diff --git a/python/paddle/distributed/fleet/base/fleet_base.py b/python/paddle/distributed/fleet/base/fleet_base.py
index 9ea912c78c56a98d662d2e0e5058ee890a0cc2ed..1a4b79e6ae1ca9444ab3827decbcc6847f33d27c 100644
--- a/python/paddle/distributed/fleet/base/fleet_base.py
+++ b/python/paddle/distributed/fleet/base/fleet_base.py
@@ -179,6 +179,9 @@ class Fleet(object):
                 fleet.init(strategy=strategy)
 
         """
+        if strategy is None:
+            strategy = DistributedStrategy()
+        self._user_defined_strategy = copy.deepcopy(strategy)
 
         if role_maker is None:
             if isinstance(is_collective, bool):
@@ -220,10 +223,6 @@ class Fleet(object):
             else:
                 paddle.distributed.init_parallel_env()
 
-        if strategy is None:
-            strategy = DistributedStrategy()
-        self._user_defined_strategy = copy.deepcopy(strategy)
-
     def is_first_worker(self):
         """
         Check whether the node is the first instance of worker.
diff --git a/python/paddle/fluid/dygraph/parallel.py b/python/paddle/fluid/dygraph/parallel.py
index 852684cb95d1ad678e5c914b48ab71919a2af55d..d7576ddc70a2760606fbfacf16ed7a0e826ca63f 100644
--- a/python/paddle/fluid/dygraph/parallel.py
+++ b/python/paddle/fluid/dygraph/parallel.py
@@ -395,11 +395,10 @@ class DataParallel(layers.Layer):
                                              1024)
             self.init_reducer()
         else:
-            warnings.warn(
-                "nranks is less than 2, "
-                "maybe you need to check the current system environment."
-                " Need to use spawn or fleetrun to "
-                "start distributed programs.")
+            warnings.warn("The program will return to single-card operation. "
+                          "Please check 1, whether you use spawn or fleetrun "
+                          "to start the program. 2. Whether it is a multi-card "
+                          "program. 3. Is the current environment multi-card.")
 
     def init_reducer(self):
         layers_param = []