diff --git a/modules/deeplearning/icing_fcn.py b/modules/deeplearning/icing_fcn.py
index d5607669dac0e3dde1aca914431d34583834aeea..b89faa9284089da27cf71eee1af21d77bf6da80b 100644
--- a/modules/deeplearning/icing_fcn.py
+++ b/modules/deeplearning/icing_fcn.py
@@ -1188,6 +1188,7 @@ def run_average_models(ckpt_dir_s_path, day_night='NIGHT', l1b_andor_l2='BOTH',
         model = load_model(ckpt_dir, day_night=day_night, l1b_andor_l2=l1b_andor_l2, use_flight_altitude=use_flight_altitude)
         k_model = model.model
         model_weight_s.append(k_model.get_weights())
+    print('done loading models ******************************************')
 
     avg_model_weights = []
     for m in model_weight_s:
@@ -1204,13 +1205,13 @@ def run_average_models(ckpt_dir_s_path, day_night='NIGHT', l1b_andor_l2='BOTH',
     new_model.build_evaluation()
 
     # -- Save the averaged weights to a new the model
-    if not os.path.exists(modeldir):
-        os.mkdir(modeldir)
-    ckpt = tf.train.Checkpoint(step=tf.Variable(1), model=new_model.model)
-    ckpt_manager = tf.train.CheckpointManager(ckpt, modeldir, max_to_keep=3)
-
-    new_model.model.set_weights(avg_weights)
-    ckpt_manager.save()
+    # if not os.path.exists(modeldir):
+    #     os.mkdir(modeldir)
+    # ckpt = tf.train.Checkpoint(step=tf.Variable(1), model=new_model.model)
+    # ckpt_manager = tf.train.CheckpointManager(ckpt, modeldir, max_to_keep=3)
+    #
+    # new_model.model.set_weights(avg_weights)
+    # ckpt_manager.save()
 
     return