diff --git a/egomimic/pl_utils/pl_model.py b/egomimic/pl_utils/pl_model.py
index 487381be..5beaf9a1 100644
--- a/egomimic/pl_utils/pl_model.py
+++ b/egomimic/pl_utils/pl_model.py
@@ -78,19 +78,23 @@ def on_validation_start(self):
         self.model.device = self.device
         
         if self.trainer.is_global_zero:
-            os.makedirs(
-                os.path.join(self.video_dir(), f"epoch_{self.trainer.current_epoch}"),
-                exist_ok=True,
-            )
+            os.makedirs(os.path.join(self.video_dir(), f"epoch_{self.trainer.current_epoch}"),exist_ok=True)
+
 
-    @rank_zero_only
     def validation_step(self, batch, batch_idx, dataloader_idx=0):
         """
         Run a validation step on the batch, and save that batch of images into the val_image_buffer.  Once the buffer hits 1000 images, save that as a 30fps video using torchvision.io.write_video.
         """
+        print(f"[VAL_STEP] rank={self.global_rank}, batch_idx={batch_idx}",flush=True)
+
         batch = self.model.process_batch_for_training(batch)
         metrics, images_dict = self.model.forward_eval_logging(batch)
 
+        metrics = {
+            k: (v.to(self.device) if torch.is_tensor(v) else torch.tensor(v, device=self.device))
+            for k, v in metrics.items()
+        }
+
         ## images is now a dict
         for key, images in images_dict.items():
             os.makedirs(
@@ -117,10 +121,10 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0):
                 self.val_image_buffer[key].clear()
                 self.val_counter[key] += 1
 
-        self.log_dict(metrics)
+        self.log_dict(metrics, sync_dist=True)
 
-    @rank_zero_only
     def on_validation_end(self):
+        print(f"[ON_VALIDATION_END] rank={self.global_rank}",flush=True)
         for key, buffer in self.val_image_buffer.items():
             os.makedirs(
                 os.path.join(