diff --git a/codes/models/ExtensibleTrainer.py b/codes/models/ExtensibleTrainer.py
index 395f88bc..9d4339f8 100644
--- a/codes/models/ExtensibleTrainer.py
+++ b/codes/models/ExtensibleTrainer.py
@@ -226,7 +226,7 @@ class ExtensibleTrainer(BaseModel):
             [e.after_optimize(state) for e in self.experiments]
 
         # Record visual outputs for usage in debugging and testing.
-        if 'visuals' in self.opt['logger'].keys():
+        if 'visuals' in self.opt['logger'].keys() and self.rank <= 0:
             sample_save_path = os.path.join(self.opt['path']['models'], "..", "visual_dbg")
             for v in self.opt['logger']['visuals']:
                 if v not in state.keys():
diff --git a/codes/models/base_model.py b/codes/models/base_model.py
index 37af08cf..0b092520 100644
--- a/codes/models/base_model.py
+++ b/codes/models/base_model.py
@@ -10,6 +10,10 @@ from apex import amp
 class BaseModel():
     def __init__(self, opt):
         self.opt = opt
+        if opt['dist']:
+            self.rank = torch.distributed.get_rank()
+        else:
+            self.rank = -1  # non dist training
         self.device = torch.device('cuda' if opt['gpu_ids'] is not None else 'cpu')
         self.amp_level = 'O0' if opt['amp_opt_level'] is None else opt['amp_opt_level']
         self.is_train = opt['is_train']