diff --git a/python/paddle/fleet/base/fleet_base.py b/python/paddle/fleet/base/fleet_base.py
index 46d06e5d026cae0cb2fcc78360b2b3d0faf0acd9..13b9fc3220a0911415b6abc6f4cf91298038e298 100644
--- a/python/paddle/fleet/base/fleet_base.py
+++ b/python/paddle/fleet/base/fleet_base.py
@@ -309,6 +309,7 @@ class Fleet(object):
                     loss, self._role_maker, self.user_defined_optimizer,
                     self.user_defined_strategy, valid_optimizer_list,
                     valid_graph_optimizer_list)
+
         optimize_ops = []
         params_grads = []
         if meta_optimizer:
diff --git a/python/paddle/fleet/meta_optimizers/graph_execution_optimizer.py b/python/paddle/fleet/meta_optimizers/graph_execution_optimizer.py
index 13d62a6d462a2b76a609739a63785922221bc3b0..cc3d1cd2128bd6b4e632e45e025cded7ace0fa41 100644
--- a/python/paddle/fleet/meta_optimizers/graph_execution_optimizer.py
+++ b/python/paddle/fleet/meta_optimizers/graph_execution_optimizer.py
@@ -78,14 +78,14 @@ class GraphExecutionOptimizer(MetaOptimizerBase):
     # should fix the variable 
     def _setup_nccl_op(self, startup_program, main_program):
         trainer_endpoints = self.role_maker.get_trainer_endpoints()
-        trainers = trainer_endpoints
         trainer_id = self.role_maker.worker_index()
         current_endpoint = self.role_maker.get_trainer_endpoints()[trainer_id]
         trainer_endpoints_env = ",".join(trainer_endpoints)
         trainers_num = self.role_maker.worker_num()
-        trainer_endpoints.remove(current_endpoint)
         if trainer_id == 0:
-            wait_server_ready(trainer_endpoints)
+            other_trainer_endpoints = trainer_endpoints[:]
+            other_trainer_endpoints.remove(current_endpoint)
+            wait_server_ready(other_trainer_endpoints)
         nccl_id_var = startup_program.global_block().create_var(
             name="NCCLID", persistable=True, type=core.VarDesc.VarType.RAW)
         for i in range(1, self.user_defined_strategy.nccl_comm_num):
@@ -110,7 +110,7 @@ class GraphExecutionOptimizer(MetaOptimizerBase):
             inputs={},
             outputs={"NCCLID": nccl_id_var},
             attrs={
-                "trainers": trainers,
+                "trainers": trainer_endpoints,
                 "trainer_id": trainer_id,
                 "nccl_comm_num": self.user_defined_strategy.nccl_comm_num,
                 "use_hierarchical_allreduce":