diff --git a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/run_fwfm.py b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/run_fwfm.py
index fb36440348c5e76572eb4ddd02993b9abcc36da5..f97c37546b780d650120483ff27cf5317e8d6378 100644
--- a/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/run_fwfm.py
+++ b/TensorFlow/built-in/recommendation/DeepCTR_Series_for_TensorFlow/examples/run_fwfm.py
@@ -61,16 +61,16 @@ def main():
     custom_op.name = "NpuOptimizer"
     custom_op.parameter_map["precision_mode"].s = tf.compat.as_bytes(args.precision_mode)
 
-    if args.profiling:
-        custom_op.parameter_map["profiling_mode"].b = True
-        custom_op.parameter_map["profiling_options"].s = tf.compat.as_bytes(
-            '{"output":"' + args.profiling_dump_path + '", \
-                          "training_trace":"on", \
-                          "task_trace":"on", \
-                          "aicpu":"on", \
-                          "aic_metrics":"PipeUtilization",\
-                          "fp_point":"concatenate_1/concat", \
-                          "bp_point":"training/Adam/gradients/gradients/AddN_83"}')
+    #if args.profiling:
+    #    custom_op.parameter_map["profiling_mode"].b = True
+    #    custom_op.parameter_map["profiling_options"].s = tf.compat.as_bytes(
+    #        '{"output":"' + args.profiling_dump_path + '", \
+    #                      "training_trace":"on", \
+    #                      "task_trace":"on", \
+    #                      "aicpu":"on", \
+    #                      "aic_metrics":"PipeUtilization",\
+    #                      "fp_point":"concatenate_1/concat", \
+    #                      "bp_point":"training/Adam/gradients/gradients/AddN_83"}')
 
     npu_keras_sess = set_keras_session_npu_config(config=sess_config)
     data = pd.read_csv(os.path.join(args.data_dir, 'criteo_sample.txt'))