diff --git a/examples/pipeline/test_single_linr.py b/examples/pipeline/test_single_linr.py
new file mode 100644
index 0000000000..0828cf1671
--- /dev/null
+++ b/examples/pipeline/test_single_linr.py
@@ -0,0 +1,72 @@
+#
+#  Copyright 2019 The FATE Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+from fate_client.pipeline import FateFlowPipeline
+from fate_client.pipeline.components.fate import CoordinatedLinR
+from fate_client.pipeline.components.fate import Evaluation
+from fate_client.pipeline.interface import DataWarehouseChannel
+
+pipeline = FateFlowPipeline().set_roles(guest="9999", host="9998", arbiter="9998")
+
+"""feature_scale_0 = FeatureScale(name="feature_scale_0",
+                               method="min_max",
+                               train_data=intersection_0.outputs["output_data"])
+
+feature_scale_1 = FeatureScale(name="feature_scale_1",
+                               test_data=intersection_1.outputs["output_data"],
+                               input_model=feature_scale_0.outputs["output_model"])"""
+
+linr_0 = CoordinatedLinR("linr_0",
+                         max_iter=10,
+                         batch_size=-1,
+                         init_param={"fit_intercept": False})
+
+linr_0.guest.component_setting(train_data=DataWarehouseChannel(name="motor_hetero_guest",
+                                                               namespace="experiment"))
+linr_0.hosts[0].component_setting(train_data=DataWarehouseChannel(name="motor_hetero_host",
+                                                                  namespace="experiment"))
+
+evaluation_0 = Evaluation("evaluation_0",
+                          runtime_roles="guest",
+                          input_data=linr_0.outputs["train_output_data"])
+
+# pipeline.add_task(feature_scale_0)
+# pipeline.add_task(feature_scale_1)
+pipeline.add_task(linr_0)
+pipeline.add_task(evaluation_0)
+# pipeline.add_task(hetero_feature_binning_0)
+pipeline.compile()
+print(pipeline.get_dag())
+pipeline.fit()
+
+# print(pipeline.get_task_info("statistics_0").get_output_model())
+print(pipeline.get_task_info("linr_0").get_output_model())
+print(pipeline.get_task_info("linr_0").get_output_data())
+print(pipeline.get_task_info("evaluation_0").get_output_metrics())
+
+pipeline.deploy([linr_0])
+
+predict_pipeline = FateFlowPipeline()
+
+deployed_pipeline = pipeline.get_deployed_pipeline()
+linr_0.guest.component_setting(test_data=DataWarehouseChannel(name="breast_hetero_guest",
+                                                              namespace="experiment"))
+linr_0.hosts[0].component_setting(test_data=DataWarehouseChannel(name="breast_hetero_host",
+                                                                 namespace="experiment"))
+
+predict_pipeline.add_task(deployed_pipeline)
+predict_pipeline.compile()
+# print("\n\n\n")
+# print(predict_pipeline.compile().get_dag())
+predict_pipeline.predict()