diff --git a/pytorch_lightning/trainer/trainer.py b/pytorch_lightning/trainer/trainer.py
index 0f76c07229..35027cf3c1 100644
--- a/pytorch_lightning/trainer/trainer.py
+++ b/pytorch_lightning/trainer/trainer.py
@@ -851,16 +851,18 @@ class Trainer(
         if self.is_function_implemented('on_fit_start'):
             model.on_fit_start()
 
-        self.setup('fit')
-        if self.is_function_implemented('setup'):
-            model.setup('fit')
-
         # on multi-gpu jobs we only want to manipulate (download, etc) on node_rank=0, local_rank=0
         # or in the case where each node needs to do its own manipulation in which case just local_rank=0
         if self.can_prepare_data():
             model.prepare_data()
             self._is_data_prepared = True
 
+        self.barrier('fit_prepare_data')
+
+        self.setup('fit')
+        if self.is_function_implemented('setup'):
+            model.setup('fit')
+
         # Run auto batch size scaling
         if self.auto_scale_batch_size:
             if isinstance(self.auto_scale_batch_size, bool):
@@ -1150,6 +1152,8 @@ class Trainer(
             model_ref = self.model if model is None else model
             model_ref.setup('test')
 
+        self.barrier('test_setup')
+
         if model is None and ckpt_path == 'best' and self.checkpoint_callback.save_top_k <= 0:
             raise MisconfigurationException(
                 'ckpt_path is "best", but ModelCheckpoint is not configured to save the best model.')
@@ -1251,6 +1255,14 @@ class Trainer(
                 raise MisconfigurationException('You have defined `test_step()` but did not'
                                                 ' implement `test_dataloader` nor passed in `.test(test_dataloader)`.')
 
+    def barrier(self, name):
+        if self.use_ddp or self.use_ddp2:
+            torch_distrib.barrier()
+
+        if self.on_tpu and XLA_AVAILABLE:
+            # wait for all processes to catch up
+            torch_xla.core.xla_model.rendezvous(f'pl.Trainer.{name}')
+
 
 class _PatchDataLoader(object):
     r"""