Update doc

doc/Offical_Docs/7-0_Python_Pipeline_Int_CN.md

@@ -6,7 +6,7 @@ Paddle Serving 实现了一套通用的多模型组合服务编程框架 Python
 
 Python Pipeline 使用案例请阅读[Python Pipeline 快速部署案例](./3-2_QuickStart_Pipeline_OCR_CN.md)
 
-通过阅读以下内容掌握 Python Pipeline 设计方案、高阶用法和优化指南等。
+通过阅读以下内容掌握 Python Pipeline 核心功能和使用方法、高阶功能用法和性能优化指南等。
 - [Python Pipeline 框架设计](7-1_Python_Pipeline_Design_CN.md)
 - [Python Pipeline 高阶用法](7-2_Python_Pipeline_Senior_CN.md)
 - [Python Pipeline 优化指南](7-3_Python_Pipeline_Optimize_CN.md)

doc/Offical_Docs/7-1_Python_Pipeline_Design_CN.md

@@ -42,7 +42,7 @@ Request 是输入结构，`key` 与 `value` 是配对的 string 数组。 `name`
 
 Response 是输出结构，`err_no` 和 `err_msg` 表达处理结果的正确性和错误信息，`key` 和 `value` 为结果。
 
-Pipeline 服务包装了继承于 WebService 类，以 OCR 示例为例，派生出 OcrService 类，get_pipeline_response 函数内实现 DAG 拓扑关系，默认服务入口为 read_op，函数返回的 Op 为最后一个处理，此处要求最后返回的 Op 必须唯一。
+Pipeline 服务包装了继承于 WebService 类，以 [OCR 示例](https://github.com/PaddlePaddle/Serving/tree/develop/examples/Pipeline/PaddleOCR/ocr)为例，派生出 OcrService 类，get_pipeline_response 函数内实现 DAG 拓扑关系，默认服务入口为 read_op，函数返回的 Op 为最后一个处理，此处要求最后返回的 Op 必须唯一。
 
 所有服务和模型的所有配置信息在 `config.yml` 中记录，URL 的 name 字段由 OcrService 初始化定义；run_service 函数启动服务。
 
@@ -177,6 +177,8 @@ Pipeline 的日志模块在 `logger.py` 中定义，使用了 `logging.handlers.
 
 ```
 
+**四. 服务超时与重试**
+
 
 
 ## 自定义信息
@@ -297,7 +299,6 @@ def init_op(self):
 ```
 
 RequestOp 和 ResponseOp 是 Python Pipeline 的中2个特殊 Op，分别是用分解 RPC 数据加入到图执行引擎中，和拿到图执行引擎的预测结果并打包 RPC 数据到客户端。
-
 RequestOp 类的设计如下所示，核心是在 unpack_request_package 函数中解析请求数据，因此，当修改 Request 结构后重写此函数实现全新的解包处理。
 
 |                接口                 |                    说明                    |
@@ -334,7 +335,6 @@ class RequestOp(Op):
         return dict_data, log_id, None, ""
 ```
 
-
 ResponseOp 类的设计如下所示，核心是在 pack_response_package 中打包返回结构，因此修改 Response 结构后重写此函数实现全新的打包格式。
 
 |                  接口                  |                    说明                     |
@@ -381,3 +381,35 @@ class ProductErrCode(enum.Enum):
     """
     pass
 ```
+
+其使用方法如下所示，定义了一种错误类型 `Product_Error` ，在 `preprocess` 函数返回值中设置错误信息，在 `postprocess` 函数中也可以设置。
+```python
+
+class ProductErrCode(enum.Enum):
+    """
+    ProductErrCode is a base class for recording business error code. 
+    product developers inherit this class and extend more error codes. 
+    """
+    Product_Error = 100001,
+
+def preprocess(self, input_dicts, data_id, log_id):
+    """
+    In preprocess stage, assembling data for process stage. users can 
+    override this function for model feed features.
+    Args:
+        input_dicts: input data to be preprocessed
+        data_id: inner unique id
+        log_id: global unique id for RTT
+    Return:
+        input_dict: data for process stage
+        is_skip_process: skip process stage or not, False default
+        prod_errcode: None default, otherwise, product errores occured.
+                      It is handled in the same way as exception. 
+        prod_errinfo: "" default
+    """
+    (_, input_dict), = input_dicts.items()
+    if input_dict.get_key("product_error"):
+        return input_dict, False, Product_Error, "Product Error Occured"
+    return input_dict, False, None, "" 
+
+```

doc/Offical_Docs/7-2_Python_Pipeline_Senior_CN.md

@@ -10,12 +10,13 @@
 - MKLDNN 推理加速
 
 
-**一. DAG 结构跳过某个 Op 运行 **
+**一.DAG 结构跳过某个 Op 运行**
 
 此应用场景一般在 Op 前后处理中有 if 条件判断时，不满足条件时，跳过后面处理。实际做法是在跳过此 Op 的 process 阶段，只要在 preprocess 做好判断，跳过 process 阶段，在和 postprocess 后直接返回即可。
 preprocess 返回结果列表的第二个结果是 `is_skip_process=True` 表示是否跳过当前 Op 的 process 阶段，直接进入 postprocess 处理。
 
 ```python
+## Op::preprocess() 函数实现
 def preprocess(self, input_dicts, data_id, log_id):
     """
     In preprocess stage, assembling data for process stage. users can 
@@ -42,30 +43,171 @@ def preprocess(self, input_dicts, data_id, log_id):
     return input_dict, False, None, ""
 
 ```
+以下示例 Jump::preprocess() 重载了原函数，返回了 True 字段
+```python
+class JumpOp(Op):
+    ## Overload func JumpOp::preprocess
+    def preprocess(self, input_dicts, data_id, log_id):
+        (_, input_dict), = input_dicts.items()
+        if input_dict.has_key("jump"):
+            return input_dict, True, None, ""
+        else
+            return input_dict, False, None, ""
+```
+
 
-** 二. 批量推理 **
+**二. 批量推理**
 
 Pipeline 支持批量推理，通过增大 batch size 可以提高 GPU 利用率。Python Pipeline 支持3种 batch 形式以及适用的场景如下：
-- 场景1：一个推理请求包含批量数据(batch)
-  - 单条数据定长，批量变长，数据转成BCHW格式
-  - 单条数据变长，前处理中将单条数据做 padding 转成定长
-- 场景2：一个推理请求的批量数据拆分成多个小块推理(mini-batch)
-  - 由于 padding 会按最长对齐，当一批数据中有个"极大"尺寸数据时会导致推理变慢
-  - 指定一个块大小，从而缩小"极大"尺寸数据的作用范围
-- 场景3：合并多个请求数据批量推理(auto-batching)
-  - 推理耗时明显长于前后处理，合并多个请求数据推理一次会提高吞吐和GPU利用率
-  - 要求多个请求数据的 shape 一致
-
-|                  接口                  |                    说明                     |
-| :------------------------------------------: | :-----------------------------------------: |
-|  batch | client 发送批量数据，client.predict 的 batch=True |
-| mini-batch | preprocess 按 list 类型返回，参考 OCR 示例 RecOp的preprocess|
-| auto-batching | config.yml 中 Op 级别设置 batch_size 和 auto_batching_timeout |
-
-
-** 三. 单机多卡推理 **
-
-单机多卡推理，M 个 Op 进程与 N 个 GPU 卡绑定，在 `config.yml` 中配置3个参数有关系，首先选择进程模式、并发数即进程数，devices 是 GPU 卡 ID。绑定方法是进程启动时遍历 GPU 卡 ID，例如启动7个 Op 进程 `config.yml` 设置 devices:0,1,2，那么第1，4，7个启动的进程与0卡绑定，第2，4个启动的进程与1卡绑定，3，6进程与卡2绑定。
+- 场景1：客户端打包批量数据(Client Batch)
+- 场景2：服务端合并多个请求动态合并批量(Server auto-batching)
+- 场景3：服务端拆分一个批量数据推理请求成为多个小块推理(Server mini-batch)
+
+
+1. 客户端打包批量数据
+
+当输入数据是 numpy 类型，如shape 为[4, 3, 512, 512]的 numpy 数据，即4张图片，可直接作为输入数据。
+当输入数据的 shape 不同时，需要按最大的shape的尺寸 Padding 对齐后发送给服务端
+
+2. 服务端合并多个请求动态合并批量
+有助于提升吞吐和计算资源的利用率，当多个请求的 shape 尺寸不相同时，不支持合并。当前有2种合并策略，分别是：
+
+- 等待时间与最大批量结合（推荐）：结合`batch_size`和`auto_batching_timeout`配合使用，实际请求的批量条数超过`batch_size`时会立即执行，不超过时会等待`auto_batching_timeout`时间再执行
+```
+op:
+    bow:
+        # 并发数，is_thread_op=True时，为线程并发；否则为进程并发
+        concurrency: 1
+
+        # client连接类型，brpc, grpc和local_predictor
+        client_type: brpc
+
+        # Serving IPs
+        server_endpoints: ["127.0.0.1:9393"]
+
+        # bow模型client端配置
+        client_config: "imdb_bow_client_conf/serving_client_conf.prototxt"
+
+        # 批量查询Serving的数量, 默认1。batch_size>1要设置auto_batching_timeout，否则不足batch_size时会阻塞
+        batch_size: 2
+
+        # 批量查询超时，与batch_size配合使用
+        auto_batching_timeout: 2000
+```
+- 阻塞式等待：仅设置`batch_size`，不设置`auto_batching_timeout`或`auto_batching_timeout=0`，会一直等待接受 `batch_size` 个请求后再推理。
+
+```
+op:
+    bow:
+        # 并发数，is_thread_op=True时，为线程并发；否则为进程并发
+        concurrency: 1
+
+        # client连接类型，brpc, grpc和local_predictor
+        client_type: brpc
+
+        # Serving IPs
+        server_endpoints: ["127.0.0.1:9393"]
+
+        # bow模型client端配置
+        client_config: "imdb_bow_client_conf/serving_client_conf.prototxt"
+
+        # 批量查询Serving的数量, 默认1。batch_size>1要设置auto_batching_timeout，否则不足batch_size时会阻塞
+        batch_size: 2
+
+        # 批量查询超时，与batch_size配合使用
+        auto_batching_timeout: 2000
+
+```
+
+
+
+3.服务端拆分一个批量数据推理请求成为多个小块推理：会降低批量数据 Padding 对齐的大小，从而提升速度。可参考 [OCR 示例]()，核心思路是拆分数据成多个小批量，放入 list 对象 feed_list 并返回
+
+```
+def preprocess(self, input_dicts, data_id, log_id):
+    (_, input_dict), = input_dicts.items()
+    raw_im = input_dict["image"]
+    data = np.frombuffer(raw_im, np.uint8)
+    im = cv2.imdecode(data, cv2.IMREAD_COLOR)
+    dt_boxes = input_dict["dt_boxes"]
+    dt_boxes = self.sorted_boxes(dt_boxes)
+    feed_list = []
+    img_list = []
+    max_wh_ratio = 0
+
+    ## Many mini-batchs, the type of feed_data is list.
+    max_batch_size = len(dt_boxes)
+
+    # If max_batch_size is 0, skipping predict stage
+    if max_batch_size == 0:
+        return {}, True, None, ""
+    boxes_size = len(dt_boxes)
+    batch_size = boxes_size // max_batch_size
+    rem = boxes_size % max_batch_size
+    for bt_idx in range(0, batch_size + 1):
+        imgs = None
+        boxes_num_in_one_batch = 0
+        if bt_idx == batch_size:
+            if rem == 0:
+                continue
+            else:
+                boxes_num_in_one_batch = rem
+        elif bt_idx < batch_size:
+            boxes_num_in_one_batch = max_batch_size
+        else:
+            _LOGGER.error("batch_size error, bt_idx={}, batch_size={}".
+                          format(bt_idx, batch_size))
+            break
+
+        start = bt_idx * max_batch_size
+        end = start + boxes_num_in_one_batch
+        img_list = []
+        for box_idx in range(start, end):
+            boximg = self.get_rotate_crop_image(im, dt_boxes[box_idx])
+            img_list.append(boximg)
+            h, w = boximg.shape[0:2]
+            wh_ratio = w * 1.0 / h
+            max_wh_ratio = max(max_wh_ratio, wh_ratio)
+        _, w, h = self.ocr_reader.resize_norm_img(img_list[0],
+                                                  max_wh_ratio).shape
+
+        imgs = np.zeros((boxes_num_in_one_batch, 3, w, h)).astype('float32')
+        for id, img in enumerate(img_list):
+            norm_img = self.ocr_reader.resize_norm_img(img, max_wh_ratio)
+            imgs[id] = norm_img
+        feed = {"x": imgs.copy()}
+        feed_list.append(feed)
+
+        return feed_list, False, None, ""
+```
+
+
+**三. 单机多卡推理**
+
+单机多卡推理与 `config.yml` 中配置4个参数关系紧密，`is_thread_op`、`concurrency`、`device_type` 和 `devices`，必须在进程模型和 GPU 模式，每张卡上可分配多个进程，即 M 个 Op 进程与 N 个 GPU 卡绑定。
+```
+dag:
+    #op资源类型, True, 为线程模型；False，为进程模型
+    is_thread_op: False
+
+op:
+    det:
+        #并发数，is_thread_op=True时，为线程并发；否则为进程并发
+        concurrency: 6
+
+        #当op配置没有server_endpoints时，从local_service_conf读取本地服务配置
+        local_service_conf:
+
+            client_type: local_predictor
+
+            # device_type, 0=cpu, 1=gpu, 2=tensorRT, 3=arm cpu, 4=kunlun xpu
+            device_type: 0
+
+            # 计算硬件 ID，当 devices 为""或不写时为 CPU 预测；当 devices 为"0", "0,1,2"时为 GPU 预测，表示使用的 GPU 卡
+            devices: "0,1,2"
+```
+以上述案例为例，`concurrency:6`，即启动6个进程，`devices:0,1,2`，根据轮询分配机制，得到如下绑定关系：
+
 - 进程ID: 0 绑定 GPU 卡0
 - 进程ID: 1 绑定 GPU 卡1
 - 进程ID: 2 绑定 GPU 卡2
@@ -74,49 +216,141 @@ Pipeline 支持批量推理，通过增大 batch size 可以提高 GPU 利用率
 - 进程ID: 5 绑定 GPU 卡2
 - 进程ID: 6 绑定 GPU 卡0
 
-`config.yml` 中硬件配置：
-```
-#计算硬件 ID，当 devices 为""或不写时为 CPU 预测；当 devices 为"0", "0,1,2"时为 GPU 预测，表示使用的 GPU 卡
-devices: "0,1,2"
-```
+对于更灵活的进程与 GPU 卡绑定方式，会持续开发。
 
 
-** 四. 多种计算芯片上推理 **
+**四. 多种计算芯片上推理**
 
-Pipeline 除了支持 CPU、GPU 芯片推理之外，还支持在多种计算硬件推理部署。在 `config.yml` 中由 `device_type` 和 `devices`。优先使用 `device_type` 指定类型，当空缺时根据 `devices` 判断。`device_type` 描述如下：
+除了支持 CPU、GPU 芯片推理之外，Python Pipeline 还支持在多种计算硬件上推理。根据 `config.yml` 中的 `device_type` 和 `devices`来设置推理硬件和加速库如下：
 - CPU(Intel) : 0
-- GPU(Jetson/海光DCU) : 1
+- GPU(GPU / Jetson / 海光 DCU) : 1
 - TensorRT : 2
 - CPU(Arm) : 3
 - XPU : 4
 - Ascend310 : 5 
 - ascend910 : 6
 
-config.yml中硬件配置：
+当不设置`device_type`时，根据 `devices` 来设置，即当 `device_type` 为 "" 或空缺时为 CPU 推理；当有设定如"0,1,2"时，为 GPU 推理，并指定 GPU 卡。
+
+以使用 GPU 的编号为0和1号卡并开启 TensorRT 为例，TensorRT 要配合 `ir_optim` 一同开启，`config.yml`详细配置如下：
 ```
-#计算硬件类型: 空缺时由devices决定(CPU/GPU)，0=cpu, 1=gpu, 2=tensorRT, 3=arm cpu, 4=kunlun xpu
-device_type: 0
+# 计算硬件类型
+device_type: 2
+
+# 计算硬件ID，优先由device_type决定硬件类型
+devices: "0,1"
+
+# 开启ir优化
+ir_optim: True
 
-#计算硬件ID，优先由device_type决定硬件类型。devices为""或空缺时为CPU预测；当为"0", "0,1,2"时为GPU预测，表示使用的GPU卡
-devices: "" # "0,1"
 ```
            
-** 五. 低精度推理 **
+**五. 低精度推理**
 Pipeline Serving支持低精度推理，CPU、GPU和TensoRT支持的精度类型如下图所示：
 
-- CPU
-  - fp32(default)
-  - fp16
-  - bf16(mkldnn)
-- GPU
-  - fp32(default)
-  - fp16
-  - int8
-- Tensor RT
-  - fp32(default)
-  - fp16
-  - int8 
-
-使用int8时，要开启use_calib: True
-
-参考[simple_web_service](../../examples/Pipeline/simple_web_service)示例
+
+
+低精度推理需要有量化模型，配合`config.yml`配置一起使用，以[低精度示例]() 为例
+
+1. CPU 低精度推理配置
+
+通过设置，`device_type` 和 `devices` 字段使用 CPU 推理，通过调整`precision`、`thread_num`和`use_mkldnn`参数选择低精度和性能调优。
+
+```
+op:
+    imagenet:
+        #并发数，is_thread_op=True时，为线程并发；否则为进程并发
+        concurrency: 1
+
+        #当op配置没有server_endpoints时，从local_service_conf读取本地服务配置
+        local_service_conf:
+
+            #uci模型路径
+            model_config: serving_server/
+
+            #计算硬件类型: 空缺时由devices决定(CPU/GPU)，0=cpu, 1=gpu, 2=tensorRT, 3=arm cpu, 4=kunlun xpu
+            device_type: 0
+
+            #计算硬件ID，当devices为""或不写时为CPU预测；当devices为"0", "0,1,2"时为GPU预测，表示使用的GPU卡
+            devices: ""
+
+            #client类型，包括brpc, grpc和local_predictor.local_predictor不启动Serving服务，进程内预测
+            client_type: local_predictor
+
+            #Fetch结果列表，以client_config中fetch_var的alias_name为准
+            fetch_list: ["score"]
+
+            #精度，CPU 支持: "fp32"(default), "bf16"(mkldnn); 不支持: "int8"
+            precision: "bf16"
+
+            #CPU 算数计算线程数，默认4线程
+            thread_num: 10
+
+            #开启 MKLDNN
+            use_mkldnn: True
+```
+
+2. GPU + TensorRT 低精度推理
+
+通过设置，`device_type` 和 `devices` 字段使用原生 GPU 或 TensorRT 推理，通过调整`precision`、`ir_optim`和`use_calib`参数选择低精度和性能调优，如开启 TensorRT，必须一同开启`ir_optim`，`use_calib`仅配合 int8 使用。
+```
+op:
+    imagenet:
+        #并发数，is_thread_op=True时，为线程并发；否则为进程并发
+        concurrency: 1
+
+        #当op配置没有server_endpoints时，从local_service_conf读取本地服务配置
+        local_service_conf:
+
+            #uci模型路径
+            model_config: serving_server/
+
+            #计算硬件类型: 空缺时由devices决定(CPU/GPU)，0=cpu, 1=gpu, 2=tensorRT, 3=arm cpu, 4=kunlun xpu
+            device_type: 2
+
+            #计算硬件ID，当devices为""或不写时为CPU预测；当devices为"0", "0,1,2"时为GPU预测，表示使用的GPU卡
+            devices: "1" # "0,1"
+
+            #client类型，包括brpc, grpc和local_predictor.local_predictor不启动Serving服务，进程内预测
+            client_type: local_predictor
+
+            #Fetch结果列表，以client_config中fetch_var的alias_name为准
+            fetch_list: ["score"]
+
+            #精度，GPU 支持: "fp32"(default), "fp16", "int8"
+            precision: "int8"
+
+            #开启 TensorRT int8 calibration
+            use_calib: True
+
+            #开启 ir_optim
+            ir_optim: True
+```
+
+
+3. 性能测试
+
+测试环境如下：
+- GPU 型号: A100-40GB
+- CPU 型号: Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz * 160
+- CUDA: CUDA Version: 11.2
+- CuDNN: 8.0
+
+测试方法：
+- 模型: Resnet50 量化模型
+- 部署方法: Python Pipeline 部署
+- 计时方法: 刨除第一次运行初始化，运行100次计算平均值
+
+在此环境下测试不同精度推理结果，GPU 推理性能较好的配置是
+- GPU + int8 + ir_optim + TensorRT + use_calib : 15.1 ms
+- GPU + fp16 + ir_optim + TensorRT : 17.2 ms
+
+
+CPU 推理性能较好的配置是
+- CPU + bf16 + MKLDNN : 18.2 ms
+- CPU + fp32 + thread_num=10 : 18.4 ms
+
+完整性能指标如下：
+<div align=center>
+<img src='../images/low_precision_profile.png' height = "600" align="middle"/>
+</div

examples/Pipeline/LowPrecision/ResNet50_Slim/README.md

+# Imagenet Pipeline WebService
+
+This document will takes Imagenet service as an example to introduce how to use Pipeline WebService.
+
+## Get model
+```
+wget https://paddle-inference-dist.bj.bcebos.com/inference_demo/python/resnet50/ResNet50_quant.tar.gz
+tar zxvf ResNet50_quant.tar.gz
+```
+
+## Start server
+
+```
+python3 resnet50_web_service.py &>log.txt &
+```
+
+## RPC test
+```
+python3 pipeline_rpc_client.py
+```

examples/Pipeline/LowPrecision/ResNet50_Slim/README_CN.md

+# Imagenet Pipeline WebService
+
+这里以 Imagenet 服务为例来介绍 Pipeline WebService 的使用。
+
+## 获取模型
+```
+wget https://paddle-inference-dist.bj.bcebos.com/inference_demo/python/resnet50/ResNet50_quant.tar.gz
+tar zxvf ResNet50_quant.tar.gz
+```
+
+## 启动服务
+
+```
+python3 resnet50_web_service.py &>log.txt &
+```
+
+## 测试
+```
+python3 pipeline_rpc_client.py
+```

examples/Pipeline/LowPrecision/ResNet50_Slim/benchmark.py

+# Copyright (c) 2021 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
+import os
+import base64
+import yaml
+import requests
+import time
+import json
+
+from paddle_serving_server.pipeline import PipelineClient
+import numpy as np
+from paddle_serving_client.utils import MultiThreadRunner
+from paddle_serving_client.utils import benchmark_args, show_latency
+
+
+def parse_benchmark(filein, fileout):
+    with open(filein, "r") as fin:
+        res = yaml.load(fin, yaml.FullLoader)
+        del_list = []
+        for key in res["DAG"].keys():
+            if "call" in key:
+                del_list.append(key)
+        for key in del_list:
+            del res["DAG"][key]
+    with open(fileout, "w") as fout:
+        yaml.dump(res, fout, default_flow_style=False)
+
+
+def gen_yml(device, gpu_id):
+    fin = open("config.yml", "r")
+    config = yaml.load(fin, yaml.FullLoader)
+    fin.close()
+    config["dag"]["tracer"] = {"interval_s": 10}
+    if device == "gpu":
+        config["op"]["imagenet"]["local_service_conf"]["device_type"] = 1
+        config["op"]["imagenet"]["local_service_conf"]["devices"] = gpu_id
+    else:
+        config["op"]["imagenet"]["local_service_conf"]["device_type"] = 0
+    with open("config2.yml", "w") as fout:
+        yaml.dump(config, fout, default_flow_style=False)
+
+
+def cv2_to_base64(image):
+    return base64.b64encode(image).decode('utf8')
+
+
+def run_http(idx, batch_size):
+    print("start thread ({})".format(idx))
+    url = "http://127.0.0.1:18000/imagenet/prediction"
+    start = time.time()
+
+    with open(os.path.join(".", "daisy.jpg"), 'rb') as file:
+        image_data1 = file.read()
+    image = cv2_to_base64(image_data1)
+    keys, values = [], []
+    for i in range(batch_size):
+        keys.append("image_{}".format(i))
+        values.append(image)
+    data = {"key": keys, "value": values}
+    latency_list = []
+    start_time = time.time()
+    total_num = 0
+    while True:
+        l_start = time.time()
+        r = requests.post(url=url, data=json.dumps(data))
+        print(r.json())
+        l_end = time.time()
+        latency_list.append(l_end * 1000 - l_start * 1000)
+        total_num += 1
+        if time.time() - start_time > 20:
+            break
+    end = time.time()
+    return [[end - start], latency_list, [total_num]]
+
+
+def multithread_http(thread, batch_size):
+    multi_thread_runner = MultiThreadRunner()
+    start = time.time()
+    result = multi_thread_runner.run(run_http, thread, batch_size)
+    end = time.time()
+    total_cost = end - start
+    avg_cost = 0
+    total_number = 0
+    for i in range(thread):
+        avg_cost += result[0][i]
+        total_number += result[2][i]
+    avg_cost = avg_cost / thread
+    print("Total cost: {}s".format(total_cost))
+    print("Each thread cost: {}s. ".format(avg_cost))
+    print("Total count: {}. ".format(total_number))
+    print("AVG QPS: {} samples/s".format(batch_size * total_number /
+                                         total_cost))
+    show_latency(result[1])
+
+
+def run_rpc(thread, batch_size):
+    client = PipelineClient()
+    client.connect(['127.0.0.1:18080'])
+    start = time.time()
+    test_img_dir = "imgs/"
+    for img_file in os.listdir(test_img_dir):
+        with open(os.path.join(test_img_dir, img_file), 'rb') as file:
+            image_data = file.read()
+        image = cv2_to_base64(image_data)
+        start_time = time.time()
+        while True:
+            ret = client.predict(feed_dict={"image": image}, fetch=["res"])
+            if time.time() - start_time > 10:
+                break
+    end = time.time()
+    return [[end - start]]
+
+
+def multithread_rpc(thraed, batch_size):
+    multi_thread_runner = MultiThreadRunner()
+    result = multi_thread_runner.run(run_rpc, thread, batch_size)
+
+
+if __name__ == "__main__":
+    if sys.argv[1] == "yaml":
+        mode = sys.argv[2]  # brpc/  local predictor
+        thread = int(sys.argv[3])
+        device = sys.argv[4]
+        if device == "gpu":
+            gpu_id = sys.argv[5]
+        else:
+            gpu_id = None
+        gen_yml(device, gpu_id)
+    elif sys.argv[1] == "run":
+        mode = sys.argv[2]  # http/ rpc
+        thread = int(sys.argv[3])
+        batch_size = int(sys.argv[4])
+        if mode == "http":
+            multithread_http(thread, batch_size)
+        elif mode == "rpc":
+            multithread_rpc(thread, batch_size)
+    elif sys.argv[1] == "dump":
+        filein = sys.argv[2]
+        fileout = sys.argv[3]
+        parse_benchmark(filein, fileout)

examples/Pipeline/LowPrecision/ResNet50_Slim/benchmark.sh

+export FLAGS_profile_pipeline=1
+alias python3="python3.6"
+modelname="clas-ResNet_v2_50"
+
+# HTTP
+#ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
+sleep 3
+# Create yaml，If you already have the config.yaml, ignore it.
+#python3 benchmark.py yaml local_predictor 1 gpu
+rm -rf profile_log_$modelname
+
+echo "Starting HTTP Clients..."
+# Start a client in each thread, tesing the case of multiple threads.
+for thread_num in 1 2 4 8 12 16
+do
+  for batch_size in 1
+  do
+    echo "----${modelname} thread num: ${thread_num} batch size: ${batch_size} mode:http ----" >>profile_log_$modelname
+    # Start one web service, If you start the service yourself, you can ignore it here.
+    #python3 web_service.py >web.log 2>&1 &
+    #sleep 3
+
+    # --id is the serial number of the GPU card, Must be the same as the gpu id used by the server.
+    nvidia-smi --id=3 --query-gpu=memory.used --format=csv -lms 1000 > gpu_use.log 2>&1 &
+    nvidia-smi --id=3 --query-gpu=utilization.gpu --format=csv -lms 1000 > gpu_utilization.log 2>&1 &
+    echo "import psutil\ncpu_utilization=psutil.cpu_percent(1,False)\nprint('CPU_UTILIZATION:', cpu_utilization)\n" > cpu_utilization.py
+    # Start http client
+    python3 benchmark.py run http $thread_num $batch_size > profile 2>&1
+
+    # Collect CPU metrics, Filter data that is zero momentarily, Record the maximum value of GPU memory and the average value of GPU utilization
+    python3 cpu_utilization.py >> profile_log_$modelname
+    grep -av '^0 %' gpu_utilization.log > gpu_utilization.log.tmp
+    awk 'BEGIN {max = 0} {if(NR>1){if ($modelname > max) max=$modelname}} END {print "MAX_GPU_MEMORY:", max}' gpu_use.log >> profile_log_$modelname
+    awk -F' ' '{sum+=$1} END {print "GPU_UTILIZATION:", sum/NR, sum, NR }' gpu_utilization.log.tmp >> profile_log_$modelname
+
+    # Show profiles
+    python3 ../../../util/show_profile.py profile $thread_num >> profile_log_$modelname
+    tail -n 8 profile >> profile_log_$modelname
+    echo '' >> profile_log_$modelname
+  done
+done
+
+# Kill all nvidia-smi background task.
+pkill nvidia-smi

examples/Pipeline/LowPrecision/ResNet50_Slim/config.yml

+#worker_num, 最大并发数。当build_dag_each_worker=True时, 框架会创建worker_num个进程，每个进程内构建grpcSever和DAG
+##当build_dag_each_worker=False时，框架会设置主线程grpc线程池的max_workers=worker_num
+worker_num: 1
+
+#http端口, rpc_port和http_port不允许同时为空。当rpc_port可用且http_port为空时，不自动生成http_port
+http_port: 18080
+rpc_port: 9993
+
+dag:
+    #op资源类型, True, 为线程模型；False，为进程模型
+    is_thread_op: False
+op:
+    imagenet:
+        #并发数，is_thread_op=True时，为线程并发；否则为进程并发
+        concurrency: 1
+
+        #当op配置没有server_endpoints时，从local_service_conf读取本地服务配置
+        local_service_conf:
+
+            #uci模型路径
+            model_config: serving_server/
+
+            #计算硬件类型: 空缺时由devices决定(CPU/GPU)，0=cpu, 1=gpu, 2=tensorRT, 3=arm cpu, 4=kunlun xpu
+            device_type: 1
+
+            #计算硬件ID，当devices为""或不写时为CPU预测；当devices为"0", "0,1,2"时为GPU预测，表示使用的GPU卡
+            devices: "0" # "0,1"
+
+            #client类型，包括brpc, grpc和local_predictor.local_predictor不启动Serving服务，进程内预测
+            client_type: local_predictor
+
+            #Fetch结果列表，以client_config中fetch_var的alias_name为准
+            fetch_list: ["score"]
+
+            #precsion, 预测精度，降低预测精度可提升预测速度
+            #GPU 支持: "fp32"(default), "fp16", "int8"；
+            #CPU 支持: "fp32"(default), "fp16", "bf16"(mkldnn); 不支持: "int8"
+            precision: "fp32" 
+
+            #开启 TensorRT calibration
+            use_calib: True
+
+            #开启 ir_optim
+            ir_optim: True 

examples/Pipeline/LowPrecision/ResNet50_Slim/pipeline_http_client.py

+# Copyright (c) 2021 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 numpy as np
+import requests
+import json
+import cv2
+import base64
+import os
+
+
+def cv2_to_base64(image):
+    return base64.b64encode(image).decode('utf8')
+
+
+if __name__ == "__main__":
+    url = "http://127.0.0.1:18080/imagenet/prediction"
+    with open(os.path.join(".", "daisy.jpg"), 'rb') as file:
+        image_data1 = file.read()
+    image = cv2_to_base64(image_data1)
+    data = {"key": ["image"], "value": [image]}
+    for i in range(1):
+        r = requests.post(url=url, data=json.dumps(data))
+        print(r.json())

examples/Pipeline/LowPrecision/ResNet50_Slim/pipeline_rpc_client.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from paddle_serving_server.pipeline import PipelineClient
+import numpy as np
+import requests
+import json
+import cv2
+import base64
+import os
+
+client = PipelineClient()
+client.connect(['127.0.0.1:9993'])
+
+
+def cv2_to_base64(image):
+    return base64.b64encode(image).decode('utf8')
+
+
+with open("daisy.jpg", 'rb') as file:
+    image_data = file.read()
+image = cv2_to_base64(image_data)
+
+for i in range(1):
+    ret = client.predict(feed_dict={"image": image}, fetch=["label", "prob"])
+    print(ret)

examples/Pipeline/LowPrecision/ResNet50_Slim/resnet50_web_service.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import sys
+from paddle_serving_app.reader import Sequential, URL2Image, Resize, CenterCrop, RGB2BGR, Transpose, Div, Normalize, Base64ToImage
+from paddle_serving_server.web_service import WebService, Op
+import logging
+import numpy as np
+import base64, cv2
+
+
+class ImagenetOp(Op):
+    def init_op(self):
+        self.seq = Sequential([
+            Resize(256), CenterCrop(224), RGB2BGR(), Transpose((2, 0, 1)),
+            Div(255), Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225],
+                                True)
+        ])
+        self.label_dict = {}
+        label_idx = 0
+        with open("imagenet.label") as fin:
+            for line in fin:
+                self.label_dict[label_idx] = line.strip()
+                label_idx += 1
+
+    def preprocess(self, input_dicts, data_id, log_id):
+        (_, input_dict), = input_dicts.items()
+        batch_size = len(input_dict.keys())
+        imgs = []
+        for key in input_dict.keys():
+            data = base64.b64decode(input_dict[key].encode('utf8'))
+            data = np.fromstring(data, np.uint8)
+            im = cv2.imdecode(data, cv2.IMREAD_COLOR)
+            img = self.seq(im)
+            imgs.append(img[np.newaxis, :].copy())
+        input_imgs = np.concatenate(imgs, axis=0)
+        return {"image": input_imgs}, False, None, ""
+
+    def postprocess(self, input_dicts, fetch_dict, data_id, log_id):
+        score_list = fetch_dict["score"]
+        result = {"label": [], "prob": []}
+        for score in score_list:
+            score = score.tolist()
+            max_score = max(score)
+            result["label"].append(self.label_dict[score.index(max_score)]
+                                   .strip().replace(",", ""))
+            result["prob"].append(max_score)
+        result["label"] = str(result["label"])
+        result["prob"] = str(result["prob"])
+        return result, None, ""
+
+
+class ImageService(WebService):
+    def get_pipeline_response(self, read_op):
+        image_op = ImagenetOp(name="imagenet", input_ops=[read_op])
+        return image_op
+
+
+uci_service = ImageService(name="imagenet")
+uci_service.prepare_pipeline_config("config.yml")
+uci_service.run_service()