Merge branch 'develop' into doc-fix

doc/PIPELINE_SERVING.md

+# Pipeline Serving
+
+([简体中文](PIPELINE_SERVING_CN.md)|English)
+
+
+Paddle Serving is usually used for the deployment of single model, but the end-to-end deep learning model can not solve all the problems at present. Usually, it is necessary to use multiple deep learning models to solve practical problems.
+
+Paddle Serving provides a user-friendly programming framework for multi-model composite services, Pipeline Serving, which aims to reduce the threshold of programming, improve resource utilization (especially GPU), and improve the prediction efficiency.
+
+## Architecture Design
+
+The Server side is built based on gRPC and graph execution engine. The relationship between them is shown in the following figure.
+
+<center>
+<img src='pipeline_serving-image1.png' height = "250" align="middle"/>
+</center>
+### Graph Execution Engine
+
+The graph execution engine consists of OPs and Channels, and the connected OPs share one Channel.
+
+- Channel can be understood as a buffer queue. Each OP accepts only one Channel input and multiply Channel outputs (each output is the same); a Channel can contain outputs from multiple OPs, and data from the same Channel can be used as input for multiple OPs.
+- Users only need to define relationships between OPs. Graph engine will analyze the dependencies of the entire graph and declaring Channels at the compile time.
+- After Request data enters the graph execution engine service, the graph engine will generator an Request ID, and Reponse is returned through corresponding Request ID.
+- For cases where large data needs to be transferred between OPs, consider RAM DB external memory for global storage and data transfer by passing index keys in Channel.
+
+<center>
+<img src='pipeline_serving-image2.png' height = "300" align="middle"/>
+</center>
+
+
+### OP Design
+
+- The default function of a single OP is to access a single Paddle Serving Service based on the input Channel data and put the result into the output Channel.
+- OP supports user customization, including preprocess, process, postprocess functions that can be inherited and implemented by the user.
+- OP can set the number of concurrencies to increase the number of concurrencies processed.
+- OP can be started by a thread or process.
+
+### Channel Design
+
+- Channel is the data structure for sharing data between OPs, responsible for sharing data or sharing data status information.
+- Outputs from multiple OPs can be stored in the same Channel, and data from the same Channel can be used by multiple OPs.
+- The following illustration shows the design of Channel in the graph execution engine, using input buffer and output buffer to align data between multiple OP inputs and multiple OP outputs, with a queue in the middle to buffer.
+
+<center>
+<img src='pipeline_serving-image3.png' height = "500" align="middle"/>
+</center>
+
+
+### Extreme Case Consideration
+
+- Request timeout
+
+  The entire graph execution engine may time out at every step. The graph execution engine controls the time out by setting `timeout` value. Requests that time out at any step will return a timeout response.
+
+- Channel stores too much data
+
+  Channels may store too much data, causing copy time to be too high. Graph execution engines can store OP calculation results in external memory, such as high-speed memory KV systems.
+
+- Whether input buffers and output buffers in Channel will increase indefinitely
+
+  - It will not increase indefinitely. The input to the entire graph execution engine is placed inside a Channel's internal queue, directly acting as a traffic control buffer queue for the entire service.
+  - For input buffer, adjust the number of concurrencies of OP1 and OP2 according to the amount of computation, so that the number of input buffers from each input OP is relatively balanced.
+  - For output buffer, you can use a similar process as input buffer, which adjusts the concurrency of OP3 and OP4 to control the buffer length of output buffer.
+  - Note: The length of the input buffer depends on the speed at which each item in the internal queue is ready, and the length of the output buffer depends on the speed at which downstream OPs obtain data from the output buffer.
+
+## Detailed Design
+
+### User Interface Design
+
+#### 1. General OP Definition
+
+As the basic unit of graph execution engine, the general OP constructor is as follows:
+
+```python
+def __init__(name=None,
+             input_ops=[],
+             server_endpoints=[],
+             fetch_list=[],
+             client_config=None,
+             concurrency=1,
+             timeout=-1,
+             retry=1)
+```
+
+The meaning of each parameter is as follows:
+
+|    Parameter     |                           Meaning                            |
+| :--------------: | :----------------------------------------------------------: |
+|       name       | (str) String used to identify the OP type, which must be globally unique. |
+|    input_ops     |     (list) A list of all previous OPs of the current Op.     |
+| server_endpoints | (list) List of endpoints for remote Paddle Serving Service. If this parameter is not set, the OP will not access the remote Paddle Serving Service, that is, the process operation will not be performed. |
+|    fetch_list    | (list) List of fetch variable names for remote Paddle Serving Service. |
+|  client_config   | (str) The path of the client configuration file corresponding to the Paddle Serving Service. |
+|   concurrency    |             (int) The number of concurrent OPs.              |
+|     timeout      | (int) The timeout time of the process operation, in seconds. If the value is less than zero, no timeout is considered. |
+|      retry       | (int) Timeout number of retries. When the value is 1, no retries are made. |
+
+#### 2. General OP Secondary Development Interface
+
+|             Interface or Variable              |                           Explain                            |
+| :--------------------------------------------: | :----------------------------------------------------------: |
+|       def preprocess(self, input_dicts)        | Process the data obtained from the channel, and the processed data will be used as the input of the **process** function. |
+|          def process(self, feed_dict)          | The RPC prediction process is based on the Paddle Serving Client, and the processed data will be used as the input of the **postprocess** function. |
+| def postprocess(self, input_dicts, fetch_dict) | After processing the prediction results, the processed data will be put into the subsequent Channel to be obtained by the subsequent OP. |
+|               def init_op(self)                |      Used to load resources (such as word dictionary).       |
+|              self.concurrency_idx              | Concurrency index of current thread / process (different kinds of OP are calculated separately). |
+
+In a running cycle, OP will execute three operations: preprocess, process, and postprocess (when the `server_endpoints` parameter is not set, the process operation is not executed). Users can rewrite these three functions. The default implementation is as follows:
+
+```python
+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))
+  call_result = self.client.predict(
+    feed=feed_dict, fetch=self._fetch_names)
+  return call_result
+
+def postprocess(self, input_dicts, fetch_dict):
+  return fetch_dict
+```
+
+The parameter of **preprocess** is the data `input_dicts` in the previous Channel. This variable is a dictionary with the name of the previous OP as key and the output of the corresponding OP as value.
+
+The parameter of **process** is the input variable `fetch_dict` (the return value of the preprocess function) of the Paddle Serving Client prediction interface. This variable is a dictionary with feed_name as the key and the data in the ndarray format as the value.
+
+The parameters of **postprocess** are `input_dicts` and `fetch_dict`. `input_dicts` is consistent with the parameter of preprocess, and `fetch_dict` is the return value of the process function (if process is not executed, this value is the return value of preprocess).
+
+Users can also rewrite the **init_op** function to load some custom resources (such as word dictionary). The default implementation is as follows:
+
+```python
+def init_op(self):
+  pass
+```
+
+It should be noted that in the threaded version of OP, each OP will only call this function once, so the loaded resources must be thread safe.
+
+#### 3. RequestOp Definition
+
+RequestOp is used to process RPC data received by Pipeline Server, and the processed data will be added to the graph execution engine. Its constructor is as follows:
+
+```python
+def __init__(self)
+```
+
+#### 4. RequestOp Secondary Development Interface
+
+|           Interface or Variable           |                           Explain                            |
+| :---------------------------------------: | :----------------------------------------------------------: |
+|             def init_op(self)             | It is used to load resources (such as dictionaries), and is consistent with general OP. |
+| def unpack_request_package(self, request) |                  Process received RPC data.                  |
+
+The default implementation of **unpack_request_package** is to make the key and value in RPC request into a dictionary:
+
+```python
+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
+```
+
+The return value is required to be a dictionary type.
+
+#### 5. ResponseOp Definition
+
+ResponseOp is used to process the prediction results of the graph execution engine. The processed data will be used as the RPC return value of Pipeline Server. Its constructor is as follows:
+
+```python
+def __init__(self, input_ops)
+```
+
+`input_ops` is the last OP of graph execution engine. Users can construct different DAGs by setting different `input_ops` without modifying the topology of OPs.
+
+#### 6. ResponseOp Secondary Development Interface
+
+|            Interface or Variable             |                           Explain                            |
+| :------------------------------------------: | :----------------------------------------------------------: |
+|              def init_op(self)               | It is used to load resources (such as dictionaries), and is consistent with general OP. |
+| def pack_response_package(self, channeldata) | Process the prediction results of the graph execution engine as the return of RPC. |
+
+The default implementation of **pack_response_package** is to convert the dictionary of prediction results into key and value in RPC response:
+
+```python
+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()
+      np.set_printoptions(threshold=np.nan)
+      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))
+  else:
+    resp.error_info = channeldata.error_info
+  return resp
+```
+
+#### 7. PipelineServer Definition
+
+The definition of PipelineServer is relatively simple, as follows:
+
+```python
+server = PipelineServer()
+server.set_response_op(response_op)
+server.prepare_server(config_yml_path)
+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
+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
+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
+    retry: 1  # The number of times DAG executor retries after failure. The default value is 1, that is, no retrying
+    use_profile: false  # Whether to print the log on the server side. The default is false
+```
+
+
+
+## Example
+
+Here, we build a simple imdb model enable example to show how to use Pipeline Serving. The relevant code can be found in the `python/examples/pipeline/imdb_model_ensemble` folder. The Server-side structure in the example is shown in the following figure:
+
+
+
+<center>
+<img src='pipeline_serving-image4.png' height = "200" align="middle"/>
+</center>
+
+
+### Get the model file and start the Paddle Serving Service
+
+```shell
+cd python/examples/pipeline/imdb_model_ensemble
+sh get_data.sh
+python -m paddle_serving_server.serve --model imdb_cnn_model --port 9292 &> cnn.log &
+python -m paddle_serving_server.serve --model imdb_bow_model --port 9393 &> bow.log &
+```
+
+### Start PipelineServer
+
+Run the following code
+
+```python
+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
+
+logging.basicConfig(level=logging.DEBUG)
+
+_LOGGER = logging.getLogger()
+
+
+class ImdbRequestOp(RequestOp):
+    def init_op(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
+
+
+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=5,
+    timeout=-1,
+    retry=1)
+
+# 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()
+```
+
+### Perform prediction through PipelineClient
+
+```python
+from paddle_serving_client.pipeline import PipelineClient
+import numpy as np
+
+client = PipelineClient()
+client.connect(['127.0.0.1:18080'])
+
+words = 'i am very sad | 0'
+
+futures = []
+for i in range(3):
+    futures.append(
+        client.predict(
+            feed_dict={"words": words},
+            fetch=["prediction"],
+            asyn=True))
+
+for f in futures:
+    res = f.result()
+    if res["ecode"] != 0:
+        print(res)
+        exit(1)
+```
+
+
+
+## How to optimize through 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.
+
+### Output profile information on server side
+
+The server is controlled by the `use_profile` field in yaml:
+
+```yaml
+dag:
+    use_profile: true
+```
+
+After the function is enabled, the server will print the corresponding log information to the standard output in the process of prediction. In order to show the time consumption of each stage more intuitively, scripts are provided for further analysis and processing of log files.
+
+The output of the server is first saved to a file. Taking profile as an example, the script converts the time monitoring information in the log into JSON format and saves it to the trace file. The trace file can be visualized through the tracing function of Chrome browser.
+
+```shell
+python timeline_trace.py profile trace
+```
+
+Specific operation: open Chrome browser, input in the address bar `chrome://tracing/` , jump to the tracing page, click the load button, open the saved trace file, and then visualize the time information of each stage of the prediction service.
+
+### Output profile information on client side
+
+The profile function can be enabled by setting `profile=True` in the `predict` interface on the client side.
+
+After the function is enabled, the client will print the log information corresponding to the prediction to the standard output during the prediction process, and the subsequent analysis and processing are the same as that of the server.

doc/PIPELINE_SERVING_CN.md

+# Pipeline Serving
+
+(简体中文|[English](PIPELINE_SERVING.md))
+
+Paddle Serving 通常用于单模型的一键部署，但端到端的深度学习模型当前还不能解决所有问题，多个深度学习模型配合起来使用还是解决现实问题的常规手段。
+
+Paddle Serving 提供了用户友好的多模型组合服务编程框架，Pipeline Serving，旨在降低编程门槛，提高资源使用率（尤其是GPU设备），提升整体的预估效率。
+
+## 整体架构设计
+
+Server端基于 gRPC 和图执行引擎构建，两者的关系如下图所示。
+
+<center>
+<img src='pipeline_serving-image1.png' height = "250" align="middle"/>
+</center>
+
+### 图执行引擎
+
+图执行引擎由 OP 和 Channel 构成，相连接的 OP 之间会共享一个 Channel。
+
+- Channel 可以理解为一个缓冲队列。每个 OP 只接受一个 Channel 的输入和多个 Channel 的输出（每个输出相同）；一个 Channel 可以包含来自多个 OP 的输出，同一个 Channel 的数据可以作为多个 OP 的输入Channel
+- 用户只需要定义 OP 间的关系，在编译期图引擎负责分析整个图的依赖关系，并声明Channel
+- Request 进入图执行引擎服务后会产生一个 Request Id，Reponse 会通过 Request Id 进行对应的返回
+- 对于 OP 之间需要传输过大数据的情况，可以考虑 RAM DB 外存进行全局存储，通过在 Channel 中传递索引的 Key 来进行数据传输
+
+<center>
+<img src='pipeline_serving-image2.png' height = "300" align="middle"/>
+</center>
+
+
+### OP的设计
+
+- 单个OP默认的功能是根据输入的 Channel 数据，访问一个 Paddle Serving 的单模型服务，并将结果存在输出的 Channel
+- 单个 OP 可以支持用户自定义，包括 preprocess，process，postprocess 三个函数都可以由用户继承和实现
+- 单个 OP 可以控制并发数，从而增加处理并发数
+- OP 可以由线程或进程启动
+
+### Channel的设计
+
+- Channel 是 OP 之间共享数据的数据结构，负责共享数据或者共享数据状态信息
+- Channel 可以支持多个OP的输出存储在同一个 Channel，同一个 Channel 中的数据可以被多个 OP 使用
+- 下图为图执行引擎中 Channel 的设计，采用 input buffer 和 output buffer 进行多 OP 输入或多 OP 输出的数据对齐，中间采用一个 Queue 进行缓冲
+
+<center>
+<img src='pipeline_serving-image3.png' height = "500" align="middle"/>
+</center>
+
+
+### 极端情况的考虑
+
+- 请求超时的处理
+
+  整个图执行引擎每一步都有可能发生超时，图执行引擎里面通过设置 timeout 值来控制，任何环节超时的请求都会返回超时响应。
+
+- Channel 存储的数据过大
+
+  Channel 中可能会存储过大的数据，导致拷贝等耗时过高，图执行引擎里面可以通过将 OP 计算结果数据存储到外存，如高速的内存 KV 系统
+
+- Channel 设计中的 input buffer 和 output buffer 是否会无限增加
+
+  - 不会。整个图执行引擎的输入会放到一个 Channel 的 internal queue 里面，直接作为整个服务的流量控制缓冲队列
+  - 对于 input buffer，根据计算量的情况调整 OP1 和 OP2 的并发数，使得 input buffer 来自各个输入 OP 的数量相对平衡
+  - 对于 output buffer，可以采用和 input buffer 类似的处理方法，即调整 OP3 和 OP4 的并发数，使得 output buffer 的缓冲长度得到控制
+  - 注：input buffer 的长度取决于 internal queue 中每个 item 完全 ready 的速度，output buffer 的长度取决于下游 OP 从 output buffer 获取数据的速度
+
+## 详细设计
+
+### 用户接口设计
+
+#### 1. 普通 OP 定义
+
+普通 OP 作为图执行引擎中的基本单元，其构造函数如下：
+
+```python
+def __init__(name=None,
+             input_ops=[],
+             server_endpoints=[],
+             fetch_list=[],
+             client_config=None,
+             concurrency=1,
+             timeout=-1,
+             retry=1)
+```
+
+各参数含义如下
+
+|      参数名      |                             含义                             |
+| :--------------: | :----------------------------------------------------------: |
+|       name       |    （str）用于标识 OP 类型的字符串，该字段必须全局唯一。     |
+|    input_ops     |            （list）当前 OP 的所有前继 OP 的列表。            |
+| server_endpoints | （list）远程 Paddle Serving Service 的 endpoints 列表。如果不设置该参数，则不访问远程 Paddle Serving Service，即 不会执行 process 操作。 |
+|    fetch_list    |     （list）远程 Paddle Serving Service 的 fetch 列表。      |
+|  client_config   | （str）Paddle Serving Service 对应的 Client 端配置文件路径。 |
+|   concurrency    |                     （int）OP 的并发数。                     |
+|     timeout      | （int）process 操作的超时时间，单位为秒。若该值小于零，则视作不超时。 |
+|      retry       |       （int）超时重试次数。当该值为 1 时，不进行重试。       |
+
+#### 2. 普通 OP二次开发接口
+
+|                   变量或接口                   |                             说明                             |
+| :--------------------------------------------: | :----------------------------------------------------------: |
+|       def preprocess(self, input_dicts)        | 对从 Channel 中获取的数据进行处理，处理完的数据将作为 **process** 函数的输入。 |
+|          def process(self, feed_dict)          | 基于 Paddle Serving Client 进行 RPC 预测，处理完的数据将作为 **postprocess** 函数的输入。 |
+| def postprocess(self, input_dicts, fetch_dict) | 处理预测结果，处理完的数据将被放入后继 Channel 中，以被后继 OP 获取。 |
+|               def init_op(self)                |                  用于加载资源（如字典等）。                  |
+|              self.concurrency_idx              |   当前线程（进程）的并发数索引（不同种类的 OP 单独计算）。   |
+
+OP 在一个运行周期中会依次执行 preprocess，process，postprocess 三个操作（当不设置 `server_endpoints` 参数时，不执行 process 操作），用户可以对这三个函数进行重写，默认实现如下：
+
+```python
+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))
+  call_result = self.client.predict(
+    feed=feed_dict, fetch=self._fetch_names)
+  return call_result
+
+def postprocess(self, input_dicts, fetch_dict):
+  return fetch_dict
+```
+
+
+**preprocess** 的参数是前继 Channel 中的数据 `input_dicts`，该变量是一个以前继 OP 的 name 为 Key，对应 OP 的输出为 Value 的字典。
+
+**process** 的参数是 Paddle Serving Client 预测接口的输入变量 `fetch_dict`（preprocess 函数的返回值），该变量是一个以 feed_name 为 Key，对应 ndarray 格式的数据为 Value 的字典。
+
+**postprocess** 的参数是 `input_dicts` 和 `fetch_dict`，`input_dicts` 与 preprocess 的参数一致，`fetch_dict` 是 process 函数的返回值（如果没有执行 process ，则该值为 preprocess 的返回值）。
+
+用户还可以对 **init_op** 函数进行重写，已加载自定义的一些资源（比如字典等），默认实现如下：
+
+```python
+def init_op(self):
+  pass
+```
+
+需要注意的是，在线程版 OP 中，每个 OP 只会调用一次该函数，故加载的资源必须要求是线程安全的。
+
+#### 3. RequestOp 定义
+
+RequestOp 用于处理 Pipeline Server 接收到的 RPC 数据，处理后的数据将会被加入到图执行引擎中。其构造函数如下：
+
+```python
+def __init__(self)
+```
+
+#### 4. RequestOp 二次开发接口
+
+|                变量或接口                 |                    说明                    |
+| :---------------------------------------: | :----------------------------------------: |
+|             def init_op(self)             | 用于加载资源（如字典等），与普通 OP 一致。 |
+| def unpack_request_package(self, request) |          处理接收到的 RPC 数据。           |
+
+**unpack_request_package** 的默认实现是将 RPC request 中的 key 和 value 做成字典：
+
+```python
+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
+```
+
+要求返回值是一个字典类型。
+
+#### 5. ResponseOp 定义
+
+ResponseOp 用于处理图执行引擎的预测结果，处理后的数据将会作为 Pipeline Server 的RPC 返回值，其构造函数如下：
+
+```python
+def __init__(self, input_ops)
+```
+
+其中，`input_ops` 是图执行引擎的最后一个 OP，用户可以通过设置不同的 `input_ops` 以在不修改 OP 的拓扑关系下构造不同的 DAG。
+
+#### 6. ResponseOp 二次开发接口
+
+|                  变量或接口                  |                    说明                     |
+| :------------------------------------------: | :-----------------------------------------: |
+|              def init_op(self)               | 用于加载资源（如字典等），与普通 OP 一致。  |
+| def pack_response_package(self, channeldata) | 处理图执行引擎的预测结果，作为 RPC 的返回。 |
+
+**pack_response_package** 的默认实现是将预测结果的字典转化为 RPC response 中的 key 和 value：
+
+```python
+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()
+      np.set_printoptions(threshold=np.nan)
+      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))
+  else:
+    resp.error_info = channeldata.error_info
+  return resp
+```
+
+#### 7. PipelineServer定义
+
+PipelineServer 的定义比较简单，如下所示：
+
+```python
+server = PipelineServer()
+server.set_response_op(response_op)
+server.prepare_server(config_yml_path)
+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
+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
+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
+    retry: 1  # DAG Executor 在失败后重试次数，默认为 1，即不重试
+    use_profile: false  # 是否在 Server 端打印日志，默认为 false
+```
+
+
+
+## 例子
+
+这里通过搭建简单的 imdb model ensemble 例子来展示如何使用 Pipeline Serving，相关代码在 `python/examples/pipeline/imdb_model_ensemble` 文件夹下可以找到，例子中的 Server 端结构如下图所示：
+
+
+
+<center>
+<img src='pipeline_serving-image4.png' height = "200" align="middle"/>
+</center>
+
+
+### 获取模型文件并启动 Paddle Serving Service
+
+```shell
+cd python/examples/pipeline/imdb_model_ensemble
+sh get_data.sh
+python -m paddle_serving_server.serve --model imdb_cnn_model --port 9292 &> cnn.log &
+python -m paddle_serving_server.serve --model imdb_bow_model --port 9393 &> bow.log &
+```
+
+### 启动 PipelineServer
+
+运行下面代码
+
+```python
+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
+
+logging.basicConfig(level=logging.DEBUG)
+
+_LOGGER = logging.getLogger()
+
+
+class ImdbRequestOp(RequestOp):
+    def init_op(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
+
+
+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=5,
+    timeout=-1,
+    retry=1)
+
+# 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()
+```
+
+### 通过 PipelineClient 执行预测
+
+```python
+from paddle_serving_client.pipeline import PipelineClient
+import numpy as np
+
+client = PipelineClient()
+client.connect(['127.0.0.1:18080'])
+
+words = 'i am very sad | 0'
+
+futures = []
+for i in range(3):
+    futures.append(
+        client.predict(
+            feed_dict={"words": words},
+            fetch=["prediction"],
+            asyn=True))
+
+for f in futures:
+    res = f.result()
+    if res["ecode"] != 0:
+        print(res)
+        exit(1)
+```
+
+
+
+## 如何通过 Timeline 工具进行优化
+
+为了更好地对性能进行优化，PipelineServing 提供了 Timeline 工具，对整个服务的各个阶段时间进行打点。
+
+### 在 Server 端输出 Profile 信息
+
+Server 端用 yaml 中的 `use_profile` 字段进行控制：
+
+```yaml
+dag:
+    use_profile: true
+```
+
+开启该功能后，Server 端在预测的过程中会将对应的日志信息打印到标准输出，为了更直观地展现各阶段的耗时，提供脚本对日志文件做进一步的分析处理。
+
+使用时先将 Server 的输出保存到文件，以 profile 为例，脚本将日志中的时间打点信息转换成 json 格式保存到trace 文件，trace 文件可以通过 chrome 浏览器的 tracing 功能进行可视化。
+
+```shell
+python timeline_trace.py profile trace
+```
+
+具体操作：打开 chrome 浏览器，在地址栏输入 chrome://tracing/ ，跳转至 tracing 页面，点击 load 按钮，打开保存的 trace 文件，即可将预测服务的各阶段时间信息可视化。
+
+### 在 Client 端输出 Profile 信息
+
+Client 端在 `predict` 接口设置 `profile=True`，即可开启 Profile 功能。
+
+开启该功能后，Client 端在预测的过程中会将该次预测对应的日志信息打印到标准输出，后续分析处理同 Server。

python/examples/pipeline/imdb_model_ensemble/analyse.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.pipeline import Analyst
+import json
+import logging
+import sys
+
+logging.basicConfig(level=logging.INFO)
+
+if __name__ == "__main__":
+    if len(sys.argv) < 3:
+        print("Usage: python analyse.py <log_filename> <trace_filename>")
+        exit(1)
+    log_filename = sys.argv[1]
+    trace_filename = sys.argv[2]
+    analyst = Analyst(log_filename)
+    analyst.save_trace(trace_filename)
+    op_analyst = analyst.get_op_analyst()
+    op_concurrency = op_analyst.concurrency_analysis("analyse.yaml")
+    print(json.dumps(op_concurrency, indent=2, separators=(',', ':')))

python/examples/pipeline/imdb_model_ensemble/analyse.yaml

+bow:
+    midp: 0
+cnn:
+    midp: 1

python/examples/pipeline/imdb_model_ensemble/config.yml

-use_multithread: true
-client_type: brpc
-retry: 1
-profile: false
-prot: 8080
-worker_num: 2
+port: 18080
+worker_num: 1
+build_dag_each_worker: false
+dag:
+    is_thread_op: true
+    client_type: brpc
+    retry: 1
+    use_profile: false

python/examples/pipeline/imdb_model_ensemble/test_pipeline_client.py

@@ -13,18 +13,19 @@
 # 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)
+client.connect(['127.0.0.1:18080'])
 
 words = 'i am very sad | 0'
 
-for i in range(1):
-    fetch_map = lp_wrapper(feed_dict={"words": words}, fetch=["prediction"])
-    print(fetch_map)
+futures = []
+for i in range(100):
+    futures.append(
+        client.predict(
+            feed_dict={"words": words}, fetch=["prediction"], asyn=True))
 
-#lp.print_stats()
+for f in futures:
+    res = f.result()
+    if res["ecode"] != 0:
+        print("predict failed: {}".format(res))

python/examples/pipeline/imdb_model_ensemble/test_pipeline_server.py

@@ -21,16 +21,13 @@ import numpy as np
 import logging
 from paddle_serving_app.reader import IMDBDataset
 
-_LOGGER = logging.getLogger(__name__)
+logging.basicConfig(level=logging.DEBUG)
 
-logging.basicConfig(
-    format='%(asctime)s %(levelname)-8s [%(filename)s:%(lineno)d] %(message)s',
-    datefmt='%Y-%m-%d %H:%M',
-    level=logging.DEBUG)
+_LOGGER = logging.getLogger()
 
 
 class ImdbRequestOp(RequestOp):
-    def load_user_resources(self):
+    def init_op(self):
         self.imdb_dataset = IMDBDataset()
         self.imdb_dataset.load_resource('imdb.vocab')
 
@@ -91,7 +88,7 @@ cnn_op = Op(name="cnn",
 combine_op = CombineOp(
     name="combine",
     input_ops=[bow_op, cnn_op],
-    concurrency=1,
+    concurrency=5,
     timeout=-1,
     retry=1)
 

python/examples/util/timeline_trace.py

@@ -43,6 +43,8 @@ if __name__ == "__main__":
         for line in f.readlines():
             line = line.strip().split("\t")
             if line[0] == "PROFILE":
+                if len(line) < 2:
+                    continue
                 trace_list = prase(line[1], line[2], counter)
                 counter += 1
                 for trace in trace_list:

python/pipeline/__init__.py

@@ -15,3 +15,4 @@
 from operator import Op, RequestOp, ResponseOp
 from pipeline_server import PipelineServer
 from pipeline_client import PipelineClient
+from analyse import Analyst

python/pipeline/analyse.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 json
+import copy
+import re
+import logging
+
+_LOGGER = logging.getLogger()
+
+
+class Analyst(object):
+    def __init__(self, profile_file):
+        self._profile_file = profile_file
+        self._trace = None
+
+        self.ave_call = None
+        self.ave_prepack = None
+        self.ave_postpack = None
+        self.op_analyst = None
+
+        self.start_time = None
+        self.end_time = None
+
+    def _prase_line(self, pid_str, time_str, counter):
+        pid = pid_str.split(":")[1]
+        event_list = time_str.split(" ")
+        trace_list = []
+        for event in event_list:
+            name, ts = event.split(":")
+            name_list = name.split("_")
+            ph = "B" if (name_list[-1] == "0") else "E"
+            if len(name_list) == 2:
+                name = name_list[0]
+            else:
+                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"] = tid
+            event_dict["pid"] = pid
+            event_dict["ts"] = ts
+            event_dict["ph"] = ph
+
+            trace_list.append(event_dict)
+        return trace_list
+
+    def get_trace(self):
+        if self._trace is not None:
+            return self._trace
+        all_list = []
+        counter = 0
+        with open(self._profile_file) as f:
+            for line in f.readlines():
+                line = line.strip().split("\t")
+                if line[0] == "PROFILE":
+                    trace_list = self._prase_line(line[1], line[2], counter)
+                    counter += 1
+                    for trace in trace_list:
+                        all_list.append(trace)
+        self._trace = all_list
+        return self._trace
+
+    def save_trace(self, trace_file):
+        self.get_trace()
+        trace = json.dumps(self._trace, indent=2, separators=(',', ':'))
+        with open(trace_file, "w") as f:
+            f.write(trace)
+
+    def print_profile(self):
+        self.get_profile()
+        print("graph engine call: {}".format(self.ave_call))
+        print("rpc prepack: {}".format(self.ave_prepack))
+        print("rpc postpack: {}".format(self.ave_postpack))
+        print("OP: {}".format(self.op_analyst))
+
+    def get_op_analyst(self):
+        self.get_profile()
+        return self.op_analyst
+
+    def get_profile(self):
+        if self.ave_call is not None and \
+                self.ave_prepack is not None and \
+                self.ave_postpack is not None and \
+                self.op_analyst is not None:
+            return (self.ave_call, self.ave_prepack, self.ave_postpack,
+                    self.op_analyst)
+        trace = self.get_trace()
+        time_dict = {}
+        time_list_dict = {}
+        start, end = None, None
+        for event in trace:
+            name = "{}#{}".format(event["name"], event["tid"])
+            event_t = int(event["ts"])
+            if name in time_dict:
+                ts = event_t - time_dict.pop(name)
+                ts = ts / 1e3  # ms
+                if name not in time_list_dict:
+                    time_list_dict[name] = []
+                time_list_dict[name].append(ts)
+            else:
+                time_dict[name] = event_t
+            if start is None:
+                start = event_t
+            elif start > event_t:
+                start = event_t
+            if end is None:
+                end = event_t
+            elif end < event_t:
+                end = event_t
+        self.start_time = start
+        self.end_time = end
+
+        op_analyst = OpAnalyst(start, end)
+        # reduce prepack_n, postpack_n, call_n
+        pat_prepack = re.compile(r"prepack_\d+#@G")
+        prepack_time_list = []
+        pat_postpack = re.compile(r"postpack_\d+#@G")
+        postpack_time_list = []
+        pat_call = re.compile(r"call_\d+#DAG")
+        call_time_list = []
+        for name in time_list_dict:
+            if pat_prepack.match(name):
+                prepack_time_list.extend(time_list_dict[name])
+            elif pat_postpack.match(name):
+                postpack_time_list.extend(time_list_dict[name])
+            elif pat_call.match(name):
+                call_time_list.extend(time_list_dict[name])
+            else:
+                op_analyst.add(name, time_list_dict[name])
+
+        self.ave_call = sum(call_time_list) * 1.0 / len(call_time_list)
+        self.ave_prepack = sum(prepack_time_list) * 1.0 / len(prepack_time_list)
+        self.ave_postpack = sum(postpack_time_list) * 1.0 / len(
+            postpack_time_list)
+        self.op_analyst = op_analyst
+        return (self.ave_call, self.ave_prepack, self.ave_postpack,
+                self.op_analyst)
+
+
+class OpAnalyst(object):
+    def __init__(self, start_time, end_time):
+        self.op_time_list_dict = {}
+        self._qps = None
+        self._close = False
+        self.start_time = start_time
+        self.end_time = end_time
+
+    def add(self, name_str, ts_list):
+        if self._close:
+            _LOGGER.error("OpAnalyst is closed.")
+            return
+        op_name, curr_idx, step = self._parse(name_str)
+        if op_name not in self.op_time_list_dict:
+            self.op_time_list_dict[op_name] = {}
+        if curr_idx not in self.op_time_list_dict[op_name]:
+            self.op_time_list_dict[op_name][curr_idx] = {}
+        if step not in self.op_time_list_dict[op_name][curr_idx]:
+            self.op_time_list_dict[op_name][curr_idx][step] = []
+        self.op_time_list_dict[op_name][curr_idx][step].extend(ts_list)
+
+    def _parse(self, name):
+        step, name_str = name.split("#")
+        name_str = name_str[1:-1]
+        op_name, curr_idx = name_str.split("|")
+        return op_name, curr_idx, step
+
+    def _reduce_profile(self):
+        """
+        Calculating the average time-consuming of multiple concurrent OPs.
+        """
+        if self._close:
+            return
+        for op_name in self.op_time_list_dict:
+            total_time = None
+            for curr_idx in self.op_time_list_dict[op_name]:
+                ave_dict = {}
+                for step in self.op_time_list_dict[op_name][curr_idx]:
+                    ave_dict[step] = sum(self.op_time_list_dict[op_name][
+                        curr_idx][step]) * 1.0 / len(self.op_time_list_dict[
+                            op_name][curr_idx][step])
+                if total_time is None:
+                    total_time = ave_dict
+                else:
+                    for step in ave_dict:
+                        total_time[step] += ave_dict[step]
+            for step in total_time:
+                total_time[step] = total_time[step] * 1.0 / len(
+                    self.op_time_list_dict[op_name])
+            self.op_time_list_dict[op_name] = total_time
+        self._close = True
+
+    def _get_qps(self):
+        """
+        Calculating QPS for each step based on the time
+        consumed in each step of OP.
+        """
+        if self._qps is not None:
+            return self._qps
+        self._reduce_profile()
+        self._qps = {}
+        for op_name, times in self.op_time_list_dict.items():
+            self._qps[op_name] = {
+                step: 1000.0 / ts
+                for step, ts in times.items()
+            }
+        return self._qps
+
+    def __str__(self):
+        self._reduce_profile()
+        return json.dumps(
+            self.op_time_list_dict, indent=2, separators=(', ', ':'))
+
+    def qps(self, op_name=None):
+        """
+        Get the average QPS of each step of each OP (in q/s)
+        """
+        self._get_qps()
+        if op_name is None:
+            return self._qps
+        else:
+            return self._qps[op_name]
+
+    def times(self, op_name=None):
+        """
+        Get the average time of each step of each OP (in ms)
+        """
+        self._reduce_profile()
+        if op_name is None:
+            return self.op_time_list_dict
+        else:
+            return self.op_time_list_dict[op_name]
+
+    def concurrency_analysis(self, op_config_yaml):
+        """
+        Through OP time consuming and op_config_yaml to
+        calculate the theoretical QPS, as well as the
+        number of concurrency required by each OPs.
+        
+        It should be noted that since multiple models
+        will affect each other on one card, only the
+        case that each model is on a different card can
+        be calculated.
+
+        The format of the yaml file is as follows:
+        ```yaml
+        <op_name>:
+            <step(prep, midp or postp)>: <GPU id>
+        ```
+
+        For example:
+        ```yaml
+        cnn:
+            midp: 0
+        bow:
+            midp: 1
+        ```
+        """
+        import yaml
+        with open(op_config_yaml) as f:
+            op_config = yaml.load(f)
+
+        # check that each model is deployed on a different card
+        card_set = set()
+        # and finding the most time consuming part (GPU)
+        op_times = self.times()
+        most_time = 0
+        most_time_op_name = None
+        for op in op_config:
+            for step, cards in op_config[op].items():
+                if isinstance(cards, int):
+                    cards = [cards]
+                elif isinstance(cards, str):
+                    cards = [int(x) for x in cards.split(',')]
+                else:
+                    raise Exception("Error cards type.")
+                for card in cards:
+                    if card in card_set:
+                        raise Exception(
+                            "Analysis is failed because "
+                            "different services interact when different"
+                            " models are deployed on one card.")
+                    else:
+                        card_set.add(card)
+                times_each_card = op_times[op][step] / len(cards)
+                if most_time < times_each_card:
+                    most_time = times_each_card
+                    most_time_op_name = op
+
+        # calculate base qps
+        base_qps = 1.0 / most_time  # q/ms
+        _LOGGER.info("Most Time Consuming (GPU): {} ms (op: {})"
+                     .format(most_time, most_time_op_name))
+        _LOGGER.info("Theoretically Expected QPS: {} q/s".format(base_qps *
+                                                                 1000))
+
+        # reduce op times
+        op_times = {
+            op_name: sum(step_times.values())
+            for op_name, step_times in op_times.items()
+        }
+
+        # calculate op concurrency
+        op_concurrency = {
+            op_name: round(base_qps * times, 3)
+            for op_name, times in op_times.items()
+        }
+        return op_concurrency

python/pipeline/channel.py

@@ -37,7 +37,8 @@ class ChannelDataEcode(enum.Enum):
     TYPE_ERROR = 3
     RPC_PACKAGE_ERROR = 4
     CLIENT_ERROR = 5
-    UNKNOW = 6
+    CLOSED_ERROR = 6
+    UNKNOW = 7
 
 
 class ChannelDataType(enum.Enum):
@@ -53,7 +54,8 @@ class ChannelData(object):
                  dictdata=None,
                  data_id=None,
                  ecode=None,
-                 error_info=None):
+                 error_info=None,
+                 client_need_profile=False):
         '''
         There are several ways to use it:
         
@@ -87,6 +89,13 @@ class ChannelData(object):
         self.id = data_id
         self.ecode = ecode
         self.error_info = error_info
+        self.client_need_profile = client_need_profile
+        self.profile_data_set = set()
+
+    def add_profile(self, profile_set):
+        if self.client_need_profile is False:
+            self.client_need_profile = True
+        self.profile_data_set |= profile_set
 
     @staticmethod
     def check_dictdata(dictdata):
@@ -156,7 +165,7 @@ class ChannelData(object):
             ChannelDataType(self.datatype).name, self.ecode, self.id)
 
 
-class ProcessChannel(multiprocessing.queues.Queue):
+class ProcessChannel(object):
     """ 
     (Process version) The channel used for communication between Ops.
 
@@ -186,18 +195,17 @@ class ProcessChannel(multiprocessing.queues.Queue):
     """
 
     def __init__(self, manager, name=None, maxsize=0, timeout=None):
-        # https://stackoverflow.com/questions/39496554/cannot-subclass-multiprocessing-queue-in-python-3-5/
-        if sys.version_info.major == 2:
-            super(ProcessChannel, self).__init__(maxsize=maxsize)
-        elif sys.version_info.major == 3:
-            super(ProcessChannel, self).__init__(
-                maxsize=maxsize, ctx=multiprocessing.get_context())
-        else:
-            raise Exception("Error Python version")
+        # For queue multiprocess: after putting an object on 
+        # an empty queue there may be an infinitessimal delay
+        # before the queue's :meth:`~Queue.empty`
+        # see more:
+        # - https://bugs.python.org/issue18277
+        # - https://hg.python.org/cpython/rev/860fc6a2bd21
+        self._que = manager.Queue(maxsize=maxsize)
         self._maxsize = maxsize
         self._timeout = timeout
         self.name = name
-        self._stop = False
+        self._stop = manager.Value('i', 0)
 
         self._cv = multiprocessing.Condition()
 
@@ -253,15 +261,17 @@ class ProcessChannel(multiprocessing.queues.Queue):
                 ))
         elif len(self._producers) == 1:
             with self._cv:
-                while self._stop is False:
+                while self._stop.value == 0:
                     try:
-                        self.put({op_name: channeldata}, timeout=0)
+                        self._que.put({op_name: channeldata}, timeout=0)
                         break
                     except Queue.Full:
                         self._cv.wait()
+                if self._stop.value == 1:
+                    raise ChannelStopError()
                 _LOGGER.debug(
                     self._log("{} channel size: {}".format(op_name,
-                                                           self.qsize())))
+                                                           self._que.qsize())))
                 self._cv.notify_all()
                 _LOGGER.debug(self._log("{} notify all".format(op_name)))
             _LOGGER.debug(self._log("{} push data succ!".format(op_name)))
@@ -300,15 +310,17 @@ class ProcessChannel(multiprocessing.queues.Queue):
                     self._log("{} push data succ, but not push to queue.".
                               format(op_name)))
             else:
-                while self._stop is False:
+                while self._stop.value == 0:
                     try:
                         _LOGGER.debug(
                             self._log("{} push data succ: {}".format(
                                 op_name, put_data.__str__())))
-                        self.put(put_data, timeout=0)
+                        self._que.put(put_data, timeout=0)
                         break
                     except Queue.Empty:
                         self._cv.wait()
+                if self._stop.value == 1:
+                    raise ChannelStopError()
 
                 _LOGGER.debug(
                     self._log("multi | {} push data succ!".format(op_name)))
@@ -325,25 +337,21 @@ class ProcessChannel(multiprocessing.queues.Queue):
         elif len(self._consumer_cursors) == 1:
             resp = None
             with self._cv:
-                while self._stop is False and resp is None:
+                while self._stop.value == 0 and resp is None:
                     try:
                         _LOGGER.debug(
                             self._log("{} try to get(with channel empty: {})".
-                                      format(op_name, self.empty())))
-                        # For queue multiprocess: after putting an object on 
-                        # an empty queue there may be an infinitessimal delay
-                        # before the queue's :meth:`~Queue.empty`
-                        # see more:
-                        # - https://bugs.python.org/issue18277
-                        # - https://hg.python.org/cpython/rev/860fc6a2bd21
-                        resp = self.get(timeout=1e-3)
+                                      format(op_name, self._que.empty())))
+                        resp = self._que.get(timeout=0)
                         break
                     except Queue.Empty:
                         _LOGGER.debug(
                             self._log(
                                 "{} wait for empty queue(with channel empty: {})".
-                                format(op_name, self.empty())))
+                                format(op_name, self._que.empty())))
                         self._cv.wait()
+                if self._stop.value == 1:
+                    raise ChannelStopError()
             _LOGGER.debug(
                 self._log("{} get data succ: {}".format(op_name, resp.__str__(
                 ))))
@@ -366,7 +374,7 @@ class ProcessChannel(multiprocessing.queues.Queue):
         with self._cv:
             # When the data required by the current Op is not in output_buf,
             # it is necessary to obtain a data from queue and add it to output_buf.
-            while self._stop is False and self._consumer_cursors[
+            while self._stop.value == 0 and self._consumer_cursors[
                     op_name] - self._base_cursor.value >= len(self._output_buf):
                 _LOGGER.debug(
                     self._log(
@@ -376,22 +384,18 @@ class ProcessChannel(multiprocessing.queues.Queue):
                 try:
                     _LOGGER.debug(
                         self._log("{} try to get(with channel size: {})".format(
-                            op_name, self.qsize())))
-                    # For queue multiprocess: after putting an object on 
-                    # an empty queue there may be an infinitessimal delay
-                    # before the queue's :meth:`~Queue.empty`
-                    # see more:
-                    # - https://bugs.python.org/issue18277
-                    # - https://hg.python.org/cpython/rev/860fc6a2bd21
-                    channeldata = self.get(timeout=1e-3)
+                            op_name, self._que.qsize())))
+                    channeldata = self._que.get(timeout=0)
                     self._output_buf.append(channeldata)
                     break
                 except Queue.Empty:
                     _LOGGER.debug(
                         self._log(
                             "{} wait for empty queue(with channel size: {})".
-                            format(op_name, self.qsize())))
+                            format(op_name, self._que.qsize())))
                     self._cv.wait()
+            if self._stop.value == 1:
+                raise ChannelStopError()
 
             consumer_cursor = self._consumer_cursors[op_name]
             base_cursor = self._base_cursor.value
@@ -438,9 +442,9 @@ class ProcessChannel(multiprocessing.queues.Queue):
         return resp  # reference, read only
 
     def stop(self):
-        #TODO
-        self.close()
-        self._stop = True
+        _LOGGER.debug(self._log("stop."))
+        self._stop.value = 1
+        with self._cv:
             self._cv.notify_all()
 
 
@@ -540,6 +544,8 @@ class ThreadChannel(Queue.Queue):
                         break
                     except Queue.Full:
                         self._cv.wait()
+                if self._stop:
+                    raise ChannelStopError()
                 self._cv.notify_all()
             _LOGGER.debug(self._log("{} push data succ!".format(op_name)))
             return True
@@ -578,6 +584,8 @@ class ThreadChannel(Queue.Queue):
                         break
                     except Queue.Empty:
                         self._cv.wait()
+                if self._stop:
+                    raise ChannelStopError()
 
                 _LOGGER.debug(
                     self._log("multi | {} push data succ!".format(op_name)))
@@ -600,6 +608,8 @@ class ThreadChannel(Queue.Queue):
                         break
                     except Queue.Empty:
                         self._cv.wait()
+                if self._stop:
+                    raise ChannelStopError()
             _LOGGER.debug(
                 self._log("{} get data succ: {}".format(op_name, resp.__str__(
                 ))))
@@ -630,12 +640,14 @@ class ThreadChannel(Queue.Queue):
                     break
                 except Queue.Empty:
                     self._cv.wait()
+            if self._stop:
+                raise ChannelStopError()
 
             consumer_cursor = self._consumer_cursors[op_name]
             base_cursor = self._base_cursor
             data_idx = consumer_cursor - base_cursor
-            resp = self._output_buf[data_idx]
-            _LOGGER.debug(self._log("{} get data: {}".format(op_name, resp)))
+
+            resp = None
 
             self._cursor_count[consumer_cursor] -= 1
             if consumer_cursor == base_cursor and self._cursor_count[
@@ -643,7 +655,7 @@ class ThreadChannel(Queue.Queue):
                 # When all the different Ops get the data that data_idx points
                 # to, pop the data from output_buf.
                 self._cursor_count.pop(consumer_cursor)
-                self._output_buf.pop(0)
+                resp = self._output_buf.pop(0)
                 self._base_cursor += 1
                 # to avoid cursor overflow
                 if self._base_cursor >= self._reset_max_cursor:
@@ -654,6 +666,9 @@ class ThreadChannel(Queue.Queue):
                         cursor - self._reset_max_cursor: count
                         for cursor, count in self._cursor_count.items()
                     }
+            else:
+                resp = copy.deepcopy(self._output_buf[data_idx])
+            _LOGGER.debug(self._log("{} get data: {}".format(op_name, resp)))
 
             self._consumer_cursors[op_name] += 1
             new_consumer_cursor = self._consumer_cursors[op_name]
@@ -664,11 +679,15 @@ class ThreadChannel(Queue.Queue):
             self._cv.notify_all()
 
         _LOGGER.debug(self._log("multi | {} get data succ!".format(op_name)))
-        # return resp  # reference, read only
-        return copy.deepcopy(resp)
+        return resp
 
     def stop(self):
-        #TODO
-        self.close()
+        _LOGGER.debug(self._log("stop."))
         self._stop = True
+        with self._cv:
             self._cv.notify_all()
+
+
+class ChannelStopError(RuntimeError):
+    def __init__(self):
+        pass

python/pipeline/operator.py

@@ -20,11 +20,15 @@ from concurrent import futures
 import logging
 import func_timeout
 import os
+import sys
+import numpy as np
 from numpy import *
 
 from .proto import pipeline_service_pb2
-from .channel import ThreadChannel, ProcessChannel, ChannelDataEcode, ChannelData, ChannelDataType
+from .channel import (ThreadChannel, ProcessChannel, ChannelDataEcode,
+                      ChannelData, ChannelDataType, ChannelStopError)
 from .util import NameGenerator
+from .profiler import TimeProfiler
 
 _LOGGER = logging.getLogger()
 _op_name_gen = NameGenerator("Op")
@@ -42,7 +46,6 @@ class Op(object):
                  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)
@@ -58,14 +61,17 @@ class Op(object):
         self._retry = max(1, retry)
         self._input = None
         self._outputs = []
-        self._profiler = None
+
+        self._server_use_profile = False
 
         # only for multithread
         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_profiler(self, profiler):
-        self._profiler = profiler
+    def use_profiler(self, use_profile):
+        self._server_use_profile = use_profile
 
     def _profiler_record(self, string):
         if self._profiler is None:
@@ -90,9 +96,6 @@ class Op(object):
         self._fetch_names = fetch_names
         return client
 
-    def _get_input_channel(self):
-        return self._input
-
     def get_input_ops(self):
         return self._input_ops
 
@@ -115,8 +118,11 @@ class Op(object):
         channel.add_consumer(self.name)
         self._input = channel
 
-    def _get_output_channels(self):
-        return self._outputs
+    def clean_input_channel(self):
+        self._input = None
+
+    def _get_input_channel(self):
+        return self._input
 
     def add_output_channel(self, channel):
         if not isinstance(channel, (ThreadChannel, ProcessChannel)):
@@ -126,6 +132,12 @@ class Op(object):
         channel.add_producer(self.name)
         self._outputs.append(channel)
 
+    def clean_output_channels(self):
+        self._outputs = []
+
+    def _get_output_channels(self):
+        return self._outputs
+
     def preprocess(self, input_dicts):
         # multiple previous Op
         if len(input_dicts) != 1:
@@ -136,12 +148,12 @@ class Op(object):
         (_, input_dict), = input_dicts.items()
         return input_dict
 
-    def process(self, client_predict_handler, feed_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))
-        call_result = client_predict_handler(
+        call_result = self.client.predict(
             feed=feed_dict, fetch=self._fetch_names)
         _LOGGER.debug(self._log("get call_result"))
         return call_result
@@ -149,29 +161,48 @@ class Op(object):
     def postprocess(self, input_dict, fetch_dict):
         return fetch_dict
 
-    def stop(self):
-        self._is_run = False
-
     def _parse_channeldata(self, channeldata_dict):
         data_id, error_channeldata = None, None
+        client_need_profile, profile_set = False, set()
         parsed_data = {}
 
         key = list(channeldata_dict.keys())[0]
         data_id = channeldata_dict[key].id
+        client_need_profile = channeldata_dict[key].client_need_profile
 
         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 client_need_profile:
+                profile_set |= data.profile_data_set
+        return (data_id, error_channeldata, parsed_data, client_need_profile,
+                profile_set)
+
+    def _push_to_output_channels(self,
+                                 data,
+                                 channels,
+                                 name=None,
+                                 client_need_profile=False,
+                                 profile_set=None):
         if name is None:
             name = self.name
+        self._add_profile_into_channeldata(data, client_need_profile,
+                                           profile_set)
         for channel in channels:
             channel.push(data, name)
 
+    def _add_profile_into_channeldata(self, data, client_need_profile,
+                                      profile_set):
+        profile_str = self._profiler.gen_profile_str()
+        if self._server_use_profile:
+            sys.stderr.write(profile_str)
+
+        if client_need_profile and profile_set is not None:
+            profile_set.add(profile_str)
+            data.add_profile(profile_set)
+
     def start_with_process(self, client_type):
         proces = []
         for concurrency_idx in range(self.concurrency):
@@ -226,15 +257,13 @@ class Op(object):
                 data_id=data_id)
         return preped_data, error_channeldata
 
-    def _run_process(self, client_predict_handler, preped_data, data_id,
-                     log_func):
+    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(client_predict_handler,
-                                               preped_data)
+                    midped_data = self.process(preped_data)
                 except Exception as e:
                     ecode = ChannelDataEcode.UNKNOW.value
                     error_info = log_func(e)
@@ -243,11 +272,7 @@ class Op(object):
                 for i in range(self._retry):
                     try:
                         midped_data = func_timeout.func_timeout(
-                            self._timeout,
-                            self.process,
-                            args=(
-                                client_predict_handler,
-                                preped_data, ))
+                            self._timeout, self.process, args=(preped_data, ))
                     except func_timeout.FunctionTimedOut as e:
                         if i + 1 >= self._retry:
                             ecode = ChannelDataEcode.TIMEOUT.value
@@ -314,7 +339,7 @@ class Op(object):
         return output_data, error_channeldata
 
     def _run(self, concurrency_idx, input_channel, output_channels, client_type,
-             use_multithread):
+             is_thread_op):
         def get_log_func(op_info_prefix):
             def log_func(info_str):
                 return "{} {}".format(op_info_prefix, info_str)
@@ -325,80 +350,130 @@ class Op(object):
         log = get_log_func(op_info_prefix)
         tid = threading.current_thread().ident
 
-        client = None
-        client_predict_handler = None
-        # create client based on client_type
-        try:
-            client = self.init_client(client_type, self._client_config,
-                                      self._server_endpoints, self._fetch_names)
-            if client is not None:
-                client_predict_handler = client.predict
-        except Exception as e:
-            _LOGGER.error(log(e))
-            os._exit(-1)
-
-        # load user resources
+        # init op
+        self.concurrency_idx = concurrency_idx
         try:
-            if use_multithread:
+            if is_thread_op:
                 with self._for_init_op_lock:
                     if not self._succ_init_op:
+                        # init profiler
+                        self._profiler = TimeProfiler()
+                        self._profiler.enable(True)
+                        # init client
+                        self.client = self.init_client(
+                            client_type, self._client_config,
+                            self._server_endpoints, self._fetch_names)
+                        # user defined
                         self.init_op()
                         self._succ_init_op = True
+                        self._succ_close_op = False
             else:
+                # init profiler
+                self._profiler = TimeProfiler()
+                self._profiler.enable(True)
+                # init client
+                self.client = self.init_client(client_type, self._client_config,
+                                               self._server_endpoints,
+                                               self._fetch_names)
+                # user defined
                 self.init_op()
         except Exception as e:
             _LOGGER.error(log(e))
             os._exit(-1)
 
-        self._is_run = True
-        while self._is_run:
-            self._profiler_record("{}-get#{}_0".format(op_info_prefix, tid))
+        while True:
+            #self._profiler_record("get#{}_0".format(op_info_prefix))
+            try:
                 channeldata_dict = input_channel.front(self.name)
-            self._profiler_record("{}-get#{}_1".format(op_info_prefix, tid))
+            except ChannelStopError:
+                _LOGGER.debug(log("stop."))
+                if is_thread_op:
+                    with self._for_close_op_lock:
+                        if not self._succ_close_op:
+                            self._profiler = None
+                            self.client = None
+                            self._succ_init_op = False
+                            self._succ_close_op = True
+                break
+            #self._profiler_record("get#{}_1".format(op_info_prefix))
             _LOGGER.debug(log("input_data: {}".format(channeldata_dict)))
 
-            data_id, error_channeldata, parsed_data = self._parse_channeldata(
-                channeldata_dict)
+            (data_id, error_channeldata, parsed_data, client_need_profile,
+             profile_set) = self._parse_channeldata(channeldata_dict)
             # error data in predecessor Op
             if error_channeldata is not None:
+                try:
+                    # error_channeldata with profile info
                     self._push_to_output_channels(error_channeldata,
                                                   output_channels)
+                except ChannelStopError:
+                    _LOGGER.debug(log("stop."))
+                    break
                 continue
 
             # preprecess
-            self._profiler_record("{}-prep#{}_0".format(op_info_prefix, tid))
+            self._profiler_record("prep#{}_0".format(op_info_prefix))
             preped_data, error_channeldata = self._run_preprocess(parsed_data,
                                                                   data_id, log)
-            self._profiler_record("{}-prep#{}_1".format(op_info_prefix, tid))
+            self._profiler_record("prep#{}_1".format(op_info_prefix))
             if error_channeldata is not None:
-                self._push_to_output_channels(error_channeldata,
-                                              output_channels)
+                try:
+                    self._push_to_output_channels(
+                        error_channeldata,
+                        output_channels,
+                        client_need_profile=client_need_profile,
+                        profile_set=profile_set)
+                except ChannelStopError:
+                    _LOGGER.debug(log("stop."))
+                    break
                 continue
 
             # process
-            self._profiler_record("{}-midp#{}_0".format(op_info_prefix, tid))
-            midped_data, error_channeldata = self._run_process(
-                client_predict_handler, preped_data, data_id, log)
-            self._profiler_record("{}-midp#{}_1".format(op_info_prefix, tid))
+            self._profiler_record("midp#{}_0".format(op_info_prefix))
+            midped_data, error_channeldata = self._run_process(preped_data,
+                                                               data_id, log)
+            self._profiler_record("midp#{}_1".format(op_info_prefix))
             if error_channeldata is not None:
-                self._push_to_output_channels(error_channeldata,
-                                              output_channels)
+                try:
+                    self._push_to_output_channels(
+                        error_channeldata,
+                        output_channels,
+                        client_need_profile=client_need_profile,
+                        profile_set=profile_set)
+                except ChannelStopError:
+                    _LOGGER.debug(log("stop."))
+                    break
                 continue
 
             # postprocess
-            self._profiler_record("{}-postp#{}_0".format(op_info_prefix, tid))
+            self._profiler_record("postp#{}_0".format(op_info_prefix))
             output_data, error_channeldata = self._run_postprocess(
                 parsed_data, midped_data, data_id, log)
-            self._profiler_record("{}-postp#{}_1".format(op_info_prefix, tid))
+            self._profiler_record("postp#{}_1".format(op_info_prefix))
             if error_channeldata is not None:
-                self._push_to_output_channels(error_channeldata,
-                                              output_channels)
+                try:
+                    self._push_to_output_channels(
+                        error_channeldata,
+                        output_channels,
+                        client_need_profile=client_need_profile,
+                        profile_set=profile_set)
+                except ChannelStopError:
+                    _LOGGER.debug(log("stop."))
+                    break
                 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))
+            #self._profiler_record("push#{}_0".format(op_info_prefix))
+            try:
+                self._push_to_output_channels(
+                    output_data,
+                    output_channels,
+                    client_need_profile=client_need_profile,
+                    profile_set=profile_set)
+            except ChannelStopError:
+                _LOGGER.debug(log("stop."))
+                break
+            #self._profiler_record("push#{}_1".format(op_info_prefix))
 
     def _log(self, info):
         return "{} {}".format(self.name, info)
@@ -407,11 +482,10 @@ class Op(object):
 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
+    def __init__(self):
+        # PipelineService.name = "@G"
+        super(RequestOp, self).__init__(name="@G", input_ops=[])
+        # init op
         try:
             self.init_op()
         except Exception as e:
@@ -433,10 +507,9 @@ class RequestOp(Op):
 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
+    def __init__(self, input_ops):
+        super(ResponseOp, self).__init__(name="@R", input_ops=input_ops)
+        # init op
         try:
             self.init_op()
         except Exception as e:
@@ -451,6 +524,7 @@ class ResponseOp(Op):
                 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
+                np.set_printoptions(threshold=np.nan)
                 for name, var in feed.items():
                     resp.value.append(var.__repr__())
                     resp.key.append(name)
@@ -505,7 +579,7 @@ class VirtualOp(Op):
         self._outputs.append(channel)
 
     def _run(self, concurrency_idx, input_channel, output_channels, client_type,
-             use_multithread):
+             is_thread_op):
         def get_log_func(op_info_prefix):
             def log_func(info_str):
                 return "{} {}".format(op_info_prefix, info_str)
@@ -516,14 +590,17 @@ class VirtualOp(Op):
         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))
+        while True:
+            try:
                 channeldata_dict = input_channel.front(self.name)
-            self._profiler_record("{}-get#{}_1".format(op_info_prefix, tid))
+            except ChannelStopError:
+                _LOGGER.debug(log("stop."))
+                break
 
-            self._profiler_record("{}-push#{}_0".format(op_info_prefix, tid))
+            try:
                 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))
+            except ChannelStopError:
+                _LOGGER.debug(log("stop."))
+                break

python/pipeline/pipeline_client.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 # pylint: disable=doc-string-missing
 import grpc
+import sys
 import numpy as np
 from numpy import *
 import logging
@@ -26,13 +27,19 @@ _LOGGER = logging.getLogger()
 class PipelineClient(object):
     def __init__(self):
         self._channel = None
+        self._profile_key = "pipeline.profile"
+        self._profile_value = "1"
 
-    def connect(self, endpoint):
-        self._channel = grpc.insecure_channel(endpoint)
+    def connect(self, endpoints):
+        options = [('grpc.max_receive_message_length', 512 * 1024 * 1024),
+                   ('grpc.max_send_message_length', 512 * 1024 * 1024),
+                   ('grpc.lb_policy_name', 'round_robin')]
+        g_endpoint = 'ipv4:{}'.format(','.join(endpoints))
+        self._channel = grpc.insecure_channel(g_endpoint, options=options)
         self._stub = pipeline_service_pb2_grpc.PipelineServiceStub(
             self._channel)
 
-    def _pack_request_package(self, feed_dict):
+    def _pack_request_package(self, feed_dict, profile):
         req = pipeline_service_pb2.Request()
         for key, value in feed_dict.items():
             req.key.append(key)
@@ -45,6 +52,9 @@ class PipelineClient(object):
             else:
                 raise TypeError("only str and np.ndarray type is supported: {}".
                                 format(type(value)))
+        if profile:
+            req.key.append(self._profile_key)
+            req.value.append(self._profile_value)
         return req
 
     def _unpack_response_package(self, resp, fetch):
@@ -52,6 +62,10 @@ class PipelineClient(object):
             return {"ecode": resp.ecode, "error_info": resp.error_info}
         fetch_map = {"ecode": resp.ecode}
         for idx, key in enumerate(resp.key):
+            if key == self._profile_key:
+                if resp.value[idx] != "":
+                    sys.stderr.write(resp.value[idx])
+                continue
             if fetch is not None and key not in fetch:
                 continue
             data = resp.value[idx]
@@ -62,13 +76,13 @@ class PipelineClient(object):
             fetch_map[key] = data
         return fetch_map
 
-    def predict(self, feed_dict, fetch=None, asyn=False):
+    def predict(self, feed_dict, fetch=None, asyn=False, profile=False):
         if not isinstance(feed_dict, dict):
             raise TypeError(
                 "feed must be dict type with format: {name: value}.")
         if fetch is not None and not isinstance(fetch, list):
             raise TypeError("fetch must be list type with format: [name].")
-        req = self._pack_request_package(feed_dict)
+        req = self._pack_request_package(feed_dict, profile)
         if not asyn:
             resp = self._stub.inference(req)
             return self._unpack_response_package(resp, fetch)

python/pipeline/profiler.py

@@ -23,6 +23,7 @@ elif sys.version_info.major == 3:
 else:
     raise Exception("Error Python version")
 import time
+import threading
 
 _LOGGER = logging.getLogger()
 
@@ -33,6 +34,7 @@ class TimeProfiler(object):
         self._print_head = 'PROFILE\tpid:{}\t'.format(self._pid)
         self._time_record = Queue.Queue()
         self._enable = False
+        self._lock = threading.Lock()
 
     def enable(self, enable):
         self._enable = enable
@@ -40,16 +42,24 @@ class TimeProfiler(object):
     def record(self, name_with_tag):
         if self._enable is False:
             return
+        timestamp = int(round(time.time() * 1000000))
         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))))
+        with self._lock:
+            self._time_record.put((name, tag, timestamp))
 
     def print_profile(self):
+        if self._enable is False:
+            return
+        sys.stderr.write(self.gen_profile_str())
+
+    def gen_profile_str(self):
         if self._enable is False:
             return
         print_str = self._print_head
         tmp = {}
+        with self._lock:
             while not self._time_record.empty():
                 name, tag, timestamp = self._time_record.get()
                 if name in tmp:
@@ -59,7 +69,7 @@ class TimeProfiler(object):
                 else:
                     tmp[name] = (tag, timestamp)
             print_str = "\n{}\n".format(print_str)
-        sys.stderr.write(print_str)
             for name, item in tmp.items():
                 tag, timestamp = item
                 self._time_record.put((name, tag, timestamp))
+            return print_str

python/requirements.txt

 numpy>=1.12, <=1.16.4 ; python_version<"3.5"
+protobuf>=3.12.2
 grpcio-tools>=1.28.1
 grpcio>=1.28.1
 func-timeout>=4.3.5
+pyyaml>=1.3.0

tools/Dockerfile.ci

@@ -48,3 +48,5 @@ RUN yum -y update >/dev/null \
 RUN yum install -y java \
     && wget http://repos.fedorapeople.org/repos/dchen/apache-maven/epel-apache-maven.repo -O /etc/yum.repos.d/epel-apache-maven.repo \
     && yum install -y apache-maven
+
+RUN yum install -y lsof

tools/serving_build.sh

@@ -137,6 +137,15 @@ function kill_server_process() {
     sleep 1
 }
 
+function kill_process_by_port() {
+    if [ $# != 1 ]; then
+        echo "usage: kill_process_by_port <PID>"
+        exit 1
+    fi
+    local PID=$1
+    lsof -i:$PID | awk 'NR == 1 {next} {print $2}' | xargs kill
+}
+
 function python_test_fit_a_line() {
     # pwd: /Serving/python/examples
     cd fit_a_line # pwd: /Serving/python/examples/fit_a_line
@@ -579,6 +588,7 @@ function python_test_grpc_impl() {
             check_cmd "python test_batch_client.py > /dev/null"
             check_cmd "python test_timeout_client.py > /dev/null"
             kill_server_process
+            kill_process_by_port 9393
 
             check_cmd "python test_server.py uci_housing_model > /dev/null &"
             sleep 5 # wait for the server to start
@@ -589,6 +599,7 @@ function python_test_grpc_impl() {
             check_cmd "python test_batch_client.py > /dev/null"
             check_cmd "python test_timeout_client.py > /dev/null"
             kill_server_process
+            kill_process_by_port 9393
 
             cd .. # pwd: /Serving/python/examples/grpc_impl_example
 
@@ -637,6 +648,7 @@ function python_test_grpc_impl() {
             check_cmd "python test_batch_client.py > /dev/null"
             check_cmd "python test_timeout_client.py > /dev/null"
             kill_server_process
+            kill_process_by_port 9393
 
             check_cmd "python test_server_gpu.py uci_housing_model > /dev/null &"
             sleep 5 # wait for the server to start
@@ -647,7 +659,8 @@ function python_test_grpc_impl() {
             check_cmd "python test_batch_client.py > /dev/null"
             check_cmd "python test_timeout_client.py > /dev/null"
             kill_server_process
-            ps -ef | grep "test_server_gpu" | grep -v serving_build | grep -v grep | awk '{print $2}' | xargs kill
+            kill_process_by_port 9393
+            #ps -ef | grep "test_server_gpu" | grep -v serving_build | grep -v grep | awk '{print $2}' | xargs kill
 
             cd .. # pwd: /Serving/python/examples/grpc_impl_example
 
@@ -749,6 +762,128 @@ function python_test_resnet50(){
     cd ..
 }
 
+function python_test_pipeline(){
+    # 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
+    case $TYPE in
+        CPU)
+            # 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 &
+            python -m paddle_serving_server.serve --model imdb_bow_model --port 9393 --workdir test9393 &> bow.log &
+            sleep 5
+            
+            # test: thread servicer & thread op
+            cat << EOF > config.yml
+port: 18080
+worker_num: 2
+build_dag_each_worker: false
+dag:
+    is_thread_op: true
+    client_type: brpc
+    retry: 1
+    use_profile: false
+EOF
+            python test_pipeline_server.py > /dev/null &
+            sleep 5
+            check_cmd "python test_pipeline_client.py"
+            ps -ef | grep "pipeline_server" | grep -v grep | awk '{print $2}' | xargs kill
+            kill_process_by_port 18080
+
+            # test: thread servicer & process op
+            cat << EOF > config.yml
+port: 18080
+worker_num: 2
+build_dag_each_worker: false
+dag:
+    is_thread_op: false
+    client_type: brpc
+    retry: 1
+    use_profile: false
+EOF
+            python test_pipeline_server.py > /dev/null &
+            sleep 5
+            check_cmd "python test_pipeline_client.py"
+            ps -ef | grep "pipeline_server" | grep -v grep | awk '{print $2}' | xargs kill
+            kill_process_by_port 18080
+
+            # test: process servicer & thread op
+            cat << EOF > config.yml
+port: 18080
+worker_num: 2
+build_dag_each_worker: true
+dag:
+    is_thread_op: flase
+    client_type: brpc
+    retry: 1
+    use_profile: false
+EOF
+            python test_pipeline_server.py > /dev/null &
+            sleep 5
+            check_cmd "python test_pipeline_client.py"
+            ps -ef | grep "pipeline_server" | grep -v grep | awk '{print $2}' | xargs kill
+            kill_process_by_port 18080
+
+            # test: process servicer & process op
+            cat << EOF > config.yml
+port: 18080
+worker_num: 2
+build_dag_each_worker: false
+dag:
+    is_thread_op: false
+    client_type: brpc
+    retry: 1
+    use_profile: false
+EOF
+            python test_pipeline_server.py > /dev/null &
+            sleep 5
+            check_cmd "python test_pipeline_client.py"
+            ps -ef | grep "pipeline_server" | grep -v grep | awk '{print $2}' | xargs kill
+            kill_process_by_port 18080
+            
+            kill_server_process
+            kill_process_by_port 9292
+            kill_process_by_port 9393
+
+            # start paddle serving service (grpc)
+            python -m paddle_serving_server.serve --model imdb_cnn_model --port 9292 --use_multilang --workdir test9292 &> cnn.log &
+            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
+worker_num: 2
+build_dag_each_worker: false
+dag:
+    is_thread_op: false
+    client_type: grpc
+    retry: 1
+    use_profile: false
+EOF
+            python test_pipeline_server.py > /dev/null &
+            sleep 5
+            check_cmd "python test_pipeline_client.py"
+            ps -ef | grep "pipeline_server" | grep -v grep | awk '{print $2}' | xargs kill
+            kill_process_by_port 18080
+            kill_server_process
+            kill_process_by_port 9292
+            kill_process_by_port 9393
+            ;;
+        GPU)
+            echo "pipeline ignore GPU test"
+            ;;
+        *)
+            echo "error type"
+            exit 1
+            ;;
+    esac
+    cd ../../
+    setproxy
+    unset SERVING_BIN
+}
+
 function python_app_api_test(){
     #pwd:/ Serving/python/examples
     #test image reader
@@ -784,6 +919,7 @@ function python_run_test() {
     python_test_yolov4 $TYPE # pwd: /Serving/python/examples
     python_test_grpc_impl $TYPE # pwd: /Serving/python/examples
     python_test_resnet50 $TYPE # pwd: /Serving/python/examples
+    python_test_pipeline $TYPE # pwd: /Serving/python/examples
     echo "test python $TYPE part finished as expected."
     cd ../.. # pwd: /Serving
 }