diff --git a/examples/impala/impala_multi_node_submitit.py b/examples/impala/impala_multi_node_submitit.py
index c354dd22364..118913699f9 100644
--- a/examples/impala/impala_multi_node_submitit.py
+++ b/examples/impala/impala_multi_node_submitit.py
@@ -128,10 +128,9 @@ def main(cfg: "DictConfig"):  # noqa: F821
     # Main loop
     collected_frames = 0
     num_network_updates = 0
-    start_time = time.time()
     pbar = tqdm.tqdm(total=total_frames)
     accumulator = []
-    sampling_start = time.time()
+    start_time = sampling_start = time.time()
     for i, data in enumerate(collector):
 
         log_info = {}
@@ -164,8 +163,8 @@ def main(cfg: "DictConfig"):  # noqa: F821
         for j in range(sgd_updates):
 
             # Create a single batch of trajectories
-            stacked_data = torch.stack(accumulator, dim=0)
-            stacked_data = stacked_data.to(device)
+            stacked_data = torch.stack(accumulator, dim=0).contiguous()
+            stacked_data = stacked_data.to(device, non_blocking=True)
 
             # Compute advantage
             stacked_data = adv_module(stacked_data)
diff --git a/examples/impala/impala_single_node.py b/examples/impala/impala_single_node.py
index 47cf31cc6df..2cd1043f46f 100644
--- a/examples/impala/impala_single_node.py
+++ b/examples/impala/impala_single_node.py
@@ -47,8 +47,8 @@ def main(cfg: "DictConfig"):  # noqa: F821
     max_grad_norm = cfg.optim.max_grad_norm
     num_test_episodes = cfg.logger.num_test_episodes
     total_network_updates = (
-                                    total_frames // (frames_per_batch * batch_size)
-                            ) * cfg.loss.sgd_updates
+        total_frames // (frames_per_batch * batch_size)
+    ) * cfg.loss.sgd_updates
 
     # Create models (check utils_atari.py)
     actor, critic = make_ppo_models(cfg.env.env_name)
@@ -116,10 +116,9 @@ def main(cfg: "DictConfig"):  # noqa: F821
     # Main loop
     collected_frames = 0
     num_network_updates = 0
-    start_time = time.time()
     pbar = tqdm.tqdm(total=total_frames)
     accumulator = []
-    sampling_start = time.time()
+    start_time = sampling_start = time.time()
     for i, data in enumerate(collector):
 
         log_info = {}
@@ -136,7 +135,7 @@ def main(cfg: "DictConfig"):  # noqa: F821
                 {
                     "train/reward": episode_rewards.mean().item(),
                     "train/episode_length": episode_length.sum().item()
-                                            / len(episode_length),
+                    / len(episode_length),
                 }
             )
 
@@ -183,7 +182,7 @@ def main(cfg: "DictConfig"):  # noqa: F821
                     "loss_critic", "loss_entropy", "loss_objective"
                 ).detach()
                 loss_sum = (
-                        loss["loss_critic"] + loss["loss_objective"] + loss["loss_entropy"]
+                    loss["loss_critic"] + loss["loss_objective"] + loss["loss_entropy"]
                 )
 
                 # Backward pass
@@ -212,7 +211,7 @@ def main(cfg: "DictConfig"):  # noqa: F821
         # Get test rewards
         with torch.no_grad(), set_exploration_type(ExplorationType.MODE):
             if ((i - 1) * frames_in_batch * frame_skip) // test_interval < (
-                    i * frames_in_batch * frame_skip
+                i * frames_in_batch * frame_skip
             ) // test_interval:
                 actor.eval()
                 eval_start = time.time()