Merge pull request #801 from barrierye/update-pipeline-doc

add PipelineWebService

doc/COMPILE.md

@@ -61,7 +61,22 @@ pip install -r python/requirements.txt
 
 If Python3 is used, replace `pip` with `pip3`.
 
+## GOPATH Setting
 
+The default GOPATH is `$HOME/go`, which you can set to other values.
+```shell
+export GOPATH=$HOME/go
+export PATH=$PATH:$GOPATH/bin
+```
+
+## Get go packages
+
+```shell
+go get -u github.com/grpc-ecosystem/grpc-gateway/protoc-gen-grpc-gateway
+go get -u github.com/grpc-ecosystem/grpc-gateway/protoc-gen-swagger
+go get -u github.com/golang/protobuf/protoc-gen-go
+go get -u google.golang.org/grpc
+```
 
 ## Compile Server
 

doc/COMPILE_CN.md

@@ -61,6 +61,22 @@ pip install -r python/requirements.txt
 
 如果使用 Python3，请以 `pip3` 替换 `pip`。
 
+## GOPATH 设置
+
+默认 GOPATH 设置为 `$HOME/go`，您也可以设置为其他值。
+```shell
+export GOPATH=$HOME/go
+export PATH=$PATH:$GOPATH/bin
+```
+
+## 获取 Go packages
+
+```shell
+go get -u github.com/grpc-ecosystem/grpc-gateway/protoc-gen-grpc-gateway
+go get -u github.com/grpc-ecosystem/grpc-gateway/protoc-gen-swagger
+go get -u github.com/golang/protobuf/protoc-gen-go
+go get -u google.golang.org/grpc
+```
 
 
 ## 编译Server部分

doc/PIPELINE_SERVING.md

@@ -251,9 +251,10 @@ server.run_server()
 Where `response_op` is the responseop mentioned above, PipelineServer will initialize Channels according to the topology relationship of each OP and build the calculation graph. `config_yml_path` is the configuration file of PipelineServer. The example file is as follows:
 
 ```yaml
-port: 18080  # gRPC port
+rpc_port: 18080  # gRPC port
 worker_num: 1  # gRPC thread pool size (the number of processes in the process version servicer). The default is 1
 build_dag_each_worker: false  # Whether to use process server or not. The default is false
+http_port: 0 # HTTP service port. Do not start HTTP service when the value is less or equals 0. The default value is 0.
 dag:
     is_thread_op: true  # Whether to use the thread version of OP. The default is true
     client_type: brpc  # Use brpc or grpc client. The default is brpc
@@ -285,6 +286,8 @@ python -m paddle_serving_server.serve --model imdb_cnn_model --port 9292 &> cnn.
 python -m paddle_serving_server.serve --model imdb_bow_model --port 9393 &> bow.log &
 ```
 
+PipelineServing also supports local automatic startup of PaddleServingService. Please refer to the example `python/examples/pipeline/ocr`.
+
 ### Start PipelineServer
 
 Run the following code
@@ -384,7 +387,7 @@ for f in futures:
 
 
 
-## How to optimize through the timeline tool
+## How to optimize with the timeline tool
 
 In order to better optimize the performance, PipelineServing provides a timeline tool to monitor the time of each stage of the whole service.
 

doc/PIPELINE_SERVING_CN.md

@@ -249,9 +249,10 @@ server.run_server()
 其中，`response_op` 为上面提到的 ResponseOp，PipelineServer 将会根据各个 OP 的拓扑关系初始化 Channel 并构建计算图。`config_yml_path` 为 PipelineServer 的配置文件，示例文件如下：
 
 ```yaml
-port: 18080  # gRPC端口号
+rpc_port: 18080  # gRPC端口号
 worker_num: 1  # gRPC线程池大小（进程版 Servicer 中为进程数），默认为 1
 build_dag_each_worker: false  # 是否使用进程版 Servicer，默认为 false
+http_port: 0 # HTTP 服务的端口号，若该值小于或等于 0 则不开启 HTTP 服务，默认为 0
 dag:
     is_thread_op: true  # 是否使用线程版Op，默认为 true
     client_type: brpc  # 使用 brpc 或 grpc client，默认为 brpc
@@ -283,6 +284,8 @@ python -m paddle_serving_server.serve --model imdb_cnn_model --port 9292 &> cnn.
 python -m paddle_serving_server.serve --model imdb_bow_model --port 9393 &> bow.log &
 ```
 
+PipelineServing 也支持本地自动启动 PaddleServingService，请参考 `python/examples/pipeline/ocr` 下的例子。
+
 ### 启动 PipelineServer
 
 运行下面代码

python/CMakeLists.txt

 if (CLIENT)
     file(INSTALL pipeline DESTINATION paddle_serving_client)
-    execute_process(COMMAND ${PYTHON_EXECUTABLE} run_codegen.py
-        WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/paddle_serving_client/pipeline/proto)
     file(GLOB_RECURSE SERVING_CLIENT_PY_FILES paddle_serving_client/*.py)
     set(PY_FILES ${SERVING_CLIENT_PY_FILES})
     SET(PACKAGE_NAME "serving_client")
@@ -11,13 +9,9 @@ endif()
 if (SERVER)
     if (NOT WITH_GPU)
         file(INSTALL pipeline DESTINATION paddle_serving_server)
-        execute_process(COMMAND ${PYTHON_EXECUTABLE} run_codegen.py
-            WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/paddle_serving_server/pipeline/proto)
         file(GLOB_RECURSE SERVING_SERVER_PY_FILES paddle_serving_server/*.py)
     else()
         file(INSTALL pipeline DESTINATION paddle_serving_server_gpu)
-        execute_process(COMMAND ${PYTHON_EXECUTABLE} run_codegen.py
-            WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/paddle_serving_server_gpu/pipeline/proto)
         file(GLOB_RECURSE SERVING_SERVER_PY_FILES paddle_serving_server_gpu/*.py)
     endif()
         set(PY_FILES ${SERVING_SERVER_PY_FILES})
@@ -25,6 +19,8 @@ if (SERVER)
         set(SETUP_LOG_FILE "setup.py.server.log")
 endif()
 
+configure_file(${CMAKE_CURRENT_SOURCE_DIR}/util.py
+    ${CMAKE_CURRENT_BINARY_DIR}/util.py)
 if (CLIENT)
 configure_file(${CMAKE_CURRENT_SOURCE_DIR}/setup.py.client.in
     ${CMAKE_CURRENT_BINARY_DIR}/setup.py)

python/examples/pipeline/imdb_model_ensemble/README_CN.md

+# IMDB model ensemble 样例
+
+## 获取模型
+```
+sh get_data.sh
+```
+
+## 启动服务
+
+```
+python -m paddle_serving_server_gpu.serve --model imdb_cnn_model --port 9292 &> cnn.log &
+python -m paddle_serving_server_gpu.serve --model imdb_bow_model --port 9393 &> bow.log &
+python test_pipeline_server.py &>pipeline.log &
+```
+
+## 启动客户端
+```
+python test_pipeline_client.py
+```
+
+## HTTP 测试
+```
+curl -X POST -k http://localhost:9999/prediction -d '{"key": ["words"], "value": ["i am very sad | 0"]}'
+```

python/examples/pipeline/imdb_model_ensemble/config.yml

-port: 18080
+rpc_port: 18085
 worker_num: 4
 build_dag_each_worker: false
+http_port: 9999
 dag:
     is_thread_op: false
     client_type: brpc

python/examples/pipeline/ocr/README.md

+# OCR Pipeline WebService 
+
+(English|[简体中文](./README_CN.md))
+
+This document will take OCR as an example to show how to use Pipeline WebService to start multi-model tandem services.
+
+## Get Model
+```
+python -m paddle_serving_app.package --get_model ocr_rec
+tar -xzvf ocr_rec.tar.gz
+python -m paddle_serving_app.package --get_model ocr_det
+tar -xzvf ocr_det.tar.gz
+```
+
+## Get Dataset (Optional)
+```
+wget --no-check-certificate https://paddle-serving.bj.bcebos.com/ocr/test_imgs.tar
+tar xf test_imgs.tar
+```
+
+## Start Service
+```
+python web_service.py &>log.txt &
+```
+
+## Test
+```
+python pipeline_http_client.py
+```
+
+
+
+<!--
+## More (PipelineServing)
+
+You can choose one of the following versions to start Service.
+
+### Remote Service Version
+```
+python -m paddle_serving_server_gpu.serve --model ocr_det_model --port 12000 --gpu_id 0 &> det.log &
+python -m paddle_serving_server_gpu.serve --model ocr_rec_model --port 12001 --gpu_id 0 &> rec.log &
+python remote_service_pipeline_server.py &>pipeline.log &
+```
+
+### Local Service Version
+```
+python local_service_pipeline_server.py &>pipeline.log &
+```
+
+### Hybrid Service Version
+```
+python -m paddle_serving_server_gpu.serve --model ocr_rec_model --port 12001 --gpu_id 0 &> rec.log &
+python hybrid_service_pipeline_server.py &>pipeline.log &
+```
+
+## Client Prediction
+
+### RPC
+```
+python pipeline_rpc_client.py
+```
+
+### HTTP
+```
+python pipeline_http_client.py
+```
+-->

python/examples/pipeline/ocr/README_CN.md

+# OCR Pipeline WebService
+
+([English](./README.md)|简体中文)
+
+本文档将以 OCR 为例，介绍如何使用 Pipeline WebService 启动多模型串联的服务。
+
+## 获取模型
+```
+python -m paddle_serving_app.package --get_model ocr_rec
+tar -xzvf ocr_rec.tar.gz
+python -m paddle_serving_app.package --get_model ocr_det
+tar -xzvf ocr_det.tar.gz
+```
+
+## 获取数据集（可选）
+```
+wget --no-check-certificate https://paddle-serving.bj.bcebos.com/ocr/test_imgs.tar
+tar xf test_imgs.tar
+```
+
+## 启动 WebService
+```
+python web_service.py &>log.txt &
+```
+
+## 测试
+```
+python pipeline_http_client.py
+```
+
+<!--
+## 其他 (PipelineServing)
+
+你可以选择下面任意一种版本启动服务。
+
+### 远程服务版本
+```
+python -m paddle_serving_server.serve --model ocr_det_model --port 12000 --gpu_id 0 &> det.log &
+python -m paddle_serving_server.serve --model ocr_rec_model --port 12001 --gpu_id 0 &> rec.log &
+python remote_service_pipeline_server.py &>pipeline.log &
+```
+
+### 本地服务版本
+```
+python local_service_pipeline_server.py &>pipeline.log &
+```
+
+### 混合服务版本
+```
+python -m paddle_serving_server_gpu.serve --model ocr_rec_model --port 12001 --gpu_id 0 &> rec.log &
+python hybrid_service_pipeline_server.py &>pipeline.log &
+```
+
+## 启动客户端
+
+### RPC
+
+```
+python pipeline_rpc_client.py
+```
+
+### HTTP
+
+```
+python pipeline_http_client.py
+```
+-->

python/examples/pipeline/ocr/config.yml

+rpc_port: 18080
+worker_num: 4
+build_dag_each_worker: false
+http_port: 9999
+dag:
+    is_thread_op: false
+    client_type: brpc
+    retry: 1
+    use_profile: false
+op:
+    det:
+        concurrency: 2
+        local_service_conf:
+            model_config: ocr_det_model
+            devices: "0"
+    rec:
+        concurrency: 1
+        timeout: -1
+        retry: 1
+        local_service_conf:
+            model_config: ocr_rec_model
+            devices: "0"

python/examples/pipeline/ocr/hybrid_service_pipeline_server.py

+# Copyright (c) 2020 PaddlePaddle 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.
+# pylint: disable=doc-string-missing
+from paddle_serving_server_gpu.pipeline import Op, RequestOp, ResponseOp
+from paddle_serving_server_gpu.pipeline import PipelineServer
+from paddle_serving_server_gpu.pipeline.proto import pipeline_service_pb2
+from paddle_serving_server_gpu.pipeline.channel import ChannelDataEcode
+from paddle_serving_server_gpu.pipeline import LocalRpcServiceHandler
+import numpy as np
+import cv2
+import time
+import base64
+import json
+from paddle_serving_app.reader import OCRReader
+from paddle_serving_app.reader import Sequential, ResizeByFactor
+from paddle_serving_app.reader import Div, Normalize, Transpose
+from paddle_serving_app.reader import DBPostProcess, FilterBoxes, GetRotateCropImage, SortedBoxes
+import time
+import re
+import base64
+import logging
+
+_LOGGER = logging.getLogger()
+
+
+class DetOp(Op):
+    def init_op(self):
+        self.det_preprocess = Sequential([
+            ResizeByFactor(32, 960), Div(255),
+            Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), Transpose(
+                (2, 0, 1))
+        ])
+        self.filter_func = FilterBoxes(10, 10)
+        self.post_func = DBPostProcess({
+            "thresh": 0.3,
+            "box_thresh": 0.5,
+            "max_candidates": 1000,
+            "unclip_ratio": 1.5,
+            "min_size": 3
+        })
+
+    def preprocess(self, input_dicts):
+        (_, input_dict), = input_dicts.items()
+        data = base64.b64decode(input_dict["image"].encode('utf8'))
+        data = np.fromstring(data, np.uint8)
+        # Note: class variables(self.var) can only be used in process op mode
+        self.im = cv2.imdecode(data, cv2.IMREAD_COLOR)
+        self.ori_h, self.ori_w, _ = self.im.shape
+        det_img = self.det_preprocess(self.im)
+        _, self.new_h, self.new_w = det_img.shape
+        return {"image": det_img}
+
+    def postprocess(self, input_dicts, fetch_dict):
+        det_out = fetch_dict["concat_1.tmp_0"]
+        ratio_list = [
+            float(self.new_h) / self.ori_h, float(self.new_w) / self.ori_w
+        ]
+        dt_boxes_list = self.post_func(det_out, [ratio_list])
+        dt_boxes = self.filter_func(dt_boxes_list[0], [self.ori_h, self.ori_w])
+        out_dict = {"dt_boxes": dt_boxes, "image": self.im}
+        return out_dict
+
+
+class RecOp(Op):
+    def init_op(self):
+        self.ocr_reader = OCRReader()
+        self.get_rotate_crop_image = GetRotateCropImage()
+        self.sorted_boxes = SortedBoxes()
+
+    def preprocess(self, input_dicts):
+        (_, input_dict), = input_dicts.items()
+        im = input_dict["image"]
+        dt_boxes = input_dict["dt_boxes"]
+        dt_boxes = self.sorted_boxes(dt_boxes)
+        feed_list = []
+        img_list = []
+        max_wh_ratio = 0
+        for i, dtbox in enumerate(dt_boxes):
+            boximg = self.get_rotate_crop_image(im, dt_boxes[i])
+            img_list.append(boximg)
+            h, w = boximg.shape[0:2]
+            wh_ratio = w * 1.0 / h
+            max_wh_ratio = max(max_wh_ratio, wh_ratio)
+        for img in img_list:
+            norm_img = self.ocr_reader.resize_norm_img(img, max_wh_ratio)
+            feed = {"image": norm_img}
+            feed_list.append(feed)
+        return feed_list
+
+    def postprocess(self, input_dicts, fetch_dict):
+        rec_res = self.ocr_reader.postprocess(fetch_dict, with_score=True)
+        res_lst = []
+        for res in rec_res:
+            res_lst.append(res[0])
+        res = {"res": str(res_lst)}
+        return res
+
+
+read_op = RequestOp()
+det_op = DetOp(
+    name="det",
+    input_ops=[read_op],
+    local_rpc_service_handler=LocalRpcServiceHandler(
+        model_config="ocr_det_model",
+        workdir="det_workdir",  # defalut: "workdir"
+        thread_num=2,  # defalut: 2
+        devices="0",  # gpu0. defalut: "" (cpu)
+        mem_optim=True,  # defalut: True
+        ir_optim=False,  # defalut: False
+        available_port_generator=None),  # defalut: None
+    concurrency=1)
+rec_op = RecOp(
+    name="rec",
+    input_ops=[det_op],
+    server_endpoints=["127.0.0.1:12001"],
+    fetch_list=["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"],
+    client_config="ocr_rec_client/serving_client_conf.prototxt",
+    concurrency=1)
+response_op = ResponseOp(input_ops=[rec_op])
+
+server = PipelineServer("ocr")
+server.set_response_op(response_op)
+server.prepare_server('config.yml')
+server.run_server()

python/examples/pipeline/ocr/local_service_pipeline_server.py

+# Copyright (c) 2020 PaddlePaddle 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.
+# pylint: disable=doc-string-missing
+from paddle_serving_server_gpu.pipeline import Op, RequestOp, ResponseOp
+from paddle_serving_server_gpu.pipeline import PipelineServer
+from paddle_serving_server_gpu.pipeline.proto import pipeline_service_pb2
+from paddle_serving_server_gpu.pipeline.channel import ChannelDataEcode
+from paddle_serving_server_gpu.pipeline import LocalRpcServiceHandler
+import numpy as np
+import cv2
+import time
+import base64
+import json
+from paddle_serving_app.reader import OCRReader
+from paddle_serving_app.reader import Sequential, ResizeByFactor
+from paddle_serving_app.reader import Div, Normalize, Transpose
+from paddle_serving_app.reader import DBPostProcess, FilterBoxes, GetRotateCropImage, SortedBoxes
+import time
+import re
+import base64
+import logging
+
+_LOGGER = logging.getLogger()
+
+
+class DetOp(Op):
+    def init_op(self):
+        self.det_preprocess = Sequential([
+            ResizeByFactor(32, 960), Div(255),
+            Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), Transpose(
+                (2, 0, 1))
+        ])
+        self.filter_func = FilterBoxes(10, 10)
+        self.post_func = DBPostProcess({
+            "thresh": 0.3,
+            "box_thresh": 0.5,
+            "max_candidates": 1000,
+            "unclip_ratio": 1.5,
+            "min_size": 3
+        })
+
+    def preprocess(self, input_dicts):
+        (_, input_dict), = input_dicts.items()
+        data = base64.b64decode(input_dict["image"].encode('utf8'))
+        data = np.fromstring(data, np.uint8)
+        # Note: class variables(self.var) can only be used in process op mode
+        self.im = cv2.imdecode(data, cv2.IMREAD_COLOR)
+        self.ori_h, self.ori_w, _ = self.im.shape
+        det_img = self.det_preprocess(self.im)
+        _, self.new_h, self.new_w = det_img.shape
+        return {"image": det_img}
+
+    def postprocess(self, input_dicts, fetch_dict):
+        det_out = fetch_dict["concat_1.tmp_0"]
+        ratio_list = [
+            float(self.new_h) / self.ori_h, float(self.new_w) / self.ori_w
+        ]
+        dt_boxes_list = self.post_func(det_out, [ratio_list])
+        dt_boxes = self.filter_func(dt_boxes_list[0], [self.ori_h, self.ori_w])
+        out_dict = {"dt_boxes": dt_boxes, "image": self.im}
+        return out_dict
+
+
+class RecOp(Op):
+    def init_op(self):
+        self.ocr_reader = OCRReader()
+        self.get_rotate_crop_image = GetRotateCropImage()
+        self.sorted_boxes = SortedBoxes()
+
+    def preprocess(self, input_dicts):
+        (_, input_dict), = input_dicts.items()
+        im = input_dict["image"]
+        dt_boxes = input_dict["dt_boxes"]
+        dt_boxes = self.sorted_boxes(dt_boxes)
+        feed_list = []
+        img_list = []
+        max_wh_ratio = 0
+        for i, dtbox in enumerate(dt_boxes):
+            boximg = self.get_rotate_crop_image(im, dt_boxes[i])
+            img_list.append(boximg)
+            h, w = boximg.shape[0:2]
+            wh_ratio = w * 1.0 / h
+            max_wh_ratio = max(max_wh_ratio, wh_ratio)
+        for img in img_list:
+            norm_img = self.ocr_reader.resize_norm_img(img, max_wh_ratio)
+            feed = {"image": norm_img}
+            feed_list.append(feed)
+        return feed_list
+
+    def postprocess(self, input_dicts, fetch_dict):
+        rec_res = self.ocr_reader.postprocess(fetch_dict, with_score=True)
+        res_lst = []
+        for res in rec_res:
+            res_lst.append(res[0])
+        res = {"res": str(res_lst)}
+        return res
+
+
+read_op = RequestOp()
+det_op = DetOp(
+    name="det",
+    input_ops=[read_op],
+    local_rpc_service_handler=LocalRpcServiceHandler(
+        model_config="ocr_det_model",
+        workdir="det_workdir",  # defalut: "workdir"
+        thread_num=2,  # defalut: 2
+        devices="0",  # gpu0. defalut: "" (cpu)
+        mem_optim=True,  # defalut: True
+        ir_optim=False,  # defalut: False
+        available_port_generator=None),  # defalut: None
+    concurrency=1)
+rec_op = RecOp(
+    name="rec",
+    input_ops=[det_op],
+    local_rpc_service_handler=LocalRpcServiceHandler(
+        model_config="ocr_rec_model"),
+    concurrency=1)
+response_op = ResponseOp(input_ops=[rec_op])
+
+server = PipelineServer("ocr")
+server.set_response_op(response_op)
+server.prepare_server('config.yml')
+server.run_server()

python/examples/pipeline/ocr/pipeline_http_client.py

+# Copyright (c) 2020 PaddlePaddle 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 paddle_serving_server_gpu.pipeline import PipelineClient
+import numpy as np
+import requests
+import json
+import cv2
+import base64
+import os
+
+
+def cv2_to_base64(image):
+    return base64.b64encode(image).decode('utf8')
+
+
+url = "http://127.0.0.1:9999/ocr/prediction"
+test_img_dir = "imgs/"
+for img_file in os.listdir(test_img_dir):
+    with open(os.path.join(test_img_dir, img_file), 'rb') as file:
+        image_data1 = file.read()
+    image = cv2_to_base64(image_data1)
+
+for i in range(4):
+    data = {"key": ["image"], "value": [image]}
+    r = requests.post(url=url, data=json.dumps(data))
+    print(r.json())

python/examples/pipeline/ocr/pipeline_rpc_client.py

+# Copyright (c) 2020 PaddlePaddle 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 paddle_serving_server_gpu.pipeline import PipelineClient
+import numpy as np
+import requests
+import json
+import cv2
+import base64
+import os
+
+client = PipelineClient()
+client.connect(['127.0.0.1:18080'])
+
+
+def cv2_to_base64(image):
+    return base64.b64encode(image).decode('utf8')
+
+
+test_img_dir = "imgs/"
+for img_file in os.listdir(test_img_dir):
+    with open(os.path.join(test_img_dir, img_file), 'rb') as file:
+        image_data = file.read()
+    image = cv2_to_base64(image_data)
+
+for i in range(4):
+    ret = client.predict(feed_dict={"image": image}, fetch=["res"])
+    print(ret)

python/examples/pipeline/ocr/remote_service_pipeline_server.py

+# Copyright (c) 2020 PaddlePaddle 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.
+# pylint: disable=doc-string-missing
+from paddle_serving_server_gpu.pipeline import Op, RequestOp, ResponseOp
+from paddle_serving_server_gpu.pipeline import PipelineServer
+from paddle_serving_server_gpu.pipeline.proto import pipeline_service_pb2
+from paddle_serving_server_gpu.pipeline.channel import ChannelDataEcode
+import numpy as np
+import cv2
+import time
+import base64
+import json
+from paddle_serving_app.reader import OCRReader
+from paddle_serving_app.reader import Sequential, ResizeByFactor
+from paddle_serving_app.reader import Div, Normalize, Transpose
+from paddle_serving_app.reader import DBPostProcess, FilterBoxes, GetRotateCropImage, SortedBoxes
+import time
+import re
+import base64
+import logging
+
+_LOGGER = logging.getLogger()
+
+
+class DetOp(Op):
+    def init_op(self):
+        self.det_preprocess = Sequential([
+            ResizeByFactor(32, 960), Div(255),
+            Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), Transpose(
+                (2, 0, 1))
+        ])
+        self.filter_func = FilterBoxes(10, 10)
+        self.post_func = DBPostProcess({
+            "thresh": 0.3,
+            "box_thresh": 0.5,
+            "max_candidates": 1000,
+            "unclip_ratio": 1.5,
+            "min_size": 3
+        })
+
+    def preprocess(self, input_dicts):
+        (_, input_dict), = input_dicts.items()
+        data = base64.b64decode(input_dict["image"].encode('utf8'))
+        data = np.fromstring(data, np.uint8)
+        # Note: class variables(self.var) can only be used in process op mode
+        self.im = cv2.imdecode(data, cv2.IMREAD_COLOR)
+        self.ori_h, self.ori_w, _ = self.im.shape
+        det_img = self.det_preprocess(self.im)
+        _, self.new_h, self.new_w = det_img.shape
+        return {"image": det_img}
+
+    def postprocess(self, input_dicts, fetch_dict):
+        det_out = fetch_dict["concat_1.tmp_0"]
+        ratio_list = [
+            float(self.new_h) / self.ori_h, float(self.new_w) / self.ori_w
+        ]
+        dt_boxes_list = self.post_func(det_out, [ratio_list])
+        dt_boxes = self.filter_func(dt_boxes_list[0], [self.ori_h, self.ori_w])
+        out_dict = {"dt_boxes": dt_boxes, "image": self.im}
+        return out_dict
+
+
+class RecOp(Op):
+    def init_op(self):
+        self.ocr_reader = OCRReader()
+        self.get_rotate_crop_image = GetRotateCropImage()
+        self.sorted_boxes = SortedBoxes()
+
+    def preprocess(self, input_dicts):
+        (_, input_dict), = input_dicts.items()
+        im = input_dict["image"]
+        dt_boxes = input_dict["dt_boxes"]
+        dt_boxes = self.sorted_boxes(dt_boxes)
+        feed_list = []
+        img_list = []
+        max_wh_ratio = 0
+        for i, dtbox in enumerate(dt_boxes):
+            boximg = self.get_rotate_crop_image(im, dt_boxes[i])
+            img_list.append(boximg)
+            h, w = boximg.shape[0:2]
+            wh_ratio = w * 1.0 / h
+            max_wh_ratio = max(max_wh_ratio, wh_ratio)
+        for img in img_list:
+            norm_img = self.ocr_reader.resize_norm_img(img, max_wh_ratio)
+            feed = {"image": norm_img}
+            feed_list.append(feed)
+        return feed_list
+
+    def postprocess(self, input_dicts, fetch_dict):
+        rec_res = self.ocr_reader.postprocess(fetch_dict, with_score=True)
+        res_lst = []
+        for res in rec_res:
+            res_lst.append(res[0])
+        res = {"res": str(res_lst)}
+        return res
+
+
+read_op = RequestOp()
+det_op = DetOp(
+    name="det",
+    input_ops=[read_op],
+    server_endpoints=["127.0.0.1:12000"],
+    fetch_list=["concat_1.tmp_0"],
+    client_config="ocr_det_client/serving_client_conf.prototxt",
+    concurrency=1)
+rec_op = RecOp(
+    name="rec",
+    input_ops=[det_op],
+    server_endpoints=["127.0.0.1:12001"],
+    fetch_list=["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"],
+    client_config="ocr_rec_client/serving_client_conf.prototxt",
+    concurrency=1)
+response_op = ResponseOp(input_ops=[rec_op])
+
+server = PipelineServer("ocr")
+server.set_response_op(response_op)
+server.prepare_server('config.yml')
+server.run_server()

python/examples/pipeline/ocr/web_service.py

+# Copyright (c) 2020 PaddlePaddle 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.
+try:
+    from paddle_serving_server_gpu.web_service import WebService, Op
+except ImportError:
+    from paddle_serving_server.web_service import WebService, Op
+import logging
+import numpy as np
+import cv2
+import base64
+from paddle_serving_app.reader import OCRReader
+from paddle_serving_app.reader import Sequential, ResizeByFactor
+from paddle_serving_app.reader import Div, Normalize, Transpose
+from paddle_serving_app.reader import DBPostProcess, FilterBoxes, GetRotateCropImage, SortedBoxes
+
+_LOGGER = logging.getLogger()
+
+
+class DetOp(Op):
+    def init_op(self):
+        self.det_preprocess = Sequential([
+            ResizeByFactor(32, 960), Div(255),
+            Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), Transpose(
+                (2, 0, 1))
+        ])
+        self.filter_func = FilterBoxes(10, 10)
+        self.post_func = DBPostProcess({
+            "thresh": 0.3,
+            "box_thresh": 0.5,
+            "max_candidates": 1000,
+            "unclip_ratio": 1.5,
+            "min_size": 3
+        })
+
+    def preprocess(self, input_dicts):
+        (_, input_dict), = input_dicts.items()
+        data = base64.b64decode(input_dict["image"].encode('utf8'))
+        data = np.fromstring(data, np.uint8)
+        # Note: class variables(self.var) can only be used in process op mode
+        self.im = cv2.imdecode(data, cv2.IMREAD_COLOR)
+        self.ori_h, self.ori_w, _ = self.im.shape
+        det_img = self.det_preprocess(self.im)
+        _, self.new_h, self.new_w = det_img.shape
+        return {"image": det_img}
+
+    def postprocess(self, input_dicts, fetch_dict):
+        det_out = fetch_dict["concat_1.tmp_0"]
+        ratio_list = [
+            float(self.new_h) / self.ori_h, float(self.new_w) / self.ori_w
+        ]
+        dt_boxes_list = self.post_func(det_out, [ratio_list])
+        dt_boxes = self.filter_func(dt_boxes_list[0], [self.ori_h, self.ori_w])
+        out_dict = {"dt_boxes": dt_boxes, "image": self.im}
+        return out_dict
+
+
+class RecOp(Op):
+    def init_op(self):
+        self.ocr_reader = OCRReader()
+        self.get_rotate_crop_image = GetRotateCropImage()
+        self.sorted_boxes = SortedBoxes()
+
+    def preprocess(self, input_dicts):
+        (_, input_dict), = input_dicts.items()
+        im = input_dict["image"]
+        dt_boxes = input_dict["dt_boxes"]
+        dt_boxes = self.sorted_boxes(dt_boxes)
+        feed_list = []
+        img_list = []
+        max_wh_ratio = 0
+        for i, dtbox in enumerate(dt_boxes):
+            boximg = self.get_rotate_crop_image(im, dt_boxes[i])
+            img_list.append(boximg)
+            h, w = boximg.shape[0:2]
+            wh_ratio = w * 1.0 / h
+            max_wh_ratio = max(max_wh_ratio, wh_ratio)
+        for img in img_list:
+            norm_img = self.ocr_reader.resize_norm_img(img, max_wh_ratio)
+            feed = {"image": norm_img}
+            feed_list.append(feed)
+        return feed_list
+
+    def postprocess(self, input_dicts, fetch_dict):
+        rec_res = self.ocr_reader.postprocess(fetch_dict, with_score=True)
+        res_lst = []
+        for res in rec_res:
+            res_lst.append(res[0])
+        res = {"res": str(res_lst)}
+        return res
+
+
+class OcrService(WebService):
+    def get_pipeline_response(self, read_op):
+        det_op = DetOp(name="det", input_ops=[read_op])
+        rec_op = RecOp(name="rec", input_ops=[det_op])
+        return rec_op
+
+
+uci_service = OcrService(name="ocr")
+uci_service.prepare_pipeline_config("config.yml")
+uci_service.run_service()

python/examples/pipeline/simple_web_service/README.md

+# Simple Pipeline WebService
+
+This document will takes UCI service as an example to introduce how to use Pipeline WebService.
+
+## Get model
+```
+sh get_data.sh
+```
+
+## Start server
+
+```
+python web_service.py &>log.txt &
+```
+
+## Http test
+```
+curl -X POST -k http://localhost:18080/uci/prediction -d '{"key": ["x"], "value": ["0.0137, -0.1136, 0.2553, -0.0692, 0.0582, -0.0727, -0.1583, -0.0584, 0.6283, 0.4919, 0.1856, 0.0795, -0.0332"]}'
+```

python/examples/pipeline/simple_web_service/README_CN.md

+# Simple Pipeline WebService
+
+这里以 Uci 服务为例来介绍 Pipeline WebService 的使用。
+
+## 获取模型
+```
+sh get_data.sh
+```
+
+## 启动服务
+
+```
+python web_service.py &>log.txt &
+```
+
+## 测试
+```
+curl -X POST -k http://localhost:18080/uci/prediction -d '{"key": ["x"], "value": ["0.0137, -0.1136, 0.2553, -0.0692, 0.0582, -0.0727, -0.1583, -0.0584, 0.6283, 0.4919, 0.1856, 0.0795, -0.0332"]}'
+```

python/examples/pipeline/simple_web_service/config.yml

+worker_num: 4
+http_port: 18080
+dag:
+    is_thread_op: false
+op:
+    uci:
+        local_service_conf:
+            model_config: uci_housing_model
+            devices: "" # "0,1"

python/examples/pipeline/simple_web_service/get_data.sh

+wget --no-check-certificate https://paddle-serving.bj.bcebos.com/uci_housing.tar.gz
+tar -xzf uci_housing.tar.gz

python/examples/pipeline/simple_web_service/web_service.py

+# Copyright (c) 2020 PaddlePaddle 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.
+try:
+    from paddle_serving_server_gpu.web_service import WebService, Op
+except ImportError:
+    from paddle_serving_server.web_service import WebService, Op
+import logging
+import numpy as np
+
+_LOGGER = logging.getLogger()
+
+
+class UciOp(Op):
+    def init_op(self):
+        self.separator = ","
+
+    def preprocess(self, input_dicts):
+        (_, input_dict), = input_dicts.items()
+        _LOGGER.info(input_dict)
+        x_value = input_dict["x"]
+        if isinstance(x_value, (str, unicode)):
+            input_dict["x"] = np.array(
+                [float(x.strip()) for x in x_value.split(self.separator)])
+        return input_dict
+
+    def postprocess(self, input_dicts, fetch_dict):
+        # _LOGGER.info(fetch_dict)
+        fetch_dict["price"] = str(fetch_dict["price"][0][0])
+        return fetch_dict
+
+
+class UciService(WebService):
+    def get_pipeline_response(self, read_op):
+        uci_op = UciOp(name="uci", input_ops=[read_op])
+        return uci_op
+
+
+uci_service = UciService(name="uci")
+uci_service.prepare_pipeline_config("config.yml")
+uci_service.run_service()

python/paddle_serving_server/__init__.py

@@ -161,6 +161,10 @@ class Server(object):
         self.container_id = None
         self.model_config_paths = None  # for multi-model in a workflow
 
+    def get_fetch_list(self):
+        fetch_names = [var.alias_name for var in self.model_conf.fetch_var]
+        return fetch_names
+
     def set_max_concurrency(self, concurrency):
         self.max_concurrency = concurrency
 

python/paddle_serving_server/web_service.py

@@ -21,12 +21,36 @@ from paddle_serving_client import Client
 from contextlib import closing
 import socket
 
+from paddle_serving_server import pipeline
+from paddle_serving_server.pipeline import Op
+
 
 class WebService(object):
     def __init__(self, name="default_service"):
         self.name = name
+        # pipeline
+        self._server = pipeline.PipelineServer(self.name)
+
+    def get_pipeline_response(self, read_op):
+        return None
+
+    def prepare_pipeline_config(self, yaml_file):
+        # build dag
+        read_op = pipeline.RequestOp()
+        last_op = self.get_pipeline_response(read_op)
+        if not isinstance(last_op, Op):
+            raise ValueError("The return value type of `get_pipeline_response` "
+                             "function is not Op type, please check function "
+                             "`get_pipeline_response`.")
+        response_op = pipeline.ResponseOp(input_ops=[last_op])
+        self._server.set_response_op(response_op)
+        self._server.prepare_server(yaml_file)
+
+    def run_service(self):
+        self._server.run_server()
 
     def load_model_config(self, model_config):
+        print("This API will be deprecated later. Please do not use it")
         self.model_config = model_config
 
     def _launch_rpc_service(self):
@@ -63,6 +87,7 @@ class WebService(object):
                        device="cpu",
                        mem_optim=True,
                        ir_optim=False):
+        print("This API will be deprecated later. Please do not use it")
         self.workdir = workdir
         self.port = port
         self.device = device
@@ -102,6 +127,7 @@ class WebService(object):
         return result
 
     def run_rpc_service(self):
+        print("This API will be deprecated later. Please do not use it")
         import socket
         localIP = socket.gethostbyname(socket.gethostname())
         print("web service address:")
@@ -151,6 +177,7 @@ class WebService(object):
             "{}".format(self.model_config), gpu=False, profile=False)
 
     def run_web_service(self):
+        print("This API will be deprecated later. Please do not use it")
         self.app_instance.run(host="0.0.0.0",
                               port=self.port,
                               threaded=False,
@@ -160,9 +187,11 @@ class WebService(object):
         return self.app_instance
 
     def preprocess(self, feed=[], fetch=[]):
+        print("This API will be deprecated later. Please do not use it")
         return feed, fetch
 
     def postprocess(self, feed=[], fetch=[], fetch_map=None):
+        print("This API will be deprecated later. Please do not use it")
         for key in fetch_map:
             fetch_map[key] = fetch_map[key].tolist()
         return fetch_map

python/paddle_serving_server_gpu/__init__.py

@@ -209,6 +209,10 @@ class Server(object):
         self.product_name = None
         self.container_id = None
 
+    def get_fetch_list(self):
+        fetch_names = [var.alias_name for var in self.model_conf.fetch_var]
+        return fetch_names
+
     def set_max_concurrency(self, concurrency):
         self.max_concurrency = concurrency
 

python/paddle_serving_server_gpu/web_service.py

@@ -24,17 +24,43 @@ import sys
 import numpy as np
 import paddle_serving_server_gpu as serving
 
+from paddle_serving_server_gpu import pipeline
+from paddle_serving_server_gpu.pipeline import Op
+
 
 class WebService(object):
     def __init__(self, name="default_service"):
         self.name = name
-        self.gpus = []
-        self.rpc_service_list = []
+        # pipeline
+        self._server = pipeline.PipelineServer(self.name)
+
+        self.gpus = []  # deprecated
+        self.rpc_service_list = []  # deprecated
+
+    def get_pipeline_response(self, read_op):
+        return None
+
+    def prepare_pipeline_config(self, yaml_file):
+        # build dag
+        read_op = pipeline.RequestOp()
+        last_op = self.get_pipeline_response(read_op)
+        if not isinstance(last_op, Op):
+            raise ValueError("The return value type of `get_pipeline_response` "
+                             "function is not Op type, please check function "
+                             "`get_pipeline_response`.")
+        response_op = pipeline.ResponseOp(input_ops=[last_op])
+        self._server.set_response_op(response_op)
+        self._server.prepare_server(yaml_file)
+
+    def run_service(self):
+        self._server.run_server()
 
     def load_model_config(self, model_config):
+        print("This API will be deprecated later. Please do not use it")
         self.model_config = model_config
 
     def set_gpus(self, gpus):
+        print("This API will be deprecated later. Please do not use it")
         self.gpus = [int(x) for x in gpus.split(",")]
 
     def default_rpc_service(self,
@@ -88,6 +114,7 @@ class WebService(object):
                        gpuid=0,
                        mem_optim=True,
                        ir_optim=False):
+        print("This API will be deprecated later. Please do not use it")
         self.workdir = workdir
         self.port = port
         self.device = device
@@ -155,6 +182,7 @@ class WebService(object):
         return result
 
     def run_rpc_service(self):
+        print("This API will be deprecated later. Please do not use it")
         import socket
         localIP = socket.gethostbyname(socket.gethostname())
         print("web service address:")
@@ -183,6 +211,7 @@ class WebService(object):
 
     # TODO: maybe change another API name: maybe run_local_predictor?
     def run_debugger_service(self, gpu=False):
+        print("This API will be deprecated later. Please do not use it")
         import socket
         localIP = socket.gethostbyname(socket.gethostname())
         print("web service address:")
@@ -209,18 +238,21 @@ class WebService(object):
             "{}".format(self.model_config), gpu=gpu, profile=False)
 
     def run_web_service(self):
+        print("This API will be deprecated later. Please do not use it")
         self.app_instance.run(host="0.0.0.0",
                               port=self.port,
                               threaded=False,
-                              processes=1)
+                              processes=4)
 
     def get_app_instance(self):
         return self.app_instance
 
     def preprocess(self, feed=[], fetch=[]):
+        print("This API will be deprecated later. Please do not use it")
         return feed, fetch
 
     def postprocess(self, feed=[], fetch=[], fetch_map=None):
-        for key in fetch_map.iterkeys():
+        print("This API will be deprecated later. Please do not use it")
+        for key in fetch_map:
             fetch_map[key] = fetch_map[key].tolist()
         return fetch_map

python/pipeline/__init__.py

@@ -11,8 +11,9 @@
 # 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.
-import logger  # this module must be the first to import
-from operator import Op, RequestOp, ResponseOp
-from pipeline_server import PipelineServer
-from pipeline_client import PipelineClient
-from analyse import Analyst
+from . import logger  # this module must be the first to import
+from .operator import Op, RequestOp, ResponseOp
+from .pipeline_server import PipelineServer
+from .pipeline_client import PipelineClient
+from .local_rpc_service_handler import LocalRpcServiceHandler
+from .analyse import Analyst

python/pipeline/channel.py

@@ -40,7 +40,8 @@ class ChannelDataEcode(enum.Enum):
     RPC_PACKAGE_ERROR = 4
     CLIENT_ERROR = 5
     CLOSED_ERROR = 6
-    UNKNOW = 7
+    NO_SERVICE = 7
+    UNKNOW = 8
 
 
 class ChannelDataType(enum.Enum):

python/pipeline/dag.py

@@ -299,13 +299,12 @@ class DAGExecutor(object):
             sys.stderr.write(profile_str)
 
         # add profile info into rpc_resp
-        profile_value = ""
         if resp_channeldata.client_need_profile:
             profile_set = resp_channeldata.profile_data_set
             profile_set.add(profile_str)
             profile_value = "".join(list(profile_set))
-        rpc_resp.key.append(self._client_profile_key)
-        rpc_resp.value.append(profile_value)
+            rpc_resp.key.append(self._client_profile_key)
+            rpc_resp.value.append(profile_value)
 
         return rpc_resp
 
@@ -338,7 +337,8 @@ class DAG(object):
             self._manager = PipelineProcSyncManager()
         _LOGGER.info("[DAG] Succ init")
 
-    def get_use_ops(self, response_op):
+    @staticmethod
+    def get_use_ops(response_op):
         unique_names = set()
         used_ops = set()
         succ_ops_of_use_op = {}  # {op_name: succ_ops}
@@ -427,11 +427,11 @@ class DAG(object):
             _LOGGER.critical("Failed to build DAG: ResponseOp"
                              " has not been set.")
             os._exit(-1)
-        used_ops, out_degree_ops = self.get_use_ops(response_op)
+        used_ops, out_degree_ops = DAG.get_use_ops(response_op)
         if not self._build_dag_each_worker:
             _LOGGER.info("================= USED OP =================")
             for op in used_ops:
-                if op.name != self._request_name:
+                if not isinstance(op, RequestOp):
                     _LOGGER.info(op.name)
             _LOGGER.info("-------------------------------------------")
         if len(used_ops) <= 1:

python/pipeline/gateway/__init__.py

+# Copyright (c) 2020 PaddlePaddle 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.

python/pipeline/gateway/proto/gateway.proto

+// Copyright (c) 2020 PaddlePaddle 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.
+
+syntax = "proto3";
+package baidu.paddle_serving.pipeline_serving;
+option go_package = ".;pipeline_serving";
+
+import "google/api/annotations.proto";
+
+message Response {
+  repeated string key = 1;
+  repeated string value = 2;
+  int32 ecode = 3;
+  string error_info = 4;
+};
+
+message Request {
+  repeated string key = 1;
+  repeated string value = 2;
+  string name = 3;
+}
+
+service PipelineService {
+  rpc inference(Request) returns (Response) {
+    option (google.api.http) = {
+      post : "/{name=*}/prediction"
+      body : "*"
+    };
+  }
+};

python/pipeline/gateway/proxy_server.go

+// Copyright (c) 2020 PaddlePaddle 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.
+
+package main
+
+import (
+  "C"
+  "flag"
+  "net/http"
+  "log"
+  "strconv"
+
+  "golang.org/x/net/context"
+  "github.com/grpc-ecosystem/grpc-gateway/runtime"
+  "google.golang.org/grpc"
+
+  gw "./proto"
+)
+
+//export run_proxy_server
+func run_proxy_server(grpc_port int, http_port int) error {
+  var (
+    pipelineEndpoint = flag.String("pipeline_endpoint", "localhost:" + strconv.Itoa(grpc_port), "endpoint of PipelineService")
+  )
+
+  ctx := context.Background()
+  ctx, cancel := context.WithCancel(ctx)
+  defer cancel()
+
+  mux := runtime.NewServeMux()
+  opts := []grpc.DialOption{grpc.WithInsecure()}
+  err := gw.RegisterPipelineServiceHandlerFromEndpoint(ctx, mux, *pipelineEndpoint, opts)
+  if err != nil {
+    return err
+  }
+
+  log.Println("start proxy service")
+  return http.ListenAndServe(":" + strconv.Itoa(http_port), mux) // proxy port
+}
+
+func main() {}

python/pipeline/local_rpc_service_handler.py

+# Copyright (c) 2020 PaddlePaddle 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.
+
+import os
+import logging
+import multiprocessing
+try:
+    from paddle_serving_server_gpu import OpMaker, OpSeqMaker, Server
+    PACKAGE_VERSION = "GPU"
+except ImportError:
+    from paddle_serving_server import OpMaker, OpSeqMaker, Server
+    PACKAGE_VERSION = "CPU"
+from . import util
+
+_LOGGER = logging.getLogger(__name__)
+_workdir_name_gen = util.NameGenerator("workdir_")
+
+
+class LocalRpcServiceHandler(object):
+    def __init__(self,
+                 model_config,
+                 workdir="",
+                 thread_num=2,
+                 devices="",
+                 mem_optim=True,
+                 ir_optim=False,
+                 available_port_generator=None):
+        if available_port_generator is None:
+            available_port_generator = util.GetAvailablePortGenerator()
+
+        self._model_config = model_config
+        self._port_list = []
+        if devices == "":
+            # cpu
+            devices = [-1]
+            self._port_list.append(available_port_generator.next())
+            _LOGGER.info("Model({}) will be launch in cpu device. Port({})"
+                         .format(model_config, self._port_list))
+        else:
+            # gpu
+            if PACKAGE_VERSION == "CPU":
+                raise ValueError(
+                    "You are using the CPU version package("
+                    "paddle-serving-server), unable to set devices")
+            devices = [int(x) for x in devices.split(",")]
+            for _ in devices:
+                self._port_list.append(available_port_generator.next())
+            _LOGGER.info("Model({}) will be launch in gpu device: {}. Port({})"
+                         .format(model_config, devices, self._port_list))
+        self._workdir = workdir
+        self._devices = devices
+        self._thread_num = thread_num
+        self._mem_optim = mem_optim
+        self._ir_optim = ir_optim
+
+        self._rpc_service_list = []
+        self._server_pros = []
+        self._fetch_vars = None
+
+    def get_fetch_list(self):
+        return self._fetch_vars
+
+    def get_port_list(self):
+        return self._port_list
+
+    def get_client_config(self):
+        return os.path.join(self._model_config, "serving_server_conf.prototxt")
+
+    def _prepare_one_server(self, workdir, port, gpuid, thread_num, mem_optim,
+                            ir_optim):
+        device = "gpu"
+        if gpuid == -1:
+            device = "cpu"
+        op_maker = OpMaker()
+        read_op = op_maker.create('general_reader')
+        general_infer_op = op_maker.create('general_infer')
+        general_response_op = op_maker.create('general_response')
+
+        op_seq_maker = OpSeqMaker()
+        op_seq_maker.add_op(read_op)
+        op_seq_maker.add_op(general_infer_op)
+        op_seq_maker.add_op(general_response_op)
+
+        server = Server()
+        server.set_op_sequence(op_seq_maker.get_op_sequence())
+        server.set_num_threads(thread_num)
+        server.set_memory_optimize(mem_optim)
+        server.set_ir_optimize(ir_optim)
+
+        server.load_model_config(self._model_config)
+        if gpuid >= 0:
+            server.set_gpuid(gpuid)
+        server.prepare_server(workdir=workdir, port=port, device=device)
+        if self._fetch_vars is None:
+            self._fetch_vars = server.get_fetch_list()
+        return server
+
+    def _start_one_server(self, service_idx):
+        self._rpc_service_list[service_idx].run_server()
+
+    def prepare_server(self):
+        for i, device_id in enumerate(self._devices):
+            if self._workdir != "":
+                workdir = "{}_{}".format(self._workdir, i)
+            else:
+                workdir = _workdir_name_gen.next()
+            self._rpc_service_list.append(
+                self._prepare_one_server(
+                    workdir,
+                    self._port_list[i],
+                    device_id,
+                    thread_num=self._thread_num,
+                    mem_optim=self._mem_optim,
+                    ir_optim=self._ir_optim))
+
+    def start_server(self):
+        for i, service in enumerate(self._rpc_service_list):
+            p = multiprocessing.Process(
+                target=self._start_one_server, args=(i, ))
+            p.daemon = True
+            self._server_pros.append(p)
+        for p in self._server_pros:
+            p.start()

python/pipeline/operator.py

@@ -38,6 +38,7 @@ from .channel import (ThreadChannel, ProcessChannel, ChannelDataEcode,
                       ChannelTimeoutError)
 from .util import NameGenerator
 from .profiler import UnsafeTimeProfiler as TimeProfiler
+from . import local_rpc_service_handler
 
 _LOGGER = logging.getLogger(__name__)
 _op_name_gen = NameGenerator("Op")
@@ -47,46 +48,128 @@ class Op(object):
     def __init__(self,
                  name=None,
                  input_ops=[],
-                 server_endpoints=[],
-                 fetch_list=[],
+                 server_endpoints=None,
+                 fetch_list=None,
                  client_config=None,
-                 concurrency=1,
-                 timeout=-1,
-                 retry=1,
-                 batch_size=1,
-                 auto_batching_timeout=None):
+                 concurrency=None,
+                 timeout=None,
+                 retry=None,
+                 batch_size=None,
+                 auto_batching_timeout=None,
+                 local_rpc_service_handler=None):
+        # In __init__, all the parameters are just saved and Op is not initialized
         if name is None:
             name = _op_name_gen.next()
         self.name = name  # to identify the type of OP, it must be globally unique
         self.concurrency = concurrency  # amount of concurrency
         self.set_input_ops(input_ops)
 
+        self._local_rpc_service_handler = local_rpc_service_handler
         self._server_endpoints = server_endpoints
-        self.with_serving = False
-        if len(self._server_endpoints) != 0:
-            self.with_serving = True
-        self._client_config = client_config
         self._fetch_names = fetch_list
-
-        if timeout > 0:
-            self._timeout = timeout / 1000.0
-        else:
-            self._timeout = -1
+        self._client_config = client_config
+        self._timeout = timeout
         self._retry = max(1, retry)
+        self._batch_size = batch_size
+        self._auto_batching_timeout = auto_batching_timeout
+
         self._input = None
         self._outputs = []
 
-        self._batch_size = batch_size
-        self._auto_batching_timeout = auto_batching_timeout
-        if self._auto_batching_timeout is not None:
-            if self._auto_batching_timeout <= 0 or self._batch_size == 1:
-                _LOGGER.warning(
-                    self._log(
-                        "Because auto_batching_timeout <= 0 or batch_size == 1,"
-                        " set auto_batching_timeout to None."))
-                self._auto_batching_timeout = None
+        self._server_use_profile = False
+        self._tracer = None
+
+        # only for thread op
+        self._for_init_op_lock = threading.Lock()
+        self._for_close_op_lock = threading.Lock()
+        self._succ_init_op = False
+        self._succ_close_op = False
+
+    def init_from_dict(self, conf):
+        # init op
+        if self.concurrency is None:
+            self.concurrency = conf["concurrency"]
+        if self._retry is None:
+            self._retry = conf["retry"]
+        if self._fetch_names is None:
+            self._fetch_names = conf.get("fetch_list")
+        if self._client_config is None:
+            self._client_config = conf.get("client_config")
+
+        if self._timeout is None:
+            self._timeout = conf["timeout"]
+        if self._timeout > 0:
+            self._timeout = self._timeout / 1000.0
+        else:
+            self._timeout = -1
+
+        if self._batch_size is None:
+            self._batch_size = conf["batch_size"]
+        if self._auto_batching_timeout is None:
+            self._auto_batching_timeout = conf["auto_batching_timeout"]
+        if self._auto_batching_timeout <= 0 or self._batch_size == 1:
+            _LOGGER.warning(
+                self._log(
+                    "Because auto_batching_timeout <= 0 or batch_size == 1,"
+                    " set auto_batching_timeout to None."))
+            self._auto_batching_timeout = None
+        else:
+            self._auto_batching_timeout = self._auto_batching_timeout / 1000.0
+
+        if self._server_endpoints is None:
+            server_endpoints = conf.get("server_endpoints", [])
+            if len(server_endpoints) != 0:
+                # remote service
+                self.with_serving = True
+                self._server_endpoints = server_endpoints
             else:
-                self._auto_batching_timeout = self._auto_batching_timeout / 1000.0
+                if self._local_rpc_service_handler is None:
+                    local_service_conf = conf.get("local_service_conf")
+                    _LOGGER.info("local_service_conf: {}".format(
+                        local_service_conf))
+                    model_config = local_service_conf.get("model_config")
+                    _LOGGER.info("model_config: {}".format(model_config))
+                    if model_config is None:
+                        self.with_serving = False
+                    else:
+                        # local rpc service
+                        self.with_serving = True
+                        service_handler = local_rpc_service_handler.LocalRpcServiceHandler(
+                            model_config=model_config,
+                            workdir=local_service_conf["workdir"],
+                            thread_num=local_service_conf["thread_num"],
+                            devices=local_service_conf["devices"],
+                            mem_optim=local_service_conf["mem_optim"],
+                            ir_optim=local_service_conf["ir_optim"])
+                        service_handler.prepare_server()  # get fetch_list
+                        serivce_ports = service_handler.get_port_list()
+                        self._server_endpoints = [
+                            "127.0.0.1:{}".format(p) for p in serivce_ports
+                        ]
+                        if self._client_config is None:
+                            self._client_config = service_handler.get_client_config(
+                            )
+                        if self._fetch_names is None:
+                            self._fetch_names = service_handler.get_fetch_list()
+                        self._local_rpc_service_handler = service_handler
+                else:
+                    self.with_serving = True
+                    self._local_rpc_service_handler.prepare_server(
+                    )  # get fetch_list
+                    serivce_ports = self._local_rpc_service_handler.get_port_list(
+                    )
+                    self._server_endpoints = [
+                        "127.0.0.1:{}".format(p) for p in serivce_ports
+                    ]
+                    if self._client_config is None:
+                        self._client_config = self._local_rpc_service_handler.get_client_config(
+                        )
+                    if self._fetch_names is None:
+                        self._fetch_names = self._local_rpc_service_handler.get_fetch_list(
+                        )
+        else:
+            self.with_serving = True
+
         if not isinstance(self, RequestOp) and not isinstance(self, ResponseOp):
             _LOGGER.info(
                 self._log("\n\tinput_ops: {},"
@@ -98,20 +181,22 @@ class Op(object):
                           "\n\tretry: {},"
                           "\n\tbatch_size: {},"
                           "\n\tauto_batching_timeout(s): {}".format(
-                              ", ".join([op.name for op in input_ops
+                              ", ".join([op.name for op in self._input_ops
                                          ]), self._server_endpoints,
                               self._fetch_names, self._client_config,
                               self.concurrency, self._timeout, self._retry,
                               self._batch_size, self._auto_batching_timeout)))
 
-        self._server_use_profile = False
-        self._tracer = None
-
-        # only for thread op
-        self._for_init_op_lock = threading.Lock()
-        self._for_close_op_lock = threading.Lock()
-        self._succ_init_op = False
-        self._succ_close_op = False
+    def launch_local_rpc_service(self):
+        if self._local_rpc_service_handler is None:
+            _LOGGER.warning(
+                self._log("Failed to launch local rpc"
+                          " service: local_rpc_service_handler is None."))
+            return
+        port = self._local_rpc_service_handler.get_port_list()
+        self._local_rpc_service_handler.start_server()
+        _LOGGER.info("Op({}) use local rpc service at port: {}"
+                     .format(self.name, port))
 
     def use_default_auto_batching_config(self):
         if self._batch_size != 1:
@@ -775,7 +860,9 @@ class RequestOp(Op):
         for idx, key in enumerate(request.key):
             data = request.value[idx]
             try:
-                data = eval(data)
+                evaled_data = eval(data)
+                if isinstance(evaled_data, np.ndarray):
+                    data = evaled_data
             except Exception as e:
                 pass
             dictdata[key] = data

python/pipeline/pipeline_client.py

@@ -42,11 +42,12 @@ class PipelineClient(object):
 
     def _pack_request_package(self, feed_dict, profile):
         req = pipeline_service_pb2.Request()
+        np.set_printoptions(threshold=sys.maxsize)
         for key, value in feed_dict.items():
             req.key.append(key)
             if isinstance(value, np.ndarray):
                 req.value.append(value.__repr__())
-            elif isinstance(value, str):
+            elif isinstance(value, (str, unicode)):
                 req.value.append(value)
             elif isinstance(value, list):
                 req.value.append(np.array(value).__repr__())
@@ -75,7 +76,9 @@ class PipelineClient(object):
                 continue
             data = resp.value[idx]
             try:
-                data = eval(data)
+                evaled_data = eval(data)
+                if isinstance(evaled_data, np.ndarray):
+                    data = evaled_data
             except Exception as e:
                 pass
             fetch_map[key] = data

python/pipeline/pipeline_server.py

@@ -22,22 +22,31 @@ from contextlib import closing
 import multiprocessing
 import yaml
 
-from .proto import pipeline_service_pb2_grpc
-from .operator import ResponseOp
-from .dag import DAGExecutor
+from .proto import pipeline_service_pb2_grpc, pipeline_service_pb2
+from . import operator
+from . import dag
+from . import util
+from . import channel
 
 _LOGGER = logging.getLogger(__name__)
 
 
 class PipelineServicer(pipeline_service_pb2_grpc.PipelineServiceServicer):
-    def __init__(self, response_op, dag_conf, worker_idx=-1):
+    def __init__(self, name, response_op, dag_conf, worker_idx=-1):
         super(PipelineServicer, self).__init__()
+        self._name = name
+
         # init dag executor
-        self._dag_executor = DAGExecutor(response_op, dag_conf, worker_idx)
+        self._dag_executor = dag.DAGExecutor(response_op, dag_conf, worker_idx)
         self._dag_executor.start()
         _LOGGER.info("[PipelineServicer] succ init")
 
     def inference(self, request, context):
+        if request.name != "" and request.name != self._name:
+            resp = pipeline_service_pb2.Response()
+            resp.ecode = channel.ChannelDataEcode.NO_SERVICE.value
+            resp.error_info = "Failed to inference: Service name error."
+            return resp
         resp = self._dag_executor.call(request)
         return resp
 
@@ -57,35 +66,83 @@ def _reserve_port(port):
 
 
 class PipelineServer(object):
-    def __init__(self):
-        self._port = None
+    def __init__(self, name=None):
+        self._name = name  # for grpc-gateway path
+        self._rpc_port = None
         self._worker_num = None
         self._response_op = None
+        self._proxy_server = None
+
+    def _grpc_gateway(self, grpc_port, http_port):
+        import os
+        from ctypes import cdll
+        from . import gateway
+        lib_path = os.path.join(
+            os.path.dirname(gateway.__file__), "libproxy_server.so")
+        proxy_server = cdll.LoadLibrary(lib_path)
+        proxy_server.run_proxy_server(grpc_port, http_port)
+
+    def _run_grpc_gateway(self, grpc_port, http_port):
+        if http_port <= 0:
+            _LOGGER.info("Ignore grpc_gateway configuration.")
+            return
+        if not util.AvailablePortGenerator.port_is_available(http_port):
+            raise SystemExit("Failed to run grpc-gateway: prot {} "
+                             "is already used".format(http_port))
+        if self._proxy_server is not None:
+            raise RuntimeError("Proxy server has been started.")
+        self._proxy_server = multiprocessing.Process(
+            target=self._grpc_gateway, args=(
+                grpc_port,
+                http_port, ))
+        self._proxy_server.daemon = True
+        self._proxy_server.start()
 
     def set_response_op(self, response_op):
-        if not isinstance(response_op, ResponseOp):
+        if not isinstance(response_op, operator.ResponseOp):
             raise Exception("Failed to set response_op: response_op "
                             "must be ResponseOp type.")
         if len(response_op.get_input_ops()) != 1:
             raise Exception("Failed to set response_op: response_op "
                             "can only have one previous op.")
         self._response_op = response_op
+        self._used_op, _ = dag.DAG.get_use_ops(self._response_op)
+
+    def prepare_server(self, yml_file=None, yml_dict=None):
+        conf = ServerYamlConfChecker.load_server_yaml_conf(
+            yml_file=yml_file, yml_dict=yml_dict)
+
+        self._rpc_port = conf.get("rpc_port")
+        self._http_port = conf.get("http_port")
+        if self._rpc_port is None:
+            if self._http_port is None:
+                raise SystemExit("Failed to prepare_server: rpc_port or "
+                                 "http_port can not be None.")
+            else:
+                # http mode: generate rpc_port
+                if not util.AvailablePortGenerator.port_is_available(
+                        self._http_port):
+                    raise SystemExit("Failed to prepare_server: http_port({}) "
+                                     "is already used".format(self._http_port))
+                self._rpc_port = util.GetAvailablePortGenerator().next()
+        else:
+            if not util.AvailablePortGenerator.port_is_available(
+                    self._rpc_port):
+                raise SystemExit("Failed to prepare_server: prot {} "
+                                 "is already used".format(self._rpc_port))
+            if self._http_port is None:
+                # rpc mode
+                pass
+            else:
+                # http mode
+                if not util.AvailablePortGenerator.port_is_available(
+                        self._http_port):
+                    raise SystemExit("Failed to prepare_server: http_port({}) "
+                                     "is already used".format(self._http_port))
 
-    def _port_is_available(self, port):
-        with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock:
-            sock.settimeout(2)
-            result = sock.connect_ex(('0.0.0.0', port))
-        return result != 0
-
-    def prepare_server(self, yml_file):
-        conf = ServerYamlConfChecker.load_server_yaml_conf(yml_file)
-
-        self._port = conf["port"]
-        if not self._port_is_available(self._port):
-            raise SystemExit("Failed to prepare_server: prot {} "
-                             "is already used".format(self._port))
         self._worker_num = conf["worker_num"]
         self._build_dag_each_worker = conf["build_dag_each_worker"]
+        self._init_ops(conf["op"])
 
         _LOGGER.info("============= PIPELINE SERVER =============")
         _LOGGER.info("\n{}".format(
@@ -98,10 +155,40 @@ class PipelineServer(object):
         _LOGGER.info("-------------------------------------------")
 
         self._conf = conf
+        self._start_local_rpc_service()
+
+    def _init_ops(self, op_conf):
+        default_conf = {
+            "concurrency": 1,
+            "timeout": -1,
+            "retry": 1,
+            "batch_size": 1,
+            "auto_batching_timeout": -1,
+            "local_service_conf": {
+                "workdir": "",
+                "thread_num": 2,
+                "devices": "",
+                "mem_optim": True,
+                "ir_optim": False,
+            },
+        }
+        for op in self._used_op:
+            if not isinstance(op, operator.RequestOp) and not isinstance(
+                    op, operator.ResponseOp):
+                conf = op_conf.get(op.name, default_conf)
+                op.init_from_dict(conf)
+
+    def _start_local_rpc_service(self):
+        # only brpc now
+        if self._conf["dag"]["client_type"] != "brpc":
+            _LOGGER.warning("Local service version must be brpc type now.")
+        for op in self._used_op:
+            if not isinstance(op, operator.RequestOp):
+                op.launch_local_rpc_service()
 
     def run_server(self):
         if self._build_dag_each_worker:
-            with _reserve_port(self._port) as port:
+            with _reserve_port(self._rpc_port) as port:
                 bind_address = 'localhost:{}'.format(port)
                 workers = []
                 for i in range(self._worker_num):
@@ -111,6 +198,9 @@ class PipelineServer(object):
                         args=(bind_address, self._response_op, self._conf, i))
                     worker.start()
                     workers.append(worker)
+                self._run_grpc_gateway(
+                    grpc_port=self._rpc_port,
+                    http_port=self._http_port)  # start grpc_gateway
                 for worker in workers:
                     worker.join()
         else:
@@ -120,9 +210,13 @@ class PipelineServer(object):
                          ('grpc.max_receive_message_length', 256 * 1024 * 1024)
                          ])
             pipeline_service_pb2_grpc.add_PipelineServiceServicer_to_server(
-                PipelineServicer(self._response_op, self._conf), server)
-            server.add_insecure_port('[::]:{}'.format(self._port))
+                PipelineServicer(self._name, self._response_op, self._conf),
+                server)
+            server.add_insecure_port('[::]:{}'.format(self._rpc_port))
             server.start()
+            self._run_grpc_gateway(
+                grpc_port=self._rpc_port,
+                http_port=self._http_port)  # start grpc_gateway
             server.wait_for_termination()
 
     def _run_server_func(self, bind_address, response_op, dag_conf, worker_idx):
@@ -133,7 +227,8 @@ class PipelineServer(object):
             futures.ThreadPoolExecutor(
                 max_workers=1, ), options=options)
         pipeline_service_pb2_grpc.add_PipelineServiceServicer_to_server(
-            PipelineServicer(response_op, dag_conf, worker_idx), server)
+            PipelineServicer(self._name, response_op, dag_conf, worker_idx),
+            server)
         server.add_insecure_port(bind_address)
         server.start()
         server.wait_for_termination()
@@ -144,12 +239,25 @@ class ServerYamlConfChecker(object):
         pass
 
     @staticmethod
-    def load_server_yaml_conf(yml_file):
-        with open(yml_file) as f:
-            conf = yaml.load(f.read())
+    def load_server_yaml_conf(yml_file=None, yml_dict=None):
+        if yml_file is not None and yml_dict is not None:
+            raise SystemExit("Failed to prepare_server: only one of yml_file"
+                             " or yml_dict can be selected as the parameter.")
+        if yml_file is not None:
+            with open(yml_file) as f:
+                conf = yaml.load(f.read())
+        elif yml_dict is not None:
+            conf = yml_dict
+        else:
+            raise SystemExit("Failed to prepare_server: yml_file or yml_dict"
+                             " can not be None.")
         ServerYamlConfChecker.check_server_conf(conf)
         ServerYamlConfChecker.check_dag_conf(conf["dag"])
         ServerYamlConfChecker.check_tracer_conf(conf["dag"]["tracer"])
+        for op_name in conf["op"]:
+            ServerYamlConfChecker.check_op_conf(conf["op"][op_name])
+            ServerYamlConfChecker.check_local_service_conf(conf["op"][op_name][
+                "local_service_conf"])
         return conf
 
     @staticmethod
@@ -161,26 +269,80 @@ class ServerYamlConfChecker(object):
     @staticmethod
     def check_server_conf(conf):
         default_conf = {
-            "port": 9292,
+            # "rpc_port": 9292,
             "worker_num": 1,
             "build_dag_each_worker": False,
+            #"http_port": 0,
             "dag": {},
+            "op": {},
         }
 
         conf_type = {
-            "port": int,
+            "rpc_port": int,
+            "http_port": int,
             "worker_num": int,
             "build_dag_each_worker": bool,
+            "grpc_gateway_port": int,
         }
 
         conf_qualification = {
-            "port": [(">=", 1024), ("<=", 65535)],
+            "rpc_port": [(">=", 1024), ("<=", 65535)],
+            "http_port": [(">=", 1024), ("<=", 65535)],
             "worker_num": (">=", 1),
         }
 
         ServerYamlConfChecker.check_conf(conf, default_conf, conf_type,
                                          conf_qualification)
 
+    @staticmethod
+    def check_local_service_conf(conf):
+        default_conf = {
+            "workdir": "",
+            "thread_num": 2,
+            "devices": "",
+            "mem_optim": True,
+            "ir_optim": False,
+        }
+        conf_type = {
+            "model_config": str,
+            "workdir": str,
+            "thread_num": int,
+            "devices": str,
+            "mem_optim": bool,
+            "ir_optim": bool,
+        }
+        conf_qualification = {"thread_num": (">=", 1), }
+        ServerYamlConfChecker.check_conf(conf, default_conf, conf_type,
+                                         conf_qualification)
+
+    @staticmethod
+    def check_op_conf(conf):
+        default_conf = {
+            "concurrency": 1,
+            "timeout": -1,
+            "retry": 1,
+            "batch_size": 1,
+            "auto_batching_timeout": -1,
+            "local_service_conf": {},
+        }
+        conf_type = {
+            "server_endpoints": list,
+            "fetch_list": list,
+            "client_config": str,
+            "concurrency": int,
+            "timeout": int,
+            "retry": int,
+            "batch_size": int,
+            "auto_batching_timeout": int,
+        }
+        conf_qualification = {
+            "concurrency": (">=", 1),
+            "retry": (">=", 1),
+            "batch_size": (">=", 1),
+        }
+        ServerYamlConfChecker.check_conf(conf, default_conf, conf_type,
+                                         conf_qualification)
+
     @staticmethod
     def check_tracer_conf(conf):
         default_conf = {"interval_s": -1, }
@@ -231,6 +393,8 @@ class ServerYamlConfChecker(object):
     @staticmethod
     def check_conf_type(conf, conf_type):
         for key, val in conf_type.items():
+            if key not in conf:
+                continue
             if not isinstance(conf[key], val):
                 raise SystemExit("[CONF] {} must be {} type, but get {}."
                                  .format(key, val, type(conf[key])))
@@ -238,6 +402,8 @@ class ServerYamlConfChecker(object):
     @staticmethod
     def check_conf_qualification(conf, conf_qualification):
         for key, qualification in conf_qualification.items():
+            if key not in conf:
+                continue
             if not isinstance(qualification, list):
                 qualification = [qualification]
             if not ServerYamlConfChecker.qualification_check(conf[key],

python/pipeline/proto/pipeline_service.proto

@@ -18,6 +18,7 @@ package baidu.paddle_serving.pipeline_serving;
 message Request {
   repeated string key = 1;
   repeated string value = 2;
+  optional string name = 3;
 };
 
 message Response {

python/pipeline/util.py

@@ -17,6 +17,8 @@ import logging
 import threading
 import multiprocessing
 import multiprocessing.managers
+from contextlib import closing
+import socket
 if sys.version_info.major == 2:
     import Queue
     from Queue import PriorityQueue
@@ -29,6 +31,34 @@ else:
 _LOGGER = logging.getLogger(__name__)
 
 
+class AvailablePortGenerator(object):
+    def __init__(self, start_port=12000):
+        self._curr_port = start_port
+
+    @staticmethod
+    def port_is_available(port):
+        with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock:
+            sock.settimeout(2)
+            result = sock.connect_ex(('0.0.0.0', port))
+        if result != 0:
+            return True
+        else:
+            return False
+
+    def next(self):
+        while not AvailablePortGenerator.port_is_available(self._curr_port):
+            self._curr_port += 1
+        self._curr_port += 1
+        return self._curr_port - 1
+
+
+_AvailablePortGenerator = AvailablePortGenerator()
+
+
+def GetAvailablePortGenerator():
+    return _AvailablePortGenerator
+
+
 class NameGenerator(object):
     # use unsafe-id-generator
     def __init__(self, prefix):

python/setup.py.app.in

@@ -16,7 +16,6 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import platform
 import os
 
 from setuptools import setup, Distribution, Extension
@@ -24,18 +23,9 @@ from setuptools import find_packages
 from setuptools import setup
 from paddle_serving_app.version import serving_app_version
 from pkg_resources import DistributionNotFound, get_distribution
+import util
         
-def python_version():
-    return [int(v) for v in platform.python_version().split(".")]
-
-def find_package(pkgname):
-    try:
-        get_distribution(pkgname)
-        return True
-    except DistributionNotFound:
-        return False
-
-max_version, mid_version, min_version = python_version()
+max_version, mid_version, min_version = util.python_version()
 
 if '${PACK}' == 'ON':
     copy_lib()

python/setup.py.client.in

@@ -16,7 +16,6 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import platform
 import os
 import sys
 
@@ -24,20 +23,10 @@ from setuptools import setup, Distribution, Extension
 from setuptools import find_packages
 from setuptools import setup
 from paddle_serving_client.version import serving_client_version
-from pkg_resources import DistributionNotFound, get_distribution
+import util
 
 py_version = sys.version_info
         
-def python_version():
-    return [int(v) for v in platform.python_version().split(".")]
-
-def find_package(pkgname):
-    try:
-        get_distribution(pkgname)
-        return True
-    except DistributionNotFound:
-        return False
-
 def copy_lib():
     if py_version[0] == 2:
         lib_list = ['libpython2.7.so.1.0', 'libssl.so.10', 'libcrypto.so.10'] 
@@ -51,18 +40,20 @@ def copy_lib():
         text = r.read()
         os.popen('cp {} ./paddle_serving_client/lib'.format(text.strip().split(' ')[1]))
 
-max_version, mid_version, min_version = python_version()
+max_version, mid_version, min_version = util.python_version()
+
+# gen pipeline proto code
+util.gen_pipeline_code("paddle_serving_client")
 
 if '${PACK}' == 'ON':
     copy_lib()
 
-
 REQUIRED_PACKAGES = [
     'six >= 1.10.0', 'protobuf >= 3.11.0', 'numpy >= 1.12', 'grpcio >= 1.28.1',
     'grpcio-tools >= 1.28.1'
 ]
 
-if not find_package("paddlepaddle") and not find_package("paddlepaddle-gpu"):
+if not util.find_package("paddlepaddle") and not util.find_package("paddlepaddle-gpu"):
     REQUIRED_PACKAGES.append("paddlepaddle")
 
 
@@ -72,8 +63,10 @@ packages=['paddle_serving_client',
     	  'paddle_serving_client.metric',
     	  'paddle_serving_client.utils',
           'paddle_serving_client.pipeline',
-          'paddle_serving_client.pipeline.proto']
-package_data={'paddle_serving_client': ['serving_client.so','lib/*'],}
+          'paddle_serving_client.pipeline.proto',
+          'paddle_serving_client.pipeline.gateway',
+          'paddle_serving_client.pipeline.gateway.proto']
+package_data={'paddle_serving_client': ['serving_client.so', 'lib/*', 'pipeline/gateway/libproxy_server.so'],}
 package_dir={'paddle_serving_client':
              '${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_client',
              'paddle_serving_client.proto':
@@ -87,7 +80,11 @@ package_dir={'paddle_serving_client':
              'paddle_serving_client.pipeline':
              '${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_client/pipeline',
              'paddle_serving_client.pipeline.proto':
-             '${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_client/pipeline/proto'}
+             '${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_client/pipeline/proto',
+             'paddle_serving_client.pipeline.gateway':
+             '${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_client/pipeline/gateway',
+             'paddle_serving_client.pipeline.gateway.proto':
+             '${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_client/pipeline/gateway/proto'}
 
 setup(
     name='paddle-serving-client',

python/setup.py.in

@@ -16,17 +16,14 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import platform
-
 from setuptools import setup, Distribution, Extension
 from setuptools import find_packages
 from setuptools import setup
 from paddle_serving.version import serving_client_version
+from grpc_tools import protoc
+import util
 
-def python_version():
-    return [int(v) for v in platform.python_version().split(".")]
-
-max_version, mid_version, min_version = python_version()
+max_version, mid_version, min_version = util.python_version()
 
 REQUIRED_PACKAGES = [
     'six >= 1.10.0', 'protobuf >= 3.1.0','paddlepaddle'

python/setup.py.server.in

@@ -16,25 +16,16 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import platform
-
 from setuptools import setup, Distribution, Extension
 from setuptools import find_packages
 from setuptools import setup
 from paddle_serving_server.version import serving_server_version
-from pkg_resources import DistributionNotFound, get_distribution
-
-def find_package(pkgname):
-    try:
-        get_distribution(pkgname)
-        return True
-    except DistributionNotFound:
-        return False
+import util
 
-def python_version():
-    return [int(v) for v in platform.python_version().split(".")]
+max_version, mid_version, min_version = util.python_version()
 
-max_version, mid_version, min_version = python_version()
+# gen pipeline proto code
+util.gen_pipeline_code("paddle_serving_server")
 
 REQUIRED_PACKAGES = [
     'six >= 1.10.0', 'protobuf >= 3.11.0', 'grpcio >= 1.28.1', 'grpcio-tools >= 1.28.1',
@@ -44,7 +35,9 @@ REQUIRED_PACKAGES = [
 packages=['paddle_serving_server',
           'paddle_serving_server.proto',
           'paddle_serving_server.pipeline',
-          'paddle_serving_server.pipeline.proto']
+          'paddle_serving_server.pipeline.proto',
+          'paddle_serving_server.pipeline.gateway',
+          'paddle_serving_server.pipeline.gateway.proto']
 
 package_dir={'paddle_serving_server':
              '${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server',
@@ -53,7 +46,13 @@ package_dir={'paddle_serving_server':
              'paddle_serving_server.pipeline':
              '${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server/pipeline',
              'paddle_serving_server.pipeline.proto':
-             '${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server/pipeline/proto'}
+             '${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server/pipeline/proto',
+             'paddle_serving_server.pipeline.gateway':
+             '${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server/pipeline/gateway',
+             'paddle_serving_server.pipeline.gateway.proto':
+             '${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server/pipeline/gateway/proto'}
+
+package_data={'paddle_serving_server': ['pipeline/gateway/libproxy_server.so'],}
 
 setup(
     name='paddle-serving-server',
@@ -65,6 +64,7 @@ setup(
     author_email='guru4elephant@gmail.com',
     install_requires=REQUIRED_PACKAGES,
     packages=packages,
+    package_data=package_data,
     package_dir=package_dir,
     # PyPI package information.
     classifiers=[

python/setup.py.server_gpu.in

@@ -16,25 +16,16 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import platform
-
 from setuptools import setup, Distribution, Extension
 from setuptools import find_packages
 from setuptools import setup
 from paddle_serving_server_gpu.version import serving_server_version
-from pkg_resources import DistributionNotFound, get_distribution
-
-def find_package(pkgname):
-    try:
-        get_distribution(pkgname)
-        return True
-    except DistributionNotFound:
-        return False
+import util
 
-def python_version():
-    return [int(v) for v in platform.python_version().split(".")]
+max_version, mid_version, min_version = util.python_version()
 
-max_version, mid_version, min_version = python_version()
+# gen pipeline proto code
+util.gen_pipeline_code("paddle_serving_server_gpu")
 
 REQUIRED_PACKAGES = [
     'six >= 1.10.0', 'protobuf >= 3.11.0', 'grpcio >= 1.28.1', 'grpcio-tools >= 1.28.1',
@@ -44,7 +35,9 @@ REQUIRED_PACKAGES = [
 packages=['paddle_serving_server_gpu',
           'paddle_serving_server_gpu.proto',
           'paddle_serving_server_gpu.pipeline',
-          'paddle_serving_server_gpu.pipeline.proto']
+          'paddle_serving_server_gpu.pipeline.proto',
+          'paddle_serving_server_gpu.pipeline.gateway',
+          'paddle_serving_server_gpu.pipeline.gateway.proto']
 
 package_dir={'paddle_serving_server_gpu':
              '${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server_gpu',
@@ -53,7 +46,13 @@ package_dir={'paddle_serving_server_gpu':
              'paddle_serving_server_gpu.pipeline':
              '${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server_gpu/pipeline',
              'paddle_serving_server_gpu.pipeline.proto':
-             '${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server_gpu/pipeline/proto'}
+             '${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server_gpu/pipeline/proto',
+             'paddle_serving_server_gpu.pipeline.gateway':
+             '${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server_gpu/pipeline/gateway',
+             'paddle_serving_server_gpu.pipeline.gateway.proto':
+             '${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server_gpu/pipeline/gateway/proto'}
+
+package_data={'paddle_serving_server_gpu': ['pipeline/gateway/libproxy_server.so'],}
 
 setup(
     name='paddle-serving-server-gpu',
@@ -65,6 +64,7 @@ setup(
     author_email='guru4elephant@gmail.com',
     install_requires=REQUIRED_PACKAGES,
     packages=packages,
+    package_data=package_data,
     package_dir=package_dir,
     # PyPI package information.
     classifiers=[

python/util.py

+# Copyright (c) 2020 PaddlePaddle 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 pkg_resources import DistributionNotFound, get_distribution
+from grpc_tools import protoc
+import os
+import platform
+
+
+def python_version():
+    return [int(v) for v in platform.python_version().split(".")]
+
+
+def find_package(pkgname):
+    try:
+        get_distribution(pkgname)
+        return True
+    except DistributionNotFound:
+        return False
+
+
+def gen_pipeline_code(package_name):
+    # pipeline service proto
+    protoc.main((
+        '',
+        '-I.',
+        '--python_out=.',
+        '--grpc_python_out=.',
+        '{}/pipeline/proto/pipeline_service.proto'.format(package_name), ))
+
+    # pipeline grpc-gateway proto
+    # *.pb.go
+    ret = os.system(
+        "cd {}/pipeline/gateway/proto/ && "
+        "../../../../../third_party/install/protobuf/bin/protoc -I. "
+        "-I$GOPATH/src "
+        "-I$GOPATH/src/github.com/grpc-ecosystem/grpc-gateway/third_party/googleapis "
+        "--go_out=plugins=grpc:. "
+        "gateway.proto".format(package_name))
+    if ret != 0:
+        exit(1)
+    # *.gw.go
+    ret = os.system(
+        "cd {}/pipeline/gateway/proto/ && "
+        "../../../../../third_party/install/protobuf/bin/protoc -I. "
+        "-I$GOPATH/src "
+        "-I$GOPATH/src/github.com/grpc-ecosystem/grpc-gateway/third_party/googleapis "
+        "--grpc-gateway_out=logtostderr=true:. "
+        "gateway.proto".format(package_name))
+    if ret != 0:
+        exit(1)
+
+    # pipeline grpc-gateway shared-lib
+    ret = os.system(
+        "cd {}/pipeline/gateway && "
+        "go build -buildmode=c-shared -o libproxy_server.so proxy_server.go".
+        format(package_name))
+    if ret != 0:
+        exit(1)

tools/serving_build.sh

@@ -19,6 +19,13 @@ function init() {
     cd Serving
     export SERVING_WORKDIR=$PWD
     $PYTHONROOT/bin/python -m pip install -r python/requirements.txt
+    export GOPATH=$HOME/go
+    export PATH=$PATH:$GOPATH/bin
+
+    go get -u github.com/grpc-ecosystem/grpc-gateway/protoc-gen-grpc-gateway
+    go get -u github.com/grpc-ecosystem/grpc-gateway/protoc-gen-swagger
+    go get -u github.com/golang/protobuf/protoc-gen-go
+    go get -u google.golang.org/grpc
 }
 
 function check_cmd() {
@@ -298,7 +305,6 @@ function python_test_bert() {
     cd bert # pwd: /Serving/python/examples/bert
     case $TYPE in
         CPU)
-            pip install paddlehub
             # Because download from paddlehub may timeout,
             # download the model from bos(max_seq_len=128).
             wget https://paddle-serving.bj.bcebos.com/paddle_hub_models/text/SemanticModel/bert_chinese_L-12_H-768_A-12.tar.gz
@@ -306,14 +312,12 @@ function python_test_bert() {
             sh get_data.sh
             check_cmd "python -m paddle_serving_server.serve --model bert_chinese_L-12_H-768_A-12_model --port 9292 &"
             sleep 5
-            pip install paddle_serving_app
             check_cmd "head -n 10 data-c.txt | python bert_client.py --model bert_chinese_L-12_H-768_A-12_client/serving_client_conf.prototxt"
             kill_server_process
             echo "bert RPC inference pass"
             ;;
         GPU)
             export CUDA_VISIBLE_DEVICES=0
-            pip install paddlehub
             # Because download from paddlehub may timeout,
             # download the model from bos(max_seq_len=128).
             wget https://paddle-serving.bj.bcebos.com/paddle_hub_models/text/SemanticModel/bert_chinese_L-12_H-768_A-12.tar.gz
@@ -321,7 +325,6 @@ function python_test_bert() {
             sh get_data.sh
             check_cmd "python -m paddle_serving_server_gpu.serve --model bert_chinese_L-12_H-768_A-12_model --port 9292 --gpu_ids 0 &"
             sleep 5
-            pip install paddle_serving_app
             check_cmd "head -n 10 data-c.txt | python bert_client.py --model bert_chinese_L-12_H-768_A-12_client/serving_client_conf.prototxt"
             kill_server_process
             echo "bert RPC inference pass"
@@ -760,13 +763,14 @@ function python_test_resnet50(){
 }
 
 function python_test_pipeline(){
-    # pwd:/ Serving/python/examples
+    # pwd: /Serving/python/examples
     local TYPE=$1
     export SERVING_BIN=${SERVING_WORKDIR}/build-server-${TYPE}/core/general-server/serving
     unsetproxy
-    cd pipeline/imdb_model_ensemble
+    cd pipeline # pwd: /Serving/python/examples/pipeline
     case $TYPE in
         CPU)
+            cd imdb_model_ensemble # pwd: /Serving/python/examples/pipeline/imdb_model_ensemble
             # start paddle serving service (brpc)
             sh get_data.sh
             python -m paddle_serving_server.serve --model imdb_cnn_model --port 9292 --workdir test9292 &> cnn.log &
@@ -775,7 +779,7 @@ function python_test_pipeline(){
             
             # test: thread servicer & thread op
             cat << EOF > config.yml
-port: 18080
+rpc_port: 18080
 worker_num: 4
 build_dag_each_worker: false
 dag:
@@ -792,7 +796,7 @@ EOF
 
             # test: thread servicer & process op
             cat << EOF > config.yml
-port: 18080
+rpc_port: 18080
 worker_num: 4
 build_dag_each_worker: false
 dag:
@@ -809,7 +813,7 @@ EOF
 
             # test: process servicer & process op
             cat << EOF > config.yml
-port: 18080
+rpc_port: 18080
 worker_num: 4
 build_dag_each_worker: false
 dag:
@@ -828,7 +832,7 @@ EOF
             pip uninstall grpcio -y
             pip install grpcio --no-binary=grpcio
             cat << EOF > config.yml
-port: 18080
+rpc_port: 18080
 worker_num: 4
 build_dag_each_worker: true
 dag:
@@ -852,7 +856,7 @@ EOF
             python -m paddle_serving_server.serve --model imdb_bow_model --port 9393 --use_multilang --workdir test9393 &> bow.log &
             sleep 5
             cat << EOF > config.yml
-port: 18080
+rpc_port: 18080
 worker_num: 4
 build_dag_each_worker: false
 dag:
@@ -869,16 +873,47 @@ EOF
             kill_server_process
             kill_process_by_port 9292
             kill_process_by_port 9393
+            cd ..
+
+            cd simple_web_service # pwd: /Serving/python/examples/pipeline/simple_web_service
+            sh get_data.sh
+            python web_service.py >/dev/null &
+            sleep 5
+            curl -X POST -k http://localhost:18080/uci/prediction -d '{"key": ["x"], "value": ["0.0137, -0.1136, 0.2553, -0.0692, 0.0582, -0.0727, -0.1583, -0.0584, 0.6283, 0.4919, 0.1856, 0.0795, -0.0332"]}'
+             check http code
+            http_code=`curl -X POST -k -d '{"key":["x"], "value": ["0.0137, -0.1136, 0.2553, -0.0692, 0.0582, -0.0727, -0.1583, -0.0584, 0.6283, 0.4919, 0.1856, 0.0795, -0.0332"]}' -s -w "%{http_code}" -o /dev/null http://localhost:18080/uci/prediction`
+            if [ ${http_code} -ne 200 ]; then
+                echo "HTTP status code -ne 200"
+                exit 1
+            fi
+            ps -ef | grep "web_service" | grep -v grep | awk '{print $2}' | xargs kill
+            ps -ef | grep "pipeline" | grep -v grep | awk '{print $2}' | xargs kill
+            kill_server_process
+            cd ..
             ;;
         GPU)
-            echo "pipeline ignore GPU test"
+            cd simple_web_service # pwd: /Serving/python/examples/pipeline/simple_web_service
+            sh get_data.sh
+            python web_service.py >/dev/null &
+            sleep 5
+            curl -X POST -k http://localhost:18080/uci/prediction -d '{"key": ["x"], "value": ["0.0137, -0.1136, 0.2553, -0.0692, 0.0582, -0.0727, -0.1583, -0.0584, 0.6283, 0.4919, 0.1856, 0.0795, -0.0332"]}'
+            # check http code
+            http_code=`curl -X POST -k -d '{"key":["x"], "value": ["0.0137, -0.1136, 0.2553, -0.0692, 0.0582, -0.0727, -0.1583, -0.0584, 0.6283, 0.4919, 0.1856, 0.0795, -0.0332"]}' -s -w "%{http_code}" -o /dev/null http://localhost:18080/uci/prediction`
+            if [ ${http_code} -ne 200 ]; then
+                echo "HTTP status code -ne 200"
+                exit 1
+            fi
+            ps -ef | grep "web_service" | grep -v grep | awk '{print $2}' | xargs kill
+            ps -ef | grep "pipeline" | grep -v grep | awk '{print $2}' | xargs kill
+            kill_server_process
+            cd .. # pwd: /Serving/python/examples/pipeline
             ;;
         *)
             echo "error type"
             exit 1
             ;;
     esac
-    cd ../../
+    cd ..
     setproxy
     unset SERVING_BIN
 }
@@ -928,118 +963,8 @@ function monitor_test() {
     mkdir _monitor_test && cd _monitor_test # pwd: /Serving/_monitor_test
     case $TYPE in
         CPU):
-            pip install pyftpdlib
-            mkdir remote_path
-            mkdir local_path
-            cd remote_path # pwd: /Serving/_monitor_test/remote_path
-            check_cmd "python -m pyftpdlib -p 8000 &>/dev/null &"
-            cd .. # pwd: /Serving/_monitor_test
-
-            # type: ftp
-            # remote_path: /
-            # remote_model_name: uci_housing.tar.gz
-            # local_tmp_path: ___tmp
-            # local_path: local_path
-            cd remote_path # pwd: /Serving/_monitor_test/remote_path
-            wget --no-check-certificate https://paddle-serving.bj.bcebos.com/uci_housing.tar.gz
-            touch donefile
-            cd ..  # pwd: /Serving/_monitor_test
-            mkdir -p local_path/uci_housing_model
-            python -m paddle_serving_server.monitor \
-                    --type='ftp' --ftp_host='127.0.0.1' --ftp_port='8000' \
-                    --remote_path='/' --remote_model_name='uci_housing.tar.gz' \
-                    --remote_donefile_name='donefile' --local_path='local_path' \
-                    --local_model_name='uci_housing_model' --local_timestamp_file='fluid_time_file' \
-                    --local_tmp_path='___tmp' --unpacked_filename='uci_housing_model' \
-                    --interval='1' >/dev/null &
-            sleep 10
-            if [ ! -f "local_path/uci_housing_model/fluid_time_file" ]; then
-                echo "local_path/uci_housing_model/fluid_time_file not exist."
-                exit 1
-            fi
-            ps -ef | grep "monitor" | grep -v grep | awk '{print $2}' | xargs kill
-            rm -rf remote_path/*
-            rm -rf local_path/*
-
-            # type: ftp
-            # remote_path: /tmp_dir
-            # remote_model_name: uci_housing_model
-            # local_tmp_path: ___tmp
-            # local_path: local_path
-            mkdir -p remote_path/tmp_dir && cd remote_path/tmp_dir # pwd: /Serving/_monitor_test/remote_path/tmp_dir
-            wget --no-check-certificate https://paddle-serving.bj.bcebos.com/uci_housing.tar.gz
-            tar -xzf uci_housing.tar.gz
-            touch donefile
-            cd ../.. # pwd: /Serving/_monitor_test
-            mkdir -p local_path/uci_housing_model
-            python -m paddle_serving_server.monitor \
-                    --type='ftp' --ftp_host='127.0.0.1' --ftp_port='8000' \
-                    --remote_path='/tmp_dir' --remote_model_name='uci_housing_model' \
-                    --remote_donefile_name='donefile' --local_path='local_path' \
-                    --local_model_name='uci_housing_model' --local_timestamp_file='fluid_time_file' \
-                    --local_tmp_path='___tmp' --interval='1' >/dev/null &
-            sleep 10
-            if [ ! -f "local_path/uci_housing_model/fluid_time_file" ]; then
-                echo "local_path/uci_housing_model/fluid_time_file not exist."
-                exit 1
-            fi
-            ps -ef | grep "monitor" | grep -v grep | awk '{print $2}' | xargs kill
-            rm -rf remote_path/*
-            rm -rf local_path/*
-
-            # type: general
-            # remote_path: /
-            # remote_model_name: uci_housing.tar.gz
-            # local_tmp_path: ___tmp
-            # local_path: local_path
-            cd remote_path # pwd: /Serving/_monitor_test/remote_path
-            wget --no-check-certificate https://paddle-serving.bj.bcebos.com/uci_housing.tar.gz
-            touch donefile
-            cd ..  # pwd: /Serving/_monitor_test
-            mkdir -p local_path/uci_housing_model
-            python -m paddle_serving_server.monitor \
-                    --type='general' --general_host='ftp://127.0.0.1:8000' \
-                    --remote_path='/' --remote_model_name='uci_housing.tar.gz' \
-                    --remote_donefile_name='donefile' --local_path='local_path' \
-                    --local_model_name='uci_housing_model' --local_timestamp_file='fluid_time_file' \
-                    --local_tmp_path='___tmp' --unpacked_filename='uci_housing_model' \
-                    --interval='1' >/dev/null &
-            sleep 10
-            if [ ! -f "local_path/uci_housing_model/fluid_time_file" ]; then
-                echo "local_path/uci_housing_model/fluid_time_file not exist."
-                exit 1
-            fi
-            ps -ef | grep "monitor" | grep -v grep | awk '{print $2}' | xargs kill
-            rm -rf remote_path/*
-            rm -rf local_path/*
-
-            # type: general
-            # remote_path: /tmp_dir
-            # remote_model_name: uci_housing_model
-            # local_tmp_path: ___tmp
-            # local_path: local_path
-            mkdir -p remote_path/tmp_dir && cd remote_path/tmp_dir # pwd: /Serving/_monitor_test/remote_path/tmp_dir
-            wget --no-check-certificate https://paddle-serving.bj.bcebos.com/uci_housing.tar.gz
-            tar -xzf uci_housing.tar.gz
-            touch donefile
-            cd ../.. # pwd: /Serving/_monitor_test
-            mkdir -p local_path/uci_housing_model
-            python -m paddle_serving_server.monitor \
-                    --type='general' --general_host='ftp://127.0.0.1:8000' \
-                    --remote_path='/tmp_dir' --remote_model_name='uci_housing_model' \
-                    --remote_donefile_name='donefile' --local_path='local_path' \
-                    --local_model_name='uci_housing_model' --local_timestamp_file='fluid_time_file' \
-                    --local_tmp_path='___tmp' --interval='1' >/dev/null &
-            sleep 10
-            if [ ! -f "local_path/uci_housing_model/fluid_time_file" ]; then
-                echo "local_path/uci_housing_model/fluid_time_file not exist."
-                exit 1
-            fi
-            ps -ef | grep "monitor" | grep -v grep | awk '{print $2}' | xargs kill
-            rm -rf remote_path/*
-            rm -rf local_path/*
-
-            ps -ef | grep "pyftpdlib" | grep -v grep | awk '{print $2}' | xargs kill
+            # The CPU part and GPU part are identical.
+            # In order to avoid Travis CI timeout (50 min), the CPU version is not checked
             ;;
         GPU):
             pip install pyftpdlib