Merge branch 'develop' into ce

cmake/external/protobuf.cmake

@@ -143,7 +143,6 @@ function(grpc_protobuf_generate_python SRCS)
     set(${SRCS} ${${SRCS}} PARENT_SCOPE)
 endfunction()
 
-
 # Print and set the protobuf library information,
 # finish this cmake process and exit from this file.
 macro(PROMPT_PROTOBUF_LIB)

core/configure/CMakeLists.txt

@@ -86,6 +86,7 @@ add_custom_command(TARGET general_model_config_py_proto POST_BUILD
 		COMMAND cp *.py ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server/proto
 		COMMENT "Copy generated general_model_config proto file into directory paddle_serving_server/proto."
 		WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
+
 add_custom_command(TARGET multi_lang_general_model_service_py_proto POST_BUILD
                 COMMAND ${CMAKE_COMMAND} -E make_directory ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server/proto
                 COMMAND cp *.py ${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server/proto

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")
@@ -7,8 +10,14 @@ 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})

python/examples/imdb/test_client.py

@@ -29,6 +29,6 @@ imdb_dataset.load_resource(sys.argv[2])
 for line in sys.stdin:
     word_ids, label = imdb_dataset.get_words_and_label(line)
     feed = {"words": word_ids}
-    fetch = ["acc", "cost", "prediction"]
+    fetch = ["prediction"]
     fetch_map = client.predict(feed=feed, fetch=fetch)
     print("{} {}".format(fetch_map["prediction"][0], label[0]))

python/examples/pipeline/imdb_model_ensemble/config.yml

+use_multithread: true
+client_type: brpc
+retry: 1
+profile: false
+prot: 8080
+worker_num: 2

python/examples/pipeline/imdb_model_ensemble/get_data.sh

+wget --no-check-certificate https://fleet.bj.bcebos.com/text_classification_data.tar.gz
+wget --no-check-certificate https://paddle-serving.bj.bcebos.com/imdb-demo/imdb_model.tar.gz
+tar -zxvf text_classification_data.tar.gz
+tar -zxvf imdb_model.tar.gz

python/examples/pipeline/imdb_model_ensemble/test_pipeline_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_client.pipeline import PipelineClient
+import numpy as np
+from line_profiler import LineProfiler
+
+client = PipelineClient()
+client.connect('localhost:8080')
+
+lp = LineProfiler()
+lp_wrapper = lp(client.predict)
+
+words = 'i am very sad | 0'
+
+for i in range(1):
+    fetch_map = lp_wrapper(feed_dict={"words": words}, fetch=["prediction"])
+    print(fetch_map)
+
+#lp.print_stats()

python/examples/pipeline/imdb_model_ensemble/test_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.pipeline import Op, RequestOp, ResponseOp
+from paddle_serving_server.pipeline import PipelineServer
+from paddle_serving_server.pipeline.proto import pipeline_service_pb2
+from paddle_serving_server.pipeline.channel import ChannelDataEcode
+import numpy as np
+import logging
+from paddle_serving_app.reader import IMDBDataset
+
+_LOGGER = logging.getLogger(__name__)
+
+logging.basicConfig(
+    format='%(asctime)s %(levelname)-8s [%(filename)s:%(lineno)d] %(message)s',
+    datefmt='%Y-%m-%d %H:%M',
+    level=logging.DEBUG)
+
+
+class ImdbRequestOp(RequestOp):
+    def load_user_resources(self):
+        self.imdb_dataset = IMDBDataset()
+        self.imdb_dataset.load_resource('imdb.vocab')
+
+    def unpack_request_package(self, request):
+        dictdata = {}
+        for idx, key in enumerate(request.key):
+            if key != "words":
+                continue
+            words = request.value[idx]
+            word_ids, _ = self.imdb_dataset.get_words_and_label(words)
+            dictdata[key] = np.array(word_ids)
+        return dictdata
+
+
+class CombineOp(Op):
+    def preprocess(self, input_data):
+        combined_prediction = 0
+        for op_name, data in input_data.items():
+            _LOGGER.info("{}: {}".format(op_name, data["prediction"]))
+            combined_prediction += data["prediction"]
+        data = {"prediction": combined_prediction / 2}
+        return data
+
+
+class ImdbResponseOp(ResponseOp):
+    # Here ImdbResponseOp is consistent with the default ResponseOp implementation
+    def pack_response_package(self, channeldata):
+        resp = pipeline_service_pb2.Response()
+        resp.ecode = channeldata.ecode
+        if resp.ecode == ChannelDataEcode.OK.value:
+            feed = channeldata.parse()
+            # ndarray to string
+            for name, var in feed.items():
+                resp.value.append(var.__repr__())
+                resp.key.append(name)
+        else:
+            resp.error_info = channeldata.error_info
+        return resp
+
+
+read_op = ImdbRequestOp()
+bow_op = Op(name="bow",
+            input_ops=[read_op],
+            server_endpoints=["127.0.0.1:9393"],
+            fetch_list=["prediction"],
+            client_config="imdb_bow_client_conf/serving_client_conf.prototxt",
+            concurrency=1,
+            timeout=-1,
+            retry=1)
+cnn_op = Op(name="cnn",
+            input_ops=[read_op],
+            server_endpoints=["127.0.0.1:9292"],
+            fetch_list=["prediction"],
+            client_config="imdb_cnn_client_conf/serving_client_conf.prototxt",
+            concurrency=1,
+            timeout=-1,
+            retry=1)
+combine_op = CombineOp(
+    name="combine",
+    input_ops=[bow_op, cnn_op],
+    concurrency=1,
+    timeout=-1,
+    retry=1)
+
+# fetch output of bow_op
+# response_op = ImdbResponseOp(input_ops=[bow_op])
+
+# fetch output of combine_op
+response_op = ImdbResponseOp(input_ops=[combine_op])
+
+# use default ResponseOp implementation
+# response_op = ResponseOp(input_ops=[combine_op])
+
+server = PipelineServer()
+server.set_response_op(response_op)
+server.prepare_server('config.yml')
+server.run_server()

python/examples/util/timeline_trace.py

@@ -16,10 +16,16 @@ def prase(pid_str, time_str, counter):
         if len(name_list) == 2:
             name = name_list[0]
         else:
-            name = name_list[0] + "_" + name_list[1]
+            name = "_".join(name_list[:-1])
+        name_list = name.split("#")
+        if len(name_list) > 1:
+            tid = name_list[-1]
+            name = "#".join(name_list[:-1])
+        else:
+            tid = 0
         event_dict = {}
         event_dict["name"] = name
-        event_dict["tid"] = 0
+        event_dict["tid"] = tid
         event_dict["pid"] = pid
         event_dict["ts"] = ts
         event_dict["ph"] = ph

python/pipeline/__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.
+
+from operator import Op, RequestOp, ResponseOp
+from pipeline_server import PipelineServer
+from pipeline_client import PipelineClient

python/pipeline/operator.py

@@ -12,3 +12,473 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # pylint: disable=doc-string-missing
+
+import threading
+import multiprocessing
+from paddle_serving_client import MultiLangClient, Client
+from concurrent import futures
+import logging
+import func_timeout
+from numpy import *
+
+from .proto import pipeline_service_pb2
+from .channel import ThreadChannel, ProcessChannel, ChannelDataEcode, ChannelData, ChannelDataType
+from .util import NameGenerator
+
+_LOGGER = logging.getLogger(__name__)
+_op_name_gen = NameGenerator("Op")
+
+
+class Op(object):
+    def __init__(self,
+                 name=None,
+                 input_ops=[],
+                 server_endpoints=[],
+                 fetch_list=[],
+                 client_config=None,
+                 concurrency=1,
+                 timeout=-1,
+                 retry=1):
+        if name is None:
+            name = _op_name_gen.next()
+        self._is_run = False
+        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._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
+
+        self._timeout = timeout
+        self._retry = max(1, retry)
+        self._input = None
+        self._outputs = []
+        self._profiler = None
+
+    def init_profiler(self, profiler):
+        self._profiler = profiler
+
+    def _profiler_record(self, string):
+        if self._profiler is None:
+            return
+        self._profiler.record(string)
+
+    def init_client(self, client_type, client_config, server_endpoints,
+                    fetch_names):
+        if self.with_serving == False:
+            _LOGGER.debug("{} no client".format(self.name))
+            return
+        _LOGGER.debug("{} client_config: {}".format(self.name, client_config))
+        _LOGGER.debug("{} fetch_names: {}".format(self.name, fetch_names))
+        if client_type == 'brpc':
+            self._client = Client()
+            self._client.load_client_config(client_config)
+        elif client_type == 'grpc':
+            self._client = MultiLangClient()
+        else:
+            raise ValueError("unknow client type: {}".format(client_type))
+        self._client.connect(server_endpoints)
+        self._fetch_names = fetch_names
+
+    def _get_input_channel(self):
+        return self._input
+
+    def get_input_ops(self):
+        return self._input_ops
+
+    def set_input_ops(self, ops):
+        if not isinstance(ops, list):
+            ops = [] if ops is None else [ops]
+        self._input_ops = []
+        for op in ops:
+            if not isinstance(op, Op):
+                raise TypeError(
+                    self._log('input op must be Op type, not {}'.format(
+                        type(op))))
+            self._input_ops.append(op)
+
+    def add_input_channel(self, channel):
+        if not isinstance(channel, (ThreadChannel, ProcessChannel)):
+            raise TypeError(
+                self._log('input channel must be Channel type, not {}'.format(
+                    type(channel))))
+        channel.add_consumer(self.name)
+        self._input = channel
+
+    def _get_output_channels(self):
+        return self._outputs
+
+    def add_output_channel(self, channel):
+        if not isinstance(channel, (ThreadChannel, ProcessChannel)):
+            raise TypeError(
+                self._log('output channel must be Channel type, not {}'.format(
+                    type(channel))))
+        channel.add_producer(self.name)
+        self._outputs.append(channel)
+
+    def preprocess(self, input_dicts):
+        # multiple previous Op
+        if len(input_dicts) != 1:
+            raise NotImplementedError(
+                'this Op has multiple previous inputs. Please override this func.'
+            )
+
+        (_, input_dict), = input_dicts.items()
+        return input_dict
+
+    def process(self, feed_dict):
+        err, err_info = ChannelData.check_npdata(feed_dict)
+        if err != 0:
+            raise NotImplementedError(
+                "{} Please override preprocess func.".format(err_info))
+        _LOGGER.debug(self._log('feed_dict: {}'.format(feed_dict)))
+        _LOGGER.debug(self._log('fetch: {}'.format(self._fetch_names)))
+        call_result = self._client.predict(
+            feed=feed_dict, fetch=self._fetch_names)
+        _LOGGER.debug(self._log("get call_result"))
+        return call_result
+
+    def postprocess(self, fetch_dict):
+        return fetch_dict
+
+    def stop(self):
+        self._is_run = False
+
+    def _parse_channeldata(self, channeldata_dict):
+        data_id, error_channeldata = None, None
+        parsed_data = {}
+
+        key = list(channeldata_dict.keys())[0]
+        data_id = channeldata_dict[key].id
+
+        for name, data in channeldata_dict.items():
+            if data.ecode != ChannelDataEcode.OK.value:
+                error_channeldata = data
+                break
+            parsed_data[name] = data.parse()
+        return data_id, error_channeldata, parsed_data
+
+    def _push_to_output_channels(self, data, channels, name=None):
+        if name is None:
+            name = self.name
+        for channel in channels:
+            channel.push(data, name)
+
+    def start_with_process(self, client_type):
+        proces = []
+        for concurrency_idx in range(self.concurrency):
+            p = multiprocessing.Process(
+                target=self._run,
+                args=(concurrency_idx, self._get_input_channel(),
+                      self._get_output_channels(), client_type))
+            p.start()
+            proces.append(p)
+        return proces
+
+    def start_with_thread(self, client_type):
+        threads = []
+        for concurrency_idx in range(self.concurrency):
+            t = threading.Thread(
+                target=self._run,
+                args=(concurrency_idx, self._get_input_channel(),
+                      self._get_output_channels(), client_type))
+            t.start()
+            threads.append(t)
+        return threads
+
+    def load_user_resources(self):
+        pass
+
+    def _run_preprocess(self, parsed_data, data_id, log_func):
+        preped_data, error_channeldata = None, None
+        try:
+            preped_data = self.preprocess(parsed_data)
+        except NotImplementedError as e:
+            # preprocess function not implemented
+            error_info = log_func(e)
+            _LOGGER.error(error_info)
+            error_channeldata = ChannelData(
+                ecode=ChannelDataEcode.NOT_IMPLEMENTED.value,
+                error_info=error_info,
+                data_id=data_id)
+        except TypeError as e:
+            # Error type in channeldata.datatype
+            error_info = log_func(e)
+            _LOGGER.error(error_info)
+            error_channeldata = ChannelData(
+                ecode=ChannelDataEcode.TYPE_ERROR.value,
+                error_info=error_info,
+                data_id=data_id)
+        except Exception as e:
+            error_info = log_func(e)
+            _LOGGER.error(error_info)
+            error_channeldata = ChannelData(
+                ecode=ChannelDataEcode.UNKNOW.value,
+                error_info=error_info,
+                data_id=data_id)
+        return preped_data, error_channeldata
+
+    def _run_process(self, preped_data, data_id, log_func):
+        midped_data, error_channeldata = None, None
+        if self.with_serving:
+            ecode = ChannelDataEcode.OK.value
+            if self._timeout <= 0:
+                try:
+                    midped_data = self.process(preped_data)
+                except Exception as e:
+                    ecode = ChannelDataEcode.UNKNOW.value
+                    error_info = log_func(e)
+                    _LOGGER.error(error_info)
+            else:
+                for i in range(self._retry):
+                    try:
+                        midped_data = func_timeout.func_timeout(
+                            self._timeout, self.process, args=(preped_data, ))
+                    except func_timeout.FunctionTimedOut as e:
+                        if i + 1 >= self._retry:
+                            ecode = ChannelDataEcode.TIMEOUT.value
+                            error_info = log_func(e)
+                            _LOGGER.error(error_info)
+                        else:
+                            _LOGGER.warn(
+                                log_func("timeout, retry({})".format(i + 1)))
+                    except Exception as e:
+                        ecode = ChannelDataEcode.UNKNOW.value
+                        error_info = log_func(e)
+                        _LOGGER.error(error_info)
+                        break
+                    else:
+                        break
+            if ecode != ChannelDataEcode.OK.value:
+                error_channeldata = ChannelData(
+                    ecode=ecode, error_info=error_info, data_id=data_id)
+            elif midped_data is None:
+                # op client return None
+                error_channeldata = ChannelData(
+                    ecode=ChannelDataEcode.CLIENT_ERROR.value,
+                    error_info=log_func(
+                        "predict failed. pls check the server side."),
+                    data_id=data_id)
+        else:
+            midped_data = preped_data
+        return midped_data, error_channeldata
+
+    def _run_postprocess(self, midped_data, data_id, log_func):
+        output_data, error_channeldata = None, None
+        try:
+            postped_data = self.postprocess(midped_data)
+        except Exception as e:
+            error_info = log_func(e)
+            _LOGGER.error(error_info)
+            error_channeldata = ChannelData(
+                ecode=ChannelDataEcode.UNKNOW.value,
+                error_info=error_info,
+                data_id=data_id)
+            return output_data, error_channeldata
+
+        if not isinstance(postped_data, dict):
+            error_info = log_func("output of postprocess funticon must be " \
+                    "dict type, but get {}".format(type(postped_data)))
+            _LOGGER.error(error_info)
+            error_channeldata = ChannelData(
+                ecode=ChannelDataEcode.UNKNOW.value,
+                error_info=error_info,
+                data_id=data_id)
+            return output_data, error_channeldata
+
+        err, _ = ChannelData.check_npdata(postped_data)
+        if err == 0:
+            output_data = ChannelData(
+                ChannelDataType.CHANNEL_NPDATA.value,
+                npdata=postped_data,
+                data_id=data_id)
+        else:
+            output_data = ChannelData(
+                ChannelDataType.DICT.value,
+                dictdata=postped_data,
+                data_id=data_id)
+        return output_data, error_channeldata
+
+    def _run(self, concurrency_idx, input_channel, output_channels,
+             client_type):
+        def get_log_func(op_info_prefix):
+            def log_func(info_str):
+                return "{} {}".format(op_info_prefix, info_str)
+
+            return log_func
+
+        op_info_prefix = "[{}|{}]".format(self.name, concurrency_idx)
+        log = get_log_func(op_info_prefix)
+        tid = threading.current_thread().ident
+
+        # create client based on client_type
+        self.init_client(client_type, self._client_config,
+                         self._server_endpoints, self._fetch_names)
+
+        # load user resources
+        self.load_user_resources()
+
+        self._is_run = True
+        while self._is_run:
+            self._profiler_record("{}-get#{}_0".format(op_info_prefix, tid))
+            channeldata_dict = input_channel.front(self.name)
+            self._profiler_record("{}-get#{}_1".format(op_info_prefix, tid))
+            _LOGGER.debug(log("input_data: {}".format(channeldata_dict)))
+
+            data_id, error_channeldata, parsed_data = self._parse_channeldata(
+                channeldata_dict)
+            # error data in predecessor Op
+            if error_channeldata is not None:
+                self._push_to_output_channels(error_channeldata,
+                                              output_channels)
+                continue
+
+            # preprecess
+            self._profiler_record("{}-prep#{}_0".format(op_info_prefix, tid))
+            preped_data, error_channeldata = self._run_preprocess(parsed_data,
+                                                                  data_id, log)
+            self._profiler_record("{}-prep#{}_1".format(op_info_prefix, tid))
+            if error_channeldata is not None:
+                self._push_to_output_channels(error_channeldata,
+                                              output_channels)
+                continue
+
+            # process
+            self._profiler_record("{}-midp#{}_0".format(op_info_prefix, tid))
+            midped_data, error_channeldata = self._run_process(preped_data,
+                                                               data_id, log)
+            self._profiler_record("{}-midp#{}_1".format(op_info_prefix, tid))
+            if error_channeldata is not None:
+                self._push_to_output_channels(error_channeldata,
+                                              output_channels)
+                continue
+
+            # postprocess
+            self._profiler_record("{}-postp#{}_0".format(op_info_prefix, tid))
+            output_data, error_channeldata = self._run_postprocess(midped_data,
+                                                                   data_id, log)
+            self._profiler_record("{}-postp#{}_1".format(op_info_prefix, tid))
+            if error_channeldata is not None:
+                self._push_to_output_channels(error_channeldata,
+                                              output_channels)
+                continue
+
+            # push data to channel (if run succ)
+            self._profiler_record("{}-push#{}_0".format(op_info_prefix, tid))
+            self._push_to_output_channels(output_data, output_channels)
+            self._profiler_record("{}-push#{}_1".format(op_info_prefix, tid))
+
+    def _log(self, info):
+        return "{} {}".format(self.name, info)
+
+
+class RequestOp(Op):
+    """ RequestOp do not run preprocess, process, postprocess. """
+
+    def __init__(self, concurrency=1):
+        # PipelineService.name = "#G"
+        super(RequestOp, self).__init__(
+            name="#G", input_ops=[], concurrency=concurrency)
+        # load user resources
+        self.load_user_resources()
+
+    def unpack_request_package(self, request):
+        dictdata = {}
+        for idx, key in enumerate(request.key):
+            data = request.value[idx]
+            try:
+                data = eval(data)
+            except Exception as e:
+                pass
+            dictdata[key] = data
+        return dictdata
+
+
+class ResponseOp(Op):
+    """ ResponseOp do not run preprocess, process, postprocess. """
+
+    def __init__(self, input_ops, concurrency=1):
+        super(ResponseOp, self).__init__(
+            name="#R", input_ops=input_ops, concurrency=concurrency)
+        # load user resources
+        self.load_user_resources()
+
+    def pack_response_package(self, channeldata):
+        resp = pipeline_service_pb2.Response()
+        resp.ecode = channeldata.ecode
+        if resp.ecode == ChannelDataEcode.OK.value:
+            if channeldata.datatype == ChannelDataType.CHANNEL_NPDATA.value:
+                feed = channeldata.parse()
+                # ndarray to string:
+                # https://stackoverflow.com/questions/30167538/convert-a-numpy-ndarray-to-stringor-bytes-and-convert-it-back-to-numpy-ndarray
+                for name, var in feed.items():
+                    resp.value.append(var.__repr__())
+                    resp.key.append(name)
+            elif channeldata.datatype == ChannelDataType.DICT.value:
+                feed = channeldata.parse()
+                for name, var in feed.items():
+                    if not isinstance(var, str):
+                        resp.ecode = ChannelDataEcode.TYPE_ERROR.value
+                        resp.error_info = self._log(
+                            "fetch var type must be str({}).".format(
+                                type(var)))
+                        break
+                    resp.value.append(var)
+                    resp.key.append(name)
+            else:
+                resp.ecode = ChannelDataEcode.TYPE_ERROR.value
+                resp.error_info = self._log(
+                    "Error type({}) in datatype.".format(channeldata.datatype))
+                _LOGGER.error(resp.error_info)
+        else:
+            resp.error_info = channeldata.error_info
+        return resp
+
+
+class VirtualOp(Op):
+    ''' For connecting two channels. '''
+
+    def __init__(self, name, concurrency=1):
+        super(VirtualOp, self).__init__(
+            name=name, input_ops=None, concurrency=concurrency)
+        self._virtual_pred_ops = []
+
+    def add_virtual_pred_op(self, op):
+        self._virtual_pred_ops.append(op)
+
+    def add_output_channel(self, channel):
+        if not isinstance(channel, (ThreadChannel, ProcessChannel)):
+            raise TypeError(
+                self._log('output channel must be Channel type, not {}'.format(
+                    type(channel))))
+        for op in self._virtual_pred_ops:
+            channel.add_producer(op.name)
+        self._outputs.append(channel)
+
+    def _run(self, concurrency_idx, input_channel, output_channels,
+             client_type):
+        def get_log_func(op_info_prefix):
+            def log_func(info_str):
+                return "{} {}".format(op_info_prefix, info_str)
+
+            return log_func
+
+        op_info_prefix = "[{}|{}]".format(self.name, concurrency_idx)
+        log = get_log_func(op_info_prefix)
+        tid = threading.current_thread().ident
+
+        self._is_run = True
+        while self._is_run:
+            self._profiler_record("{}-get#{}_0".format(op_info_prefix, tid))
+            channeldata_dict = input_channel.front(self.name)
+            self._profiler_record("{}-get#{}_1".format(op_info_prefix, tid))
+
+            self._profiler_record("{}-push#{}_0".format(op_info_prefix, tid))
+            for name, data in channeldata_dict.items():
+                self._push_to_output_channels(
+                    data, channels=output_channels, name=name)
+            self._profiler_record("{}-push#{}_1".format(op_info_prefix, tid))

python/pipeline/pipeline_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.
+# pylint: disable=doc-string-missing
+import grpc
+import numpy as np
+from numpy import *
+import logging
+import functools
+from .proto import pipeline_service_pb2
+from .proto import pipeline_service_pb2_grpc
+
+_LOGGER = logging.getLogger(__name__)
+
+
+class PipelineClient(object):
+    def __init__(self):
+        self._channel = None
+
+    def connect(self, endpoint):
+        self._channel = grpc.insecure_channel(endpoint)
+        self._stub = pipeline_service_pb2_grpc.PipelineServiceStub(
+            self._channel)
+
+    def _pack_request_package(self, feed_dict):
+        req = pipeline_service_pb2.Request()
+        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):
+                req.value.append(value)
+            elif isinstance(value, list):
+                req.value.append(np.array(value).__repr__())
+            else:
+                raise TypeError("only str and np.ndarray type is supported: {}".
+                                format(type(value)))
+        return req
+
+    def _unpack_response_package(self, resp, fetch):
+        if resp.ecode != 0:
+            return {"ecode": resp.ecode, "error_info": resp.error_info}
+        fetch_map = {"ecode": resp.ecode}
+        for idx, key in enumerate(resp.key):
+            if key not in fetch:
+                continue
+            data = resp.value[idx]
+            try:
+                data = eval(data)
+            except Exception as e:
+                pass
+            fetch_map[key] = data
+        return fetch_map
+
+    def predict(self, feed_dict, fetch, asyn=False):
+        if not isinstance(feed_dict, dict):
+            raise TypeError(
+                "feed must be dict type with format: {name: value}.")
+        if not isinstance(fetch, list):
+            raise TypeError("fetch must be list type with format: [name].")
+        req = self._pack_request_package(feed_dict)
+        if not asyn:
+            resp = self._stub.inference(req)
+            return self._unpack_response_package(resp)
+        else:
+            call_future = self._stub.inference.future(req)
+            return PipelinePredictFuture(
+                call_future,
+                functools.partial(
+                    self._unpack_response_package, fetch=fetch))
+
+
+class PipelinePredictFuture(object):
+    def __init__(self, call_future, callback_func):
+        self.call_future_ = call_future
+        self.callback_func_ = callback_func
+
+    def result(self):
+        resp = self.call_future_.result()
+        return self.callback_func_(resp)

python/pipeline/pipeline_server.py

@@ -12,3 +12,440 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # pylint: disable=doc-string-missing
+import threading
+import multiprocessing
+import multiprocessing.queues
+import sys
+if sys.version_info.major == 2:
+    import Queue
+elif sys.version_info.major == 3:
+    import queue as Queue
+else:
+    raise Exception("Error Python version")
+import os
+from paddle_serving_client import MultiLangClient, Client
+from concurrent import futures
+import numpy as np
+import grpc
+import logging
+import random
+import time
+import func_timeout
+import enum
+import collections
+import copy
+import socket
+from contextlib import closing
+import yaml
+
+from .proto import pipeline_service_pb2
+from .proto import pipeline_service_pb2_grpc
+from .operator import Op, RequestOp, ResponseOp, VirtualOp
+from .channel import ThreadChannel, ProcessChannel, ChannelData, ChannelDataEcode, ChannelDataType
+from .profiler import TimeProfiler
+from .util import NameGenerator
+
+_LOGGER = logging.getLogger(__name__)
+_profiler = TimeProfiler()
+
+
+class PipelineService(pipeline_service_pb2_grpc.PipelineServiceServicer):
+    def __init__(self, in_channel, out_channel, unpack_func, pack_func,
+                 retry=2):
+        super(PipelineService, self).__init__()
+        self.name = "#G"
+        self.set_in_channel(in_channel)
+        self.set_out_channel(out_channel)
+        _LOGGER.debug(self._log(in_channel.debug()))
+        _LOGGER.debug(self._log(out_channel.debug()))
+        #TODO: 
+        #  multi-lock for different clients
+        #  diffenert lock for server and client
+        self._id_lock = threading.Lock()
+        self._cv = threading.Condition()
+        self._globel_resp_dict = {}
+        self._id_counter = 0
+        self._reset_max_id = 1000000000000000000
+        self._retry = retry
+        self._is_run = True
+        self._pack_func = pack_func
+        self._unpack_func = unpack_func
+        self._recive_func = threading.Thread(
+            target=PipelineService._recive_out_channel_func, args=(self, ))
+        self._recive_func.start()
+
+    def _log(self, info_str):
+        return "[{}] {}".format(self.name, info_str)
+
+    def set_in_channel(self, in_channel):
+        if not isinstance(in_channel, (ThreadChannel, ProcessChannel)):
+            raise TypeError(
+                self._log('in_channel must be Channel type, but get {}'.format(
+                    type(in_channel))))
+        in_channel.add_producer(self.name)
+        self._in_channel = in_channel
+
+    def set_out_channel(self, out_channel):
+        if not isinstance(out_channel, (ThreadChannel, ProcessChannel)):
+            raise TypeError(
+                self._log('out_channel must be Channel type, but get {}'.format(
+                    type(out_channel))))
+        out_channel.add_consumer(self.name)
+        self._out_channel = out_channel
+
+    def stop(self):
+        self._is_run = False
+
+    def _recive_out_channel_func(self):
+        while self._is_run:
+            channeldata_dict = self._out_channel.front(self.name)
+            if len(channeldata_dict) != 1:
+                raise Exception("out_channel cannot have multiple input ops")
+            (_, channeldata), = channeldata_dict.items()
+            if not isinstance(channeldata, ChannelData):
+                raise TypeError(
+                    self._log('data must be ChannelData type, but get {}'.
+                              format(type(channeldata))))
+            with self._cv:
+                data_id = channeldata.id
+                self._globel_resp_dict[data_id] = channeldata
+                self._cv.notify_all()
+
+    def _get_next_id(self):
+        with self._id_lock:
+            if self._id_counter >= self._reset_max_id:
+                self._id_counter -= self._reset_max_id
+            self._id_counter += 1
+            return self._id_counter - 1
+
+    def _get_data_in_globel_resp_dict(self, data_id):
+        resp = None
+        with self._cv:
+            while data_id not in self._globel_resp_dict:
+                self._cv.wait()
+            resp = self._globel_resp_dict.pop(data_id)
+            self._cv.notify_all()
+        return resp
+
+    def _pack_data_for_infer(self, request):
+        _LOGGER.debug(self._log('start inferce'))
+        data_id = self._get_next_id()
+        dictdata = None
+        try:
+            dictdata = self._unpack_func(request)
+        except Exception as e:
+            return ChannelData(
+                ecode=ChannelDataEcode.RPC_PACKAGE_ERROR.value,
+                error_info="rpc package error: {}".format(e),
+                data_id=data_id), data_id
+        else:
+            return ChannelData(
+                datatype=ChannelDataType.DICT.value,
+                dictdata=dictdata,
+                data_id=data_id), data_id
+
+    def _pack_data_for_resp(self, channeldata):
+        _LOGGER.debug(self._log('get channeldata'))
+        return self._pack_func(channeldata)
+
+    def inference(self, request, context):
+        _profiler.record("{}-prepack_0".format(self.name))
+        data, data_id = self._pack_data_for_infer(request)
+        _profiler.record("{}-prepack_1".format(self.name))
+
+        resp_channeldata = None
+        for i in range(self._retry):
+            _LOGGER.debug(self._log('push data'))
+            _profiler.record("{}-push_0".format(self.name))
+            self._in_channel.push(data, self.name)
+            _profiler.record("{}-push_1".format(self.name))
+
+            _LOGGER.debug(self._log('wait for infer'))
+            _profiler.record("{}-fetch_0".format(self.name))
+            resp_channeldata = self._get_data_in_globel_resp_dict(data_id)
+            _profiler.record("{}-fetch_1".format(self.name))
+
+            if resp_channeldata.ecode == ChannelDataEcode.OK.value:
+                break
+            if i + 1 < self._retry:
+                _LOGGER.warn("retry({}): {}".format(
+                    i + 1, resp_channeldata.error_info))
+
+        _profiler.record("{}-postpack_0".format(self.name))
+        resp = self._pack_data_for_resp(resp_channeldata)
+        _profiler.record("{}-postpack_1".format(self.name))
+        _profiler.print_profile()
+        return resp
+
+
+class PipelineServer(object):
+    def __init__(self):
+        self._channels = []
+        self._actual_ops = []
+        self._port = None
+        self._worker_num = None
+        self._in_channel = None
+        self._out_channel = None
+        self._response_op = None
+        self._pack_func = None
+        self._unpack_func = None
+
+    def add_channel(self, channel):
+        self._channels.append(channel)
+
+    def gen_desc(self):
+        _LOGGER.info('here will generate desc for PAAS')
+        pass
+
+    def set_response_op(self, response_op):
+        if not isinstance(response_op, Op):
+            raise Exception("response_op must be Op type.")
+        if len(response_op.get_input_ops()) != 1:
+            raise Exception("response_op can only have one previous op.")
+        self._response_op = response_op
+
+    def _topo_sort(self, response_op):
+        if response_op is None:
+            raise Exception("response_op has not been set.")
+
+        def get_use_ops(root):
+            # root: response_op
+            unique_names = set()
+            use_ops = set()
+            succ_ops_of_use_op = {}  # {op_name: succ_ops}
+            que = Queue.Queue()
+            que.put(root)
+            #use_ops.add(root)
+            #unique_names.add(root.name)
+            while que.qsize() != 0:
+                op = que.get()
+                for pred_op in op.get_input_ops():
+                    if pred_op.name not in succ_ops_of_use_op:
+                        succ_ops_of_use_op[pred_op.name] = []
+                    if op != root:
+                        succ_ops_of_use_op[pred_op.name].append(op)
+                    if pred_op not in use_ops:
+                        que.put(pred_op)
+                        use_ops.add(pred_op)
+                        # check the name of op is globally unique
+                        if pred_op.name in unique_names:
+                            raise Exception("the name of Op must be unique: {}".
+                                            format(pred_op.name))
+                        unique_names.add(pred_op.name)
+            return use_ops, succ_ops_of_use_op
+
+        use_ops, out_degree_ops = get_use_ops(response_op)
+        if len(use_ops) <= 1:
+            raise Exception(
+                "Besides RequestOp and ResponseOp, there should be at least one Op in DAG."
+            )
+
+        name2op = {op.name: op for op in use_ops}
+        out_degree_num = {
+            name: len(ops)
+            for name, ops in out_degree_ops.items()
+        }
+        que_idx = 0  # scroll queue 
+        ques = [Queue.Queue() for _ in range(2)]
+        zero_indegree_num = 0
+        for op in use_ops:
+            if len(op.get_input_ops()) == 0:
+                zero_indegree_num += 1
+        if zero_indegree_num != 1:
+            raise Exception("DAG contains multiple input Ops")
+        last_op = response_op.get_input_ops()[0]
+        ques[que_idx].put(last_op)
+
+        # topo sort to get dag_views
+        dag_views = []
+        sorted_op_num = 0
+        while True:
+            que = ques[que_idx]
+            next_que = ques[(que_idx + 1) % 2]
+            dag_view = []
+            while que.qsize() != 0:
+                op = que.get()
+                dag_view.append(op)
+                sorted_op_num += 1
+                for pred_op in op.get_input_ops():
+                    out_degree_num[pred_op.name] -= 1
+                    if out_degree_num[pred_op.name] == 0:
+                        next_que.put(pred_op)
+            dag_views.append(dag_view)
+            if next_que.qsize() == 0:
+                break
+            que_idx = (que_idx + 1) % 2
+        if sorted_op_num < len(use_ops):
+            raise Exception("not legal DAG")
+
+        # create channels and virtual ops
+        def gen_channel(name_gen):
+            channel = None
+            if self._use_multithread:
+                channel = ThreadChannel(name=name_gen.next())
+            else:
+                channel = ProcessChannel(self._manager, name=name_gen.next())
+            return channel
+
+        def gen_virtual_op(name_gen):
+            return VirtualOp(name=name_gen.next())
+
+        virtual_op_name_gen = NameGenerator("vir")
+        channel_name_gen = NameGenerator("chl")
+        virtual_ops = []
+        channels = []
+        input_channel = None
+        actual_view = None
+        dag_views = list(reversed(dag_views))
+        for v_idx, view in enumerate(dag_views):
+            if v_idx + 1 >= len(dag_views):
+                break
+            next_view = dag_views[v_idx + 1]
+            if actual_view is None:
+                actual_view = view
+            actual_next_view = []
+            pred_op_of_next_view_op = {}
+            for op in actual_view:
+                # find actual succ op in next view and create virtual op
+                for succ_op in out_degree_ops[op.name]:
+                    if succ_op in next_view:
+                        if succ_op not in actual_next_view:
+                            actual_next_view.append(succ_op)
+                        if succ_op.name not in pred_op_of_next_view_op:
+                            pred_op_of_next_view_op[succ_op.name] = []
+                        pred_op_of_next_view_op[succ_op.name].append(op)
+                    else:
+                        # create virtual op
+                        virtual_op = gen_virtual_op(virtual_op_name_gen)
+                        virtual_ops.append(virtual_op)
+                        out_degree_ops[virtual_op.name] = [succ_op]
+                        actual_next_view.append(virtual_op)
+                        pred_op_of_next_view_op[virtual_op.name] = [op]
+                        virtual_op.add_virtual_pred_op(op)
+            actual_view = actual_next_view
+            # create channel
+            processed_op = set()
+            for o_idx, op in enumerate(actual_next_view):
+                if op.name in processed_op:
+                    continue
+                channel = gen_channel(channel_name_gen)
+                channels.append(channel)
+                _LOGGER.debug("{} => {}".format(channel.name, op.name))
+                op.add_input_channel(channel)
+                pred_ops = pred_op_of_next_view_op[op.name]
+                if v_idx == 0:
+                    input_channel = channel
+                else:
+                    # if pred_op is virtual op, it will use ancestors as producers to channel
+                    for pred_op in pred_ops:
+                        _LOGGER.debug("{} => {}".format(pred_op.name,
+                                                        channel.name))
+                        pred_op.add_output_channel(channel)
+                processed_op.add(op.name)
+                # find same input op to combine channel
+                for other_op in actual_next_view[o_idx + 1:]:
+                    if other_op.name in processed_op:
+                        continue
+                    other_pred_ops = pred_op_of_next_view_op[other_op.name]
+                    if len(other_pred_ops) != len(pred_ops):
+                        continue
+                    same_flag = True
+                    for pred_op in pred_ops:
+                        if pred_op not in other_pred_ops:
+                            same_flag = False
+                            break
+                    if same_flag:
+                        _LOGGER.debug("{} => {}".format(channel.name,
+                                                        other_op.name))
+                        other_op.add_input_channel(channel)
+                        processed_op.add(other_op.name)
+        output_channel = gen_channel(channel_name_gen)
+        channels.append(output_channel)
+        last_op.add_output_channel(output_channel)
+
+        pack_func, unpack_func = None, None
+        pack_func = self._response_op.pack_response_package
+        self._actual_ops = virtual_ops
+        for op in use_ops:
+            if len(op.get_input_ops()) == 0:
+                unpack_func = op.unpack_request_package
+                continue
+            self._actual_ops.append(op)
+        self._channels = channels
+        for c in channels:
+            _LOGGER.debug(c.debug())
+        return input_channel, output_channel, pack_func, unpack_func
+
+    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):
+        with open(yml_file) as f:
+            yml_config = yaml.load(f.read())
+        self._port = yml_config.get('port', 8080)
+        if not self._port_is_available(self._port):
+            raise SystemExit("Prot {} is already used".format(self._port))
+        self._worker_num = yml_config.get('worker_num', 2)
+
+        self._retry = yml_config.get('retry', 1)
+        self._client_type = yml_config.get('client_type', 'brpc')
+        self._use_multithread = yml_config.get('use_multithread', True)
+        profile = yml_config.get('profile', False)
+
+        if not self._use_multithread:
+            self._manager = multiprocessing.Manager()
+            if profile:
+                raise Exception(
+                    "profile cannot be used in multiprocess version temporarily")
+        _profiler.enable(profile)
+
+        input_channel, output_channel, self._pack_func, self._unpack_func = self._topo_sort(
+            self._response_op)
+        self._in_channel = input_channel
+        self._out_channel = output_channel
+        for op in self._actual_ops:
+            if op.with_serving:
+                self.prepare_serving(op)
+        self.gen_desc()
+
+    def _run_ops(self):
+        threads_or_proces = []
+        for op in self._actual_ops:
+            op.init_profiler(_profiler)
+            if self._use_multithread:
+                threads_or_proces.extend(
+                    op.start_with_thread(self._client_type))
+            else:
+                threads_or_proces.extend(
+                    op.start_with_process(self._client_type))
+        return threads_or_proces
+
+    def _stop_all(self, service):
+        service.stop()
+        for op in self._actual_ops:
+            op.stop()
+        for chl in self._channels:
+            chl.stop()
+
+    def run_server(self):
+        op_threads_or_proces = self._run_ops()
+        service = PipelineService(self._in_channel, self._out_channel,
+                                  self._unpack_func, self._pack_func,
+                                  self._retry)
+        server = grpc.server(
+            futures.ThreadPoolExecutor(max_workers=self._worker_num))
+        pipeline_service_pb2_grpc.add_PipelineServiceServicer_to_server(service,
+                                                                        server)
+        server.add_insecure_port('[::]:{}'.format(self._port))
+        server.start()
+        server.wait_for_termination()
+        self._stop_all()  # TODO
+        for x in op_threads_or_proces:
+            x.join()
+
+    def prepare_serving(self, op):
+        # run a server (not in PyServing)
+        _LOGGER.info("run a server (not in PyServing)")

python/pipeline/profiler.py

@@ -12,3 +12,54 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # pylint: disable=doc-string-missing
+
+import os
+import sys
+import logging
+if sys.version_info.major == 2:
+    import Queue
+elif sys.version_info.major == 3:
+    import queue as Queue
+else:
+    raise Exception("Error Python version")
+import time
+
+_LOGGER = logging.getLogger(__name__)
+
+
+class TimeProfiler(object):
+    def __init__(self):
+        self._pid = os.getpid()
+        self._print_head = 'PROFILE\tpid:{}\t'.format(self._pid)
+        self._time_record = Queue.Queue()
+        self._enable = False
+
+    def enable(self, enable):
+        self._enable = enable
+
+    def record(self, name_with_tag):
+        if self._enable is False:
+            return
+        name_with_tag = name_with_tag.split("_")
+        tag = name_with_tag[-1]
+        name = '_'.join(name_with_tag[:-1])
+        self._time_record.put((name, tag, int(round(time.time() * 1000000))))
+
+    def print_profile(self):
+        if self._enable is False:
+            return
+        print_str = self._print_head
+        tmp = {}
+        while not self._time_record.empty():
+            name, tag, timestamp = self._time_record.get()
+            if name in tmp:
+                ptag, ptimestamp = tmp.pop(name)
+                print_str += "{}_{}:{} ".format(name, ptag, ptimestamp)
+                print_str += "{}_{}:{} ".format(name, tag, timestamp)
+            else:
+                tmp[name] = (tag, timestamp)
+        print_str += "\n"
+        sys.stderr.write(print_str)
+        for name, item in tmp.items():
+            tag, timestamp = item
+            self._time_record.put((name, tag, timestamp))

python/pipeline/proto/__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/proto/pipeline_service.proto

+// Copyright (c) 2019 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 = "proto2";
+package baidu.paddle_serving.pipeline_serving;
+
+message Request {
+  repeated string key = 1;
+  repeated string value = 2;
+};
+
+message Response {
+  repeated string key = 1;
+  repeated string value = 2;
+  required int32 ecode = 3;
+  optional string error_info = 4;
+};
+
+service PipelineService {
+  rpc inference(Request) returns (Response) {}
+};

python/pipeline/proto/run_codegen.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.
+
+# Copyright 2015 gRPC authors.
+#
+# 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.
+"""Runs protoc with the gRPC plugin to generate messages and gRPC stubs."""
+
+from grpc_tools import protoc
+
+protoc.main((
+    '',
+    '-I.',
+    '--python_out=.',
+    '--grpc_python_out=.',
+    'pipeline_service.proto', ))

python/pipeline/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.
+
+import sys
+
+
+class NameGenerator(object):
+    def __init__(self, prefix):
+        self._idx = -1
+        self._prefix = prefix
+
+    def next(self):
+        self._idx += 1
+        return "{}{}".format(self._prefix, self._idx)

python/requirements.txt

 numpy>=1.12, <=1.16.4 ; python_version<"3.5"
 grpcio-tools>=1.28.1
 grpcio>=1.28.1
+func-timeout>=4.3.5

python/setup.py.client.in

@@ -65,11 +65,14 @@ REQUIRED_PACKAGES = [
 if not find_package("paddlepaddle") and not find_package("paddlepaddle-gpu"):
     REQUIRED_PACKAGES.append("paddlepaddle")
 
+
 packages=['paddle_serving_client',
           'paddle_serving_client.proto',
           'paddle_serving_client.io',
-	  'paddle_serving_client.metric',
-	  'paddle_serving_client.utils',]
+    	  '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/*'],}
 package_dir={'paddle_serving_client':
              '${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_client',
@@ -77,10 +80,14 @@ package_dir={'paddle_serving_client':
              '${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_client/proto',
              'paddle_serving_client.io':
              '${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_client/io',
-	     'paddle_serving_client.metric':
-	     '${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_client/metric',
-	     'paddle_serving_client.utils':
-	     '${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_client/utils',}
+	         'paddle_serving_client.metric':
+	         '${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_client/metric',
+	         'paddle_serving_client.utils':
+	         '${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_client/utils',
+             'paddle_serving_client.pipeline':
+             '${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_client/pipeline',
+             'paddle_serving_client.pipeline.proto':
+             '${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_client/pipeline/proto'}
 
 setup(
     name='paddle-serving-client',

python/setup.py.server.in

@@ -42,12 +42,18 @@ REQUIRED_PACKAGES = [
 ]
 
 packages=['paddle_serving_server',
-          'paddle_serving_server.proto']
+          'paddle_serving_server.proto',
+          'paddle_serving_server.pipeline',
+          'paddle_serving_server.pipeline.proto']
 
 package_dir={'paddle_serving_server':
              '${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server',
              'paddle_serving_server.proto':
-             '${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server/proto'}
+             '${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server/proto',
+             'paddle_serving_server.pipeline':
+             '${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server/pipeline',
+             'paddle_serving_server.pipeline.proto':
+             '${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server/pipeline/proto'}
 
 setup(
     name='paddle-serving-server',

python/setup.py.server_gpu.in

@@ -43,12 +43,18 @@ REQUIRED_PACKAGES = [
 
 
 packages=['paddle_serving_server_gpu',
-          'paddle_serving_server_gpu.proto']
+          'paddle_serving_server_gpu.proto',
+          'paddle_serving_server_gpu.pipeline',
+          'paddle_serving_server_gpu.pipeline.proto']
 
 package_dir={'paddle_serving_server_gpu':
              '${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server_gpu',
              'paddle_serving_server_gpu.proto':
-             '${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server_gpu/proto'}
+             '${PADDLE_SERVING_BINARY_DIR}/python/paddle_serving_server_gpu/proto',
+             'paddle_serving_server_gpu.pipeline':
+             '${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'}
 
 setup(
     name='paddle-serving-server-gpu',