diff --git a/models/rank/dnn/config.yaml b/models/rank/dnn/config.yaml
index 9db1f9bb88c2f12237589cf4002c2ab8c2b96e50..29080797f09ed2377dba26427c7809dc28d0b0b9 100755
--- a/models/rank/dnn/config.yaml
+++ b/models/rank/dnn/config.yaml
@@ -61,3 +61,8 @@ executor:
    dataset_name: dataset_2 # 名字，用来区分不同的阶段
    thread_num: 1 # 线程数
    is_infer: False # 是否是infer
+# - name: infer
+#   model: "{workspace}/model.py" # 模型路径
+#   dataset_name: dataset_2 # 名字，用来区分不同的阶段
+#   thread_num: 1 # 线程数
+#   is_infer: True # 是否是infer
diff --git a/models/rank/dnn/model.py b/models/rank/dnn/model.py
index 0f2681648a165980fee7311882630255daab7f91..d417d4d9fb2deddd15118d8b8f544ce895ddbf09 100755
--- a/models/rank/dnn/model.py
+++ b/models/rank/dnn/model.py
@@ -77,17 +77,21 @@ class Model(ModelBase):
 
         self.predict = predict
 
-        cost = fluid.layers.cross_entropy(
-            input=self.predict, label=self.label_input)
-        avg_cost = fluid.layers.reduce_mean(cost)
-        self._cost = avg_cost
-
         auc, batch_auc, _ = fluid.layers.auc(input=self.predict,
                                              label=self.label_input,
                                              num_thresholds=2**12,
                                              slide_steps=20)
+        if is_infer:
+            self._infer_results["AUC"] = auc
+            self._infer_results["BATCH_AUC"] = batch_auc
+            return
+
         self._metrics["AUC"] = auc
         self._metrics["BATCH_AUC"] = batch_auc
+        cost = fluid.layers.cross_entropy(
+            input=self.predict, label=self.label_input)
+        avg_cost = fluid.layers.reduce_mean(cost)
+        self._cost = avg_cost
 
     def optimizer(self):
         optimizer = fluid.optimizer.Adam(self.learning_rate, lazy_mode=True)