorch_lightning/trainer/training_loop.py b/pytorch_lightning/trainer/training_loop.py
index d2be9894..ef056f88 100644
--- a/pytorch_lightning/trainer/training_loop.py
+++ b/pytorch_lightning/trainer/training_loop.py
@@ -456,15 +456,18 @@ class TrainerTrainLoopMixin(ABC):
         if self.reload_dataloaders_every_epoch:
             self.reset_train_dataloader(self.get_model())
 
+        # track local dataloader so TPU can wrap each epoch
+        train_dataloader = self.train_dataloader
+
         # on TPU we have to wrap it under the ParallelLoader
         if self.use_tpu:
             device = xm.xla_device()
-            self.train_dataloader = xla_pl.ParallelLoader(self.train_dataloader, [device])
-            self.train_dataloader = self.train_dataloader.per_device_loader(device)
+            train_dataloader = xla_pl.ParallelLoader(train_dataloader, [device])
+            train_dataloader = train_dataloader.per_device_loader(device)
 
         # run epoch
         for batch_idx, batch in self.profiler.profile_iterable(
-            enumerate(self.train_dataloader), "get_train_batch"
+            enumerate(train_dataloader), "get_train_batch"
         ):
             # stop epoch if we limited the number of training batches
             if batch_idx >= self.num_training_batches:
diff --git a/tests/models/utils.py b/tests/models/utils.py
index 75be02ef..30a11a7a 100644
--- a/tests/models/utils.py
+++ b/tests/models/utils.py
@@ -188,7 +188,7 @@ def run_prediction(dataloader, trained_model, dp=False, min_acc=0.50):
         acc = torch.tensor(acc)
         acc = acc.item()
 
-    assert acc > min_acc, f'this model is expected to get > {min_acc} in test set (it got {acc})'
+    assert acc >= min_acc, f'this model is expected to get > {min_acc} in test set (it got {acc})'
 
 
 def assert_ok_model_acc(trainer, key='test_acc', thr=0.4):