diff --git a/python/pipeline/pipeline_client.py b/python/pipeline/pipeline_client.py
index 1df3e76130820b6989941540109ec4ab8c4b49ad..cf5e2e7758e458d55100ace47039dc17bb07677a 100644
--- a/python/pipeline/pipeline_client.py
+++ b/python/pipeline/pipeline_client.py
@@ -16,6 +16,7 @@ import grpc
 import numpy as np
 from numpy import *
 import logging
+import functools
 from .proto import pipeline_service_pb2
 from .proto import pipeline_service_pb2_grpc
 
@@ -31,7 +32,7 @@ class PipelineClient(object):
         self._stub = pipeline_service_pb2_grpc.PipelineServiceStub(
             self._channel)
 
-    def _pack_data_for_infer(self, feed_dict):
+    def _pack_request_package(self, feed_dict):
         req = pipeline_service_pb2.Request()
         for key, value in feed_dict.items():
             if not isinstance(value, str):
@@ -40,15 +41,7 @@ class PipelineClient(object):
             req.value.append(value)
         return req
 
-    def predict(self, feed_dict, fetch):
-        if not isinstance(feed_dict, dict):
-            raise TypeError(
-                "feed must be dict type with format: {name: value}.")
-        if not isinstance(fetch, list):
-            raise TypeError(
-                "fetch_with_type must be list type with format: [name].")
-        req = self._pack_data_for_infer(feed_dict)
-        resp = self._stub.inference(req)
+    def _unpack_response_package(self, resp, fetch):
         if resp.ecode != 0:
             return {"ecode": resp.ecode, "error_info": resp.error_info}
         fetch_map = {"ecode": resp.ecode}
@@ -62,3 +55,30 @@ class PipelineClient(object):
                 pass
             fetch_map[key] = data
         return fetch_map
+
+    def predict(self, feed_dict, fetch, asyn=False):
+        if not isinstance(feed_dict, dict):
+            raise TypeError(
+                "feed must be dict type with format: {name: value}.")
+        if not isinstance(fetch, list):
+            raise TypeError("fetch must be list type with format: [name].")
+        req = self._pack_request_package(feed_dict)
+        if not asyn:
+            resp = self._stub.inference(req)
+            return self._unpack_response_package(resp)
+        else:
+            call_future = self._stub.inference.future(req)
+            return PipelinePredictFuture(
+                call_future,
+                functools.partial(
+                    self._unpack_response_package, fetch=fetch))
+
+
+class PipelinePredictFuture(object):
+    def __init__(self, call_future, callback_func):
+        self.call_future_ = call_future
+        self.callback_func_ = callback_func
+
+    def result(self):
+        resp = self.call_future_.result()
+        return self.callback_func_(resp)