diff --git a/fleetrec/run.py b/fleetrec/run.py
index a4b51990fac298cd06aa38af4e6096b4d89e9f5b..d6b083205f472ba94bab4370f017e43dbaae48a7 100644
--- a/fleetrec/run.py
+++ b/fleetrec/run.py
@@ -1,5 +1,9 @@
 import argparse
 import os
+import sys
+
+import yaml
+from paddle.fluid.incubate.fleet.parameter_server import version
 
 from fleetrec.core.factory import TrainerFactory
 from fleetrec.core.utils import envs
@@ -25,20 +29,42 @@ def local_cluster_engine(cluster_envs, model_yaml):
     launch.run()
 
 
-def local_mpi_engine(cluster_envs, model_yaml):
+def local_mpi_engine(model_yaml):
+    print("use 1X1 MPI ClusterTraining at localhost to run model: {}".format(args.model))
+
+    cluster_envs = {}
+    cluster_envs["server_num"] = 1
+    cluster_envs["worker_num"] = 1
+    cluster_envs["start_port"] = 36001
+    cluster_envs["log_dir"] = "logs"
+    cluster_envs["train.trainer"] = "CtrTraining"
+
     print(envs.pretty_print_envs(cluster_envs, ("Local MPI Cluster Envs", "Value")))
     envs.set_runtime_envions(cluster_envs)
     print("coming soon")
 
 
 def yaml_engine(engine_yaml, model_yaml):
-    print("coming soon")
+    with open(engine_yaml, 'r') as rb:
+        _config = yaml.load(rb.read(), Loader=yaml.FullLoader)
+    assert _config is not None
+
+    envs.set_global_envs(_config)
+
+    train_location = envs.get_global_env("engine.file")
+    train_dirname = os.path.dirname(train_location)
+    base_name = os.path.splitext(os.path.basename(train_location))[0]
+    sys.path.append(train_dirname)
+    trainer_class = envs.lazy_instance(base_name, "UserDefineTrainer")
+    trainer = trainer_class(model_yaml)
+    trainer.run()
 
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description='fleet-rec run')
     parser.add_argument("--model", type=str)
     parser.add_argument("--engine", type=str)
+    parser.add_argument("--engine_extras", type=str)
 
     args = parser.parse_args()
 
@@ -46,35 +72,34 @@ if __name__ == "__main__":
         raise ValueError("argument model: {} error, must specify a existed yaml file".format(args.model))
 
     if args.engine.upper() == "SINGLE":
-        print("use SingleTraining to run model: {}".format(args.model))
-        single_envs = {}
-        single_envs["train.trainer"] = "SingleTraining"
-
-        single_engine(single_envs, args.model)
+        if version.is_transpiler():
+            print("use SingleTraining to run model: {}".format(args.model))
+            single_envs = {"train.trainer": "SingleTraining"}
+            single_engine(single_envs, args.model)
+        else:
+            local_mpi_engine(args.model)
     elif args.engine.upper() == "LOCAL_CLUSTER":
         print("use 1X1 ClusterTraining at localhost to run model: {}".format(args.model))
-
-        cluster_envs = {}
-        cluster_envs["server_num"] = 1
-        cluster_envs["worker_num"] = 1
-        cluster_envs["start_port"] = 36001
-        cluster_envs["log_dir"] = "logs"
-        cluster_envs["train.trainer"] = "ClusterTraining"
-        cluster_envs["train.strategy.mode"] = "async"
-
-        local_cluster_engine(cluster_envs, args.model)
-    elif args.engine.upper() == "LOCAL_MPI":
-        print("use 1X1 MPI ClusterTraining at localhost to run model: {}".format(args.model))
-
-        cluster_envs = {}
-        cluster_envs["server_num"] = 1
-        cluster_envs["worker_num"] = 1
-        cluster_envs["start_port"] = 36001
-        cluster_envs["log_dir"] = "logs"
-        cluster_envs["train.trainer"] = "CtrTraining"
-
-        local_mpi_engine(cluster_envs, args.model)
+        if version.is_transpiler():
+            cluster_envs = {}
+            cluster_envs["server_num"] = 1
+            cluster_envs["worker_num"] = 1
+            cluster_envs["start_port"] = 36001
+            cluster_envs["log_dir"] = "logs"
+            cluster_envs["train.trainer"] = "ClusterTraining"
+            cluster_envs["train.strategy.mode"] = "async"
+
+            local_cluster_engine(cluster_envs, args.model)
+        else:
+            local_mpi_engine(args.model)
+    elif args.engine.upper() == "CLUSTER":
+        print("launch ClusterTraining with cluster to run model: {}".format(args.model))
+        run(args.model)
+    elif args.engine.upper() == "USER_DEFINE":
+        engine_file = args.engine_extras
+        if not os.path.exists(engine_file) or not os.path.isfile(engine_file):
+            raise ValueError(
+                "argument engine: user_define error, must specify a existed yaml file".format(args.engine_file))
+        yaml_engine(engine_file, args.model)
     else:
-        if not os.path.exists(args.engine) or not os.path.isfile(args.engine):
-            raise ValueError("argument engine: {} error, must specify a existed yaml file".format(args.engine))
-        yaml_engine(args.engine, args.model)
+        raise ValueError("engine only support SINGLE/LOCAL_CLUSTER/CLUSTER/USER_DEFINE")