Merge branch 'v0.6.0' of https://github.com/PaddlePaddle/Serving into v0.6.0

doc/PADDLE_SERVING_ON_KUBERNETES.md

@@ -4,9 +4,9 @@ Paddle Serving在0.6.0版本开始支持在Kubenetes集群上部署，并提供
 
 ### 集群准备
 
-如果您还没有Kubenetes集群，我们推荐[购买并使用百度智能云CCE集群](). 如果是其他云服务商提供的集群，或者自行安装Kubenetes集群，请遵照对应的教程。
+如果您还没有Kubenetes集群，我们推荐[购买并使用百度智能云CCE集群](https://cloud.baidu.com/doc/CCE/index.html). 如果是其他云服务商提供的集群，或者自行安装Kubenetes集群，请遵照对应的教程。
 
-您还需要准备一个用于Kubenetes集群部署使用的镜像仓库，通常与云服务提供商绑定，如果您使用的是百度智能云的CCE集群，可以参照[百度智能云CCR镜像仓库使用方式]()。当然Docker Hub也可以作为镜像仓库，但是可能在部署时会出现下载速度慢的情况。
+您还需要准备一个用于Kubenetes集群部署使用的镜像仓库，通常与云服务提供商绑定，如果您使用的是百度智能云的CCE集群，可以参照[百度智能云CCR镜像仓库使用方式](https://cloud.baidu.com/doc/CCR/index.html)。当然Docker Hub也可以作为镜像仓库，但是可能在部署时会出现下载速度慢的情况。
 
 ### 环境准备
 

doc/SERVING_AUTH_DOCKER.md

+# 在Paddle Serving使用安全网关
+
+## 简介
+
+在之前的服务部署示例中，我们都从开发的角度切入，然而，在现实的生产环境中，仅仅提供一个能够预测的远端服务接口还远远不够。我们仍然要考虑以下不足。
+
+- 这个服务还不能以网关的形式提供，访问路径难以管理。
+- 这个服务接口不够安全，需要做相应的鉴权。
+- 这个服务接口不能够控制流量，无法合理利用资源。
+
+本文档的作用，就以 Uci 房价预测服务为例，来介绍如何强化预测服务API接口安全。API网关作为流量入口，对接口进行统一管理。但API网关可以提供流量加密和鉴权等安全功能。
+
+## Docker部署
+
+可以使用docker-compose来部署安全网关。这个示例的步骤就是 [部署本地Serving容器] - [部署本地安全网关] - [通过安全网关访问Serving]
+
+**注明：** docker-compose与docker不一样，它依赖于docker，一次可以部署多个docker容器，可以类比于本地版的kubenetes，docker-compose的教程请参考[docker-compose安装](https://docs.docker.com/compose/install/) 
+
+```shell
+docker-compose -f tools/auth/auth-serving-docker.yaml up -d
+```
+
+可以通过 `docker ps` 来查看启动的容器。
+
+```shell
+3035cf445029        pantsel/konga:next                                                              "/app/start.sh"          About an hour ago   Up About an hour             0.0.0.0:8005->1337/tcp                                                                               anquan_konga_1
+7ce3abee550c        registry.baidubce.com/serving_gateway/kong:paddle                               "/docker-entrypoint.…"   About an hour ago   Up About an hour (healthy)   0.0.0.0:8000->8000/tcp, 127.0.0.1:8001->8001/tcp, 0.0.0.0:8443->8443/tcp, 127.0.0.1:8444->8444/tcp   anquan_kong_1
+25810fd79a27        postgres:9.6                                                                    "docker-entrypoint.s…"   About an hour ago   Up About an hour (healthy)   5432/tcp                                                                                             anquan_db_1
+ee59a3dd4806        registry.baidubce.com/serving_dev/serving-runtime:cpu-py36                      "bash -c ' wget --no…"   About an hour ago   Up About an hour             0.0.0.0:9393->9393/tcp                                                                               anquan_serving_1
+665fd8a34e15        redis:latest                                                                    "docker-entrypoint.s…"   About an hour ago   Up About an hour             0.0.0.0:6379->6379/tcp                                                                               anquan_redis_1 
+```
+
+其中我们之前serving容器 以 9393端口暴露，KONG网关的端口是8443， KONG的Web控制台的端口是8001。接下来我们在浏览器访问 `https://$IP_ADDR:8001`, 其中 IP_ADDR就是宿主机的IP。
+
+<img src="kong-dashboard.png">
+可以看到在注册结束后，登陆，看到了 DASHBOARD，我们先看SERVICES，可以看到`serving_service`，这意味着我们端口在9393的Serving服务已经在KONG当中被注册。
+
+<img src="kong-services.png">
+<img src="kong-routes.png">
+
+然后在ROUTES中，我们可以看到 serving 被链接到了 `/serving-uci`。
+
+最后我们点击 CONSUMERS - default_user - Credentials - API KEYS ，我们可以看到 `Api Keys` 下看到很多key
+
+<img src="kong-api_keys.png">
+
+接下来可以通过curl访问
+
+```shell
+ curl -H "Content-Type:application/json" -H "X-INSTANCE-ID:kong_ins" -H "apikey:hP6v25BQVS5CcS1nqKpxdrFkUxze9JWD" -X POST -d '{"feed":[{"x": [0.0137, -0.1136, 0.2553, -0.0692, 0.0582, -0.0727, -0.1583, -0.0584, 0.6283, 0.4919, 0.1856, 0.0795, -0.0332]}], "fetch":["price"]}' https://127.0.0.1:8443/serving-uci/uci/prediction -k
+```
+
+与之前的Serving HTTP服务相比，有以下区别。
+
+- 使用https加密访问，而不是http
+- 使用serving_uci的路径映射到网关
+- 在header处增加了 `X-INSTANCE-ID`和`apikey`
+
+
+## K8S部署
+
+同样，我们也提供了K8S集群部署Serving安全网关的方式。
+
+### Step 1：启动Serving服务
+
+我们仍然以 [Uci房价预测](../python/examples/fit_a_line)服务作为例子，这里省略了镜像制作的过程，详情可以参考 [在Kubernetes集群上部署Paddle Serving](./PADDLE_SERVING_ON_KUBERNETES.md)。
+
+在这里我们直接执行 
+```
+kubectl apply -f tools/auth/serving-demo-k8s.yaml
+```
+
+可以看到
+
+### Step 2: 安装 KONG (一个集群只需要执行一次就可以)
+接下来我们执行KONG Ingress的安装
+```
+kubectl apply -f tools/auth/kong-install.yaml
+```
+
+输出是
+```
+namespace/kong created
+customresourcedefinition.apiextensions.k8s.io/kongclusterplugins.configuration.konghq.com created
+customresourcedefinition.apiextensions.k8s.io/kongconsumers.configuration.konghq.com created
+customresourcedefinition.apiextensions.k8s.io/kongingresses.configuration.konghq.com created
+customresourcedefinition.apiextensions.k8s.io/kongplugins.configuration.konghq.com created
+customresourcedefinition.apiextensions.k8s.io/tcpingresses.configuration.konghq.com created
+serviceaccount/kong-serviceaccount created
+clusterrole.rbac.authorization.k8s.io/kong-ingress-clusterrole created
+clusterrolebinding.rbac.authorization.k8s.io/kong-ingress-clusterrole-nisa-binding created
+service/kong-proxy created
+service/kong-validation-webhook created
+deployment.apps/ingress-kong created
+```
+我们可以输入
+```
+kubectl get service --all-namespaces 
+```
+会显示
+```
+NAMESPACE     NAME                      TYPE        CLUSTER-IP       EXTERNAL-IP   PORT(S)                    AGE
+default       uci                       ClusterIP   172.16.87.89     <none>        9393/TCP                   7d7h
+kong          kong-proxy                NodePort    172.16.23.91     <none>        80:8175/TCP,443:8521/TCP   102m
+kong          kong-validation-webhook   ClusterIP   172.16.114.93    <none>        443/TCP                    102m
+
+```
+
+### Step 3: 创建Ingress资源
+
+接下来需要做Serving服务和KONG的链接
+
+```
+kubectl apply -f tools/auth/kong-ingress-k8s.yaml
+```
+
+我们也给出yaml文件内容
+```
+apiVersion: extensions/v1beta1
+kind: Ingress
+metadata:
+  name: demo
+  annotations:
+    konghq.com/strip-path: "true"
+    kubernetes.io/ingress.class: kong
+spec:
+  rules:
+  - http:
+      paths:
+      - path: /foo
+        backend:
+          serviceName: {{SERVING_SERVICE_NAME}}
+          servicePort: {{SERVICE_PORT}}
+```
+其中serviceName就是uci，servicePort就是9393，如果是别的服务就需要改这两个字段，最终会映射到`/foo`下。
+在这一步之后，我们就可以 
+```
+curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"x": [0.0137, -0.1136, 0.2553, -0.0692, 0.0582, -0.0727, -0.1583, -0.0584, 0.6283, 0.4919, 0.1856, 0.0795, -0.0332]}], "fetch":["price"]}' http://$IP:$PORT/foo/uci/prediction
+```
+
+### Step 4: 增加安全网关限制
+
+之前的接口没有鉴权功能，无法验证用户身份合法性，现在我们添加一个key-auth插件
+
+执行
+```
+kubectl apply -f key-auth-k8s.yaml
+```
+
+其中,yaml文内容为
+```
+apiVersion: configuration.konghq.com/v1
+kind: KongPlugin
+metadata:
+  name: key-auth
+plugin: key-auth
+```
+
+现在，需要创建secret，key值为用户指定，需要在请求时携带Header中apikey字段
+执行
+```
+kubectl create secret generic default-apikey  \
+   --from-literal=kongCredType=key-auth  \
+   --from-literal=key=ZGVmYXVsdC1hcGlrZXkK
+```
+
+在这里，我们的key是随意制定了一串 `ZGVmYXVsdC1hcGlrZXkK`，实际情况也可以
+创建一个用户（consumer）标识访问者身份，并未该用户绑定apikey。
+执行
+```
+kubectl apply -f kong-consumer-k8s.yaml
+```
+
+其中,yaml文内容为
+```
+apiVersion: configuration.konghq.com/v1
+kind: KongConsumer
+metadata:
+  name: default
+  annotations:
+    kubernetes.io/ingress.class: kong
+username: default
+credentials:
+- default-apikey
+```
+
+如果我们这时还想再像上一步一样的做curl访问，会发现已经无法访问，此时已经具备了安全能力，我们需要对应的key。
+
+
+### Step 5: 通过API Key访问服务
+
+执行
+```
+curl -H "Content-Type:application/json" -H "apikey:ZGVmYXVsdC1hcGlrZXkK" -X POST -d '{"feed":[{"x": [0.0137, -0.1136, 0.2553, -0.0692, 0.0582, -0.0727, -0.1583, -0.0584, 0.6283, 0.4919, 0.1856, 0.0795, -0.0332]}], "fetch":["price"]}' https://$IP:$PORT/foo/uci/prediction -k
+```
+我们可以看到 apikey 已经加入到了curl请求的header当中。
+
+
+

python/examples/pipeline/PaddleClas/DarkNet53/README.md

+# Imagenet Pipeline WebService
+
+This document will takes Imagenet service as an example to introduce how to use Pipeline WebService.
+
+## Get model
+```
+sh get_model.sh
+```
+
+## Start server
+
+```
+python resnet50_web_service.py &>log.txt &
+```
+
+## RPC test
+```
+python pipeline_rpc_client.py
+```

python/examples/pipeline/PaddleClas/DarkNet53/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
+```
+sh get_model.sh
+```
+
+## 启动服务
+
+```
+python resnet50_web_service.py &>log.txt &
+```
+
+## 测试
+```
+python pipeline_rpc_client.py
+```
+

python/examples/pipeline/PaddleClas/DarkNet53/benchmark.py

+import sys
+import os
+import base64
+import yaml
+import requests
+import time
+import json
+try:
+    from paddle_serving_server_gpu.pipeline import PipelineClient
+except ImportError:
+    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)
+        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)
+    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://127.0.0.1:18080/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)
+    

python/examples/pipeline/PaddleClas/DarkNet53/benchmark.sh

+export FLAGS_profile_pipeline=1
+alias python3="python3.6"
+modelname="clas-DarkNet53"
+
+# 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

python/examples/pipeline/PaddleClas/DarkNet53/benchmark_config.yaml

+
+cuda_version: "10.1"
+cudnn_version: "7.6"
+trt_version: "6.0"
+python_version: "3.7"
+gcc_version: "8.2"
+paddle_version: "2.0.1"
+
+cpu: "Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz X12"
+gpu: "T4"
+xpu: "None"
+api: ""
+owner: "cuicheng01"
+
+model_name: "DarkNet53"
+model_type: "static"
+model_source: "PaddleClas"
+model_url: "https://paddle-imagenet-models-name.bj.bcebos.com/DarkNet53_pretrained.tar"
+
+batch_size: 1
+num_of_samples: 1000
+input_shape: "3,224,224"
+
+runtime_device: "gpu"
+ir_optim: true
+enable_memory_optim: true
+enable_tensorrt: false
+precision: "fp32"
+enable_mkldnn: false
+cpu_math_library_num_threads: ""
+
+

python/examples/pipeline/PaddleClas/DarkNet53/benchmark_config.yaml.template

+
+cuda_version: "10.1"
+cudnn_version: "7.6"
+trt_version: "6.0"
+python_version: "3.7"
+gcc_version: "8.2"
+paddle_version: "2.0.1"
+
+cpu: "Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz X12"
+gpu: "T4"
+xpu: "None"
+api: ""
+owner: "cuicheng01"
+
+model_name: "imagenet"
+model_type: "static"
+model_source: "PaddleClas"
+model_url: "model_url_path"
+
+batch_size: 1
+num_of_samples: 1000
+input_shape: "3,224,224"
+
+runtime_device: "cpu"
+ir_optim: true
+enable_memory_optim: true
+enable_tensorrt: false
+precision: "fp32"
+enable_mkldnn: false
+cpu_math_library_num_threads: ""
+
+

python/examples/pipeline/PaddleClas/DarkNet53/benchmark_gpu.sh

+export FLAGS_profile_pipeline=1
+alias python3="python3.7"
+modelname="imagenet"
+use_gpu=1
+gpu_id="0"
+benchmark_config_filename="benchmark_config.yaml"
+
+# HTTP
+ps -ef | grep web_service | awk '{print $2}' | xargs kill -9 
+sleep 3
+if [ $use_gpu -eq 1 ]; then
+  python3 benchmark.py yaml local_predictor 1 gpu $gpu_id
+else
+  python3 benchmark.py yaml local_predictor 1 cpu
+fi
+rm -rf profile_log_$modelname
+for thread_num in 1
+do
+  for batch_size in 1
+  do
+    echo "#----imagenet thread num: $thread_num batch size: $batch_size mode:http use_gpu:$use_gpu----" >>profile_log_$modelname
+    rm -rf PipelineServingLogs
+    rm -rf cpu_utilization.py
+    python3 resnet50_web_service.py >web.log 2>&1 &
+    sleep 3
+    nvidia-smi --id=${gpu_id} --query-compute-apps=used_memory --format=csv -lms 100 > gpu_use.log 2>&1 &
+    nvidia-smi --id=${gpu_id} --query-gpu=utilization.gpu --format=csv -lms 100 > gpu_utilization.log 2>&1 &
+    echo "import psutil\ncpu_utilization=psutil.cpu_percent(1,False)\nprint('CPU_UTILIZATION:', cpu_utilization)\n" > cpu_utilization.py
+    python3 benchmark.py run http $thread_num $batch_size
+    python3 cpu_utilization.py >>profile_log_$modelname
+    python3 -m paddle_serving_server_gpu.profiler >>profile_log_$modelname
+    ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
+    ps -ef | grep nvidia-smi | awk '{print $2}' | xargs kill -9
+    python3 benchmark.py dump benchmark.log benchmark.tmp
+    mv benchmark.tmp benchmark.log
+    awk 'BEGIN {max = 0} {if(NR>1){if ($modelname > max) max=$modelname}} END {print "GPU_MEM:", max}' gpu_use.log >> profile_log_$modelname
+    awk 'BEGIN {max = 0} {if(NR>1){if ($modelname > max) max=$modelname}} END {print "GPU_UTIL:", max}' gpu_utilization.log >> profile_log_$modelname
+    cat benchmark.log >> profile_log_$modelname
+    python3 -m paddle_serving_server_gpu.parse_profile --benchmark_cfg $benchmark_config_filename --benchmark_log profile_log_$modelname
+    #rm -rf gpu_use.log gpu_utilization.log
+  done
+done

python/examples/pipeline/PaddleClas/DarkNet53/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: DarkNet53/ppcls_model/
+
+            #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: ["prediction"] 

python/examples/pipeline/PaddleClas/DarkNet53/cpu_utilization.py

+import psutil
+cpu_utilization=psutil.cpu_percent(1,False)
+print('CPU_UTILIZATION:', cpu_utilization)
+

python/examples/pipeline/PaddleClas/DarkNet53/get_model.sh

+wget --no-check-certificate https://paddle-serving.bj.bcebos.com/model/DarkNet53.tar
+tar -xf DarkNet53.tar
+
+wget --no-check-certificate https://paddle-serving.bj.bcebos.com/imagenet-example/image_data.tar.gz
+tar -xzvf image_data.tar.gz

python/examples/pipeline/PaddleClas/DarkNet53/pipeline_http_client.py

+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(100):
+        r = requests.post(url=url, data=json.dumps(data))
+        print(r.json())

python/examples/pipeline/PaddleClas/DarkNet53/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.
+try:
+    from paddle_serving_server_gpu.pipeline import PipelineClient
+except ImportError:
+    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)

python/examples/pipeline/PaddleClas/DarkNet53/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, log_id):
+        score_list = fetch_dict["prediction"]
+        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()

python/examples/pipeline/PaddleClas/HRNet_W18_C/README.md

+# Imagenet Pipeline WebService
+
+This document will takes Imagenet service as an example to introduce how to use Pipeline WebService.
+
+## Get model
+```
+sh get_model.sh
+```
+
+## Start server
+
+```
+python resnet50_web_service.py &>log.txt &
+```
+
+## RPC test
+```
+python pipeline_rpc_client.py
+```

python/examples/pipeline/PaddleClas/HRNet_W18_C/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
+```
+sh get_model.sh
+```
+
+## 启动服务
+
+```
+python resnet50_web_service.py &>log.txt &
+```
+
+## 测试
+```
+python pipeline_rpc_client.py
+```
+

python/examples/pipeline/PaddleClas/HRNet_W18_C/benchmark.py

+import sys
+import os
+import base64
+import yaml
+import requests
+import time
+import json
+try:
+    from paddle_serving_server_gpu.pipeline import PipelineClient
+except ImportError:
+    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)
+        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)
+    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://127.0.0.1:18080/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)
+    

python/examples/pipeline/PaddleClas/HRNet_W18_C/benchmark.sh

+export FLAGS_profile_pipeline=1
+alias python3="python3.6"
+modelname="clas-HRNet_W18_C"
+
+# 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

python/examples/pipeline/PaddleClas/HRNet_W18_C/benchmark_config.yaml

+
+cuda_version: "10.1"
+cudnn_version: "7.6"
+trt_version: "6.0"
+python_version: "3.7"
+gcc_version: "8.2"
+paddle_version: "2.0.1"
+
+cpu: "Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz X12"
+gpu: "T4"
+xpu: "None"
+api: ""
+owner: "cuicheng01"
+
+model_name: "HRNet_W18_C"
+model_type: "static"
+model_source: "PaddleClas"
+model_url: "https://paddle-imagenet-models-name.bj.bcebos.com/HRNet_W18_C_pretrained.tar"
+
+batch_size: 1
+num_of_samples: 1000
+input_shape: "3,224,224"
+
+runtime_device: "gpu"
+ir_optim: true
+enable_memory_optim: true
+enable_tensorrt: false
+precision: "fp32"
+enable_mkldnn: false
+cpu_math_library_num_threads: ""
+
+

python/examples/pipeline/PaddleClas/HRNet_W18_C/benchmark_config.yaml.template

+
+cuda_version: "10.1"
+cudnn_version: "7.6"
+trt_version: "6.0"
+python_version: "3.7"
+gcc_version: "8.2"
+paddle_version: "2.0.1"
+
+cpu: "Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz X12"
+gpu: "T4"
+xpu: "None"
+api: ""
+owner: "cuicheng01"
+
+model_name: "imagenet"
+model_type: "static"
+model_source: "PaddleClas"
+model_url: "model_url_path"
+
+batch_size: 1
+num_of_samples: 1000
+input_shape: "3,224,224"
+
+runtime_device: "cpu"
+ir_optim: true
+enable_memory_optim: true
+enable_tensorrt: false
+precision: "fp32"
+enable_mkldnn: false
+cpu_math_library_num_threads: ""
+
+

python/examples/pipeline/PaddleClas/HRNet_W18_C/benchmark_gpu.sh

+export FLAGS_profile_pipeline=1
+alias python3="python3.7"
+modelname="imagenet"
+use_gpu=1
+gpu_id="0"
+benchmark_config_filename="benchmark_config.yaml"
+
+# HTTP
+ps -ef | grep web_service | awk '{print $2}' | xargs kill -9 
+sleep 3
+if [ $use_gpu -eq 1 ]; then
+  python3 benchmark.py yaml local_predictor 1 gpu $gpu_id
+else
+  python3 benchmark.py yaml local_predictor 1 cpu
+fi
+rm -rf profile_log_$modelname
+for thread_num in 1
+do
+  for batch_size in 1
+  do
+    echo "#----imagenet thread num: $thread_num batch size: $batch_size mode:http use_gpu:$use_gpu----" >>profile_log_$modelname
+    rm -rf PipelineServingLogs
+    rm -rf cpu_utilization.py
+    python3 resnet50_web_service.py >web.log 2>&1 &
+    sleep 3
+    nvidia-smi --id=${gpu_id} --query-compute-apps=used_memory --format=csv -lms 100 > gpu_use.log 2>&1 &
+    nvidia-smi --id=${gpu_id} --query-gpu=utilization.gpu --format=csv -lms 100 > gpu_utilization.log 2>&1 &
+    echo "import psutil\ncpu_utilization=psutil.cpu_percent(1,False)\nprint('CPU_UTILIZATION:', cpu_utilization)\n" > cpu_utilization.py
+    python3 benchmark.py run http $thread_num $batch_size
+    python3 cpu_utilization.py >>profile_log_$modelname
+    python3 -m paddle_serving_server_gpu.profiler >>profile_log_$modelname
+    ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
+    ps -ef | grep nvidia-smi | awk '{print $2}' | xargs kill -9
+    python3 benchmark.py dump benchmark.log benchmark.tmp
+    mv benchmark.tmp benchmark.log
+    awk 'BEGIN {max = 0} {if(NR>1){if ($modelname > max) max=$modelname}} END {print "GPU_MEM:", max}' gpu_use.log >> profile_log_$modelname
+    awk 'BEGIN {max = 0} {if(NR>1){if ($modelname > max) max=$modelname}} END {print "GPU_UTIL:", max}' gpu_utilization.log >> profile_log_$modelname
+    cat benchmark.log >> profile_log_$modelname
+    python3 -m paddle_serving_server_gpu.parse_profile --benchmark_cfg $benchmark_config_filename --benchmark_log profile_log_$modelname
+    #rm -rf gpu_use.log gpu_utilization.log
+  done
+done

python/examples/pipeline/PaddleClas/HRNet_W18_C/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: HRNet_W18_C/ppcls_model/
+
+            #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: ["prediction"] 

python/examples/pipeline/PaddleClas/HRNet_W18_C/cpu_utilization.py

+import psutil
+cpu_utilization=psutil.cpu_percent(1,False)
+print('CPU_UTILIZATION:', cpu_utilization)
+

python/examples/pipeline/PaddleClas/HRNet_W18_C/get_model.sh

+wget --no-check-certificate https://paddle-serving.bj.bcebos.com/model/HRNet_W18_C.tar
+tar -xf HRNet_W18_C.tar
+
+wget --no-check-certificate https://paddle-serving.bj.bcebos.com/imagenet-example/image_data.tar.gz
+tar -xzvf image_data.tar.gz

python/examples/pipeline/PaddleClas/HRNet_W18_C/pipeline_http_client.py

+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(100):
+        r = requests.post(url=url, data=json.dumps(data))
+        print(r.json())

python/examples/pipeline/PaddleClas/HRNet_W18_C/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.
+try:
+    from paddle_serving_server_gpu.pipeline import PipelineClient
+except ImportError:
+    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)

python/examples/pipeline/PaddleClas/HRNet_W18_C/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
+try:
+    from paddle_serving_server_gpu.web_service import WebService, Op
+except ImportError:
+    from paddle_serving_server.web_service import WebService, Op
+import logging
+import numpy as np
+import 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, log_id):
+        score_list = fetch_dict["prediction"]
+        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()

python/examples/pipeline/PaddleClas/MobileNetV1/README.md

+# Imagenet Pipeline WebService
+
+This document will takes Imagenet service as an example to introduce how to use Pipeline WebService.
+
+## Get model
+```
+sh get_model.sh
+```
+
+## Start server
+
+```
+python resnet50_web_service.py &>log.txt &
+```
+
+## RPC test
+```
+python pipeline_rpc_client.py
+```

python/examples/pipeline/PaddleClas/MobileNetV1/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
+```
+sh get_model.sh
+```
+
+## 启动服务
+
+```
+python resnet50_web_service.py &>log.txt &
+```
+
+## 测试
+```
+python pipeline_rpc_client.py
+```
+

python/examples/pipeline/PaddleClas/MobileNetV1/benchmark.sh

+export FLAGS_profile_pipeline=1
+alias python3="python3.6"
+modelname="clas-MobileNetV1"
+
+# 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

python/examples/pipeline/PaddleClas/MobileNetV1/cpu_utilization.py

+import psutil
+cpu_utilization=psutil.cpu_percent(1,False)
+print('CPU_UTILIZATION:', cpu_utilization)
+