diff --git a/PyTorch/contrib/cv/pose_estimation/VoxelPose/run/train_3d.py b/PyTorch/contrib/cv/pose_estimation/VoxelPose/run/train_3d.py
index 44ef94dd71d4677b8e69810011f109dd02b66b93..fbfd3a77553c2eb3f98756cfb715ef0d82cdc9a0 100644
--- a/PyTorch/contrib/cv/pose_estimation/VoxelPose/run/train_3d.py
+++ b/PyTorch/contrib/cv/pose_estimation/VoxelPose/run/train_3d.py
@@ -251,22 +251,23 @@ def main():
 
         # lr_scheduler.step()
         train_3d(config, model, optimizer, train_loader, epoch, final_output_dir, writer_dict, len(gpus), device=device, is_master_node=args.is_master_node, use_apex=args.apex)
-        precision = validate_3d(config, model, test_loader, final_output_dir, device=device, is_master_node=args.is_master_node)
+        if args.distributed:
+            precision = validate_3d(config, model, test_loader, final_output_dir, device=device, is_master_node=args.is_master_node)
 
-        if precision > best_precision:
-            best_precision = precision
-            best_model = True
-        else:
-            best_model = False
-        if args.is_master_node:
-            logger.info('=> saving checkpoint to {} (Best: {})'.format(final_output_dir, best_model))
-        model_copy=copy.deepcopy(model).cpu()
-        save_checkpoint({
-            'epoch': epoch + 1,
-            'state_dict': model_copy.module.state_dict(),
-            'precision': best_precision,
-            'optimizer': optimizer.state_dict(),
-        }, best_model, final_output_dir)
+            if precision > best_precision:
+                best_precision = precision
+                best_model = True
+            else:
+                best_model = False
+            if args.is_master_node:
+                logger.info('=> saving checkpoint to {} (Best: {})'.format(final_output_dir, best_model))
+            model_copy=copy.deepcopy(model).cpu()
+            save_checkpoint({
+                'epoch': epoch + 1,
+                'state_dict': model_copy.module.state_dict(),
+                'precision': best_precision,
+                'optimizer': optimizer.state_dict(),
+            }, best_model, final_output_dir)
 
     final_model_state_file = os.path.join(final_output_dir,
                                           'final_state.pth.tar')