try predict

src/main_portrait.py

 import datetime
+import time
+
+import tensorflow as tf
 
 from models.esmm.fe import (get_device_dict_from_redis, get_diary_dict_from_redis)
 from models.esmm.model import model_predict_diary
@@ -29,12 +32,24 @@ def user_portrait_scan_info():
         print(e)
 
 
-if __name__ == "__main__":
+def main():
+    time_begin = time.time()
+    tf.logging.set_verbosity(tf.compat.v1.logging.INFO)
+
     device_dict = get_device_dict_from_redis()
     diary_dict = get_diary_dict_from_redis()
 
     device_id = "androidid_a25a1129c0b38f7b"
-    model_path = "/home/gmuser/data/models/1595501144"
+    save_path = "/home/gmuser/data/models/1595501144"
+    predict_fn = tf.contrib.predictor.from_saved_model(save_path)
+
     diary_ids = user_portrait_tag3_get_candidate_unread_list(device_id, "diary")
-    model_predict_diary(device_id, diary_ids)
+    model_predict_diary(device_id, diary_ids, device_dict, diary_dict, predict_fn)
     # user_portrait_scan_info()
+
+    total_time = (time.time() - time_begin) / 60
+    print("total cost {:.2f} mins at {}".format(total_time, datetime.now()))
+
+
+if __name__ == "__main__":
+    main()