Merge pull request #1092 from wangjiawei04/doc_0.6

Doc Fix

README.md

@@ -53,7 +53,7 @@ We consider deploying deep learning inference service online to be a user-facing
 
 <h2 align="center">AIStudio Turorial</h2>
 
-Here we provide tutorial on AIStudio(Chinese Version) [AIStudio教程-Paddle Serving服务化部署框架](https://aistudio.baidu.com/aistudio/projectdetail/1550674)
+Here we provide tutorial on AIStudio(Chinese Version) [AIStudio教程-Paddle Serving服务化部署框架](https://www.paddlepaddle.org.cn/tutorials/projectdetail/1555945)
 
 The tutorial provides 
 <ul>

README_CN.md

@@ -53,7 +53,7 @@ Paddle Serving 旨在帮助深度学习开发者轻易部署在线预测服务
 
 <h2 align="center">教程</h2>
 
-Paddle Serving开发者为您提供了简单易用的[AIStudio教程-Paddle Serving服务化部署框架](https://aistudio.baidu.com/aistudio/projectdetail/1550674)
+Paddle Serving开发者为您提供了简单易用的[AIStudio教程-Paddle Serving服务化部署框架](https://www.paddlepaddle.org.cn/tutorials/projectdetail/1555945)
 
 教程提供了如下内容
 

doc/FAQ.md

@@ -12,11 +12,11 @@
 
 **A:** 在protobuf定feed_type和fetch_type编号与数据类型对应如下
 
-​     0-int64
+     0-int64
     
-​	  1-float32
+      1-float32
     
-​	  2-int32
+      2-int32
 
 #### Q: paddle-serving是否支持windows和Linux环境下的多线程调用 
 
@@ -37,6 +37,7 @@
 ## 安装问题
 
 #### Q: pip install安装whl包过程，报错信息如下：
+
 ```
 Collecting opencv-python
   Using cached opencv-python-4.3.0.38.tar.gz (88.0 MB)
@@ -69,9 +70,11 @@ Collecting opencv-python
       s = list(pattern)
   TypeError: 'NoneType' object is not iterable
 ```
+
 **A:** 指定opencv-python版本安装，pip install opencv-python==4.2.0.32，再安装whl包
 
 #### Q: pip3 install whl包过程报错信息如下：
+
 ```
     Complete output from command python setup.py egg_info:
     Found cython-generated files...
@@ -80,13 +83,16 @@ Collecting opencv-python
     ----------------------------------------
 Command "python setup.py egg_info" failed with error code 1 in /tmp/pip-install-taoxz02y/grpcio/
 ```
+
 **A:** 需要升级pip3，再重新执行安装命令。
+
 ```
 pip3 install --upgrade pip
 pip3 install --upgrade setuptools
 ```
 
 #### Q: 运行过程中报错，信息如下：
+
 ```
 Traceback (most recent call last):
   File "../../deploy/serving/test_client.py", line 18, in <module>
@@ -97,7 +103,9 @@ Traceback (most recent call last):
     from shapely.geometry import Polygon
 ImportError: No module named shapely.geometry
 ```
+
 **A:** 有2种方法，第一种通过pip/pip3安装shapely，第二种通过pip/pip3安装所有依赖组件。
+
 ```
 方法1：
 pip install shapely==1.7.0
@@ -116,7 +124,69 @@ pip install -r python/requirements.txt
 
 **A:** 没有安装JDK，或者JAVA_HOME路径配置错误（正确配置是JDK路径，常见错误配置成JRE路径，例如正确路径参考JAVA_HOME="/usr/lib/jvm/java-1.8.0-openjdk-1.8.0.262.b10-0.el7_8.x86_64/"）。Java JDK安装参考https://segmentfault.com/a/1190000015389941
 
+## 环境问题
+
+#### Q：使用过程中出现CXXABI错误。
+
+这个问题出现的原因是Python使用的gcc版本和Serving所需的gcc版本对不上。对于Docker用户，推荐使用[Docker容器](./RUN_IN_DOCKER_CN.md)，由于Docker容器内的Python版本与Serving在发布前都做过适配，这样就不会出现类似的错误。如果是其他开发环境，首先需要确保开发环境中具备GCC 8.2，如果没有gcc 8.2，参考安装方式
+
+```bash
+wget -q https://paddle-ci.gz.bcebos.com/gcc-8.2.0.tar.xz 
+tar -xvf gcc-8.2.0.tar.xz && \
+cd gcc-8.2.0 && \
+unset LIBRARY_PATH CPATH C_INCLUDE_PATH PKG_CONFIG_PATH CPLUS_INCLUDE_PATH INCLUDE && \
+./contrib/download_prerequisites && \
+cd .. && mkdir temp_gcc82 && cd temp_gcc82 && \
+../gcc-8.2.0/configure --prefix=/usr/local/gcc-8.2 --enable-threads=posix --disable-checking --disable-multilib && \
+make -j8 && make install
+cd .. && rm -rf temp_gcc82
+cp ${lib_so_6} ${lib_so_6}.bak  && rm -f ${lib_so_6} && 
+ln -s /usr/local/gcc-8.2/lib64/libgfortran.so.5 ${lib_so_5} && \
+ln -s /usr/local/gcc-8.2/lib64/libstdc++.so.6 ${lib_so_6} && \
+cp /usr/local/gcc-8.2/lib64/libstdc++.so.6.0.25 ${lib_path}
+```
+
+假如已经有了GCC 8.2，可以自行安装Python，此外我们也提供了两个GCC 8.2编译的[Python2.7](https://paddle-serving.bj.bcebos.com/others/Python2.7.17-gcc82.tar) 和 [Python3.6](https://paddle-serving.bj.bcebos.com/others/Python3.6.10-gcc82.tar) 。下载解压后，需要将对应的目录设置为`PYTHONROOT`，并设置`PATH`和`LD_LIBRARY_PATH`。
+
+```bash
+export PYTHONROOT=/path/of/python # 对应解压后的Python目录
+export PATH=$PYTHONROOT/bin:$PATH
+export LD_LIBRARY_PATH=$PYTHONROOT/lib:$LD_LIBRARY_PATH
+```
+
+#### Q：遇到libstdc++.so.6的版本不够的问题
 
+触发该问题的原因在于，编译Paddle Serving相关可执行程序和动态库，所采用的是GCC 8.2(Cuda 9.0和10.0的Server可执行程序受限Cuda兼容性采用GCC 4.8编译)。Python在调用的过程中，有可能链接到了其他GCC版本的 `libstdc++.so`。 需要做的就是受限确保所在环境具备GCC 8.2，其次将GCC8.2的`libstdc++.so.*`拷贝到某个目录例如`/home/libstdcpp`下。最后`export LD_LIBRARY_PATH=/home/libstdcpp:$LD_LIBRARY_PATH` 即可。
+
+#### Q: 遇到OPENSSL_1.0.1EC 符号找不到的问题。
+
+目前Serving的可执行程序和客户端动态库需要链接1.0.2k版本的openssl动态库。如果环境当中没有，可以执行
+
+```bash
+wget https://paddle-serving.bj.bcebos.com/others/centos_ssl.tar && \
+    tar xf centos_ssl.tar && rm -rf centos_ssl.tar && \
+    mv libcrypto.so.1.0.2k /usr/lib/libcrypto.so.1.0.2k && mv libssl.so.1.0.2k /usr/lib/libssl.so.1.0.2k && \
+    ln -sf /usr/lib/libcrypto.so.1.0.2k /usr/lib/libcrypto.so.10 && \
+    ln -sf /usr/lib/libssl.so.1.0.2k /usr/lib/libssl.so.10 && \
+    ln -sf /usr/lib/libcrypto.so.10 /usr/lib/libcrypto.so && \
+    ln -sf /usr/lib/libssl.so.10 /usr/lib/libssl.so
+```
+
+其中`/usr/lib` 可以换成其他目录，并确保该目录在`LD_LIBRARY_PATH`下。
+
+### GPU相关环境问题
+
+#### Q：需要做哪些检查确保Serving可以运行在GPU环境
+
+**注：如果是使用Serving提供的镜像不需要做下列检查，如果是其他开发环境可以参考以下指导。**
+
+首先需要确保`nvidia-smi`可用，其次需要确保所需的动态库so文件在`LD_LIBRARY_PATH`所在的目录（包括系统lib库）。
+
+（1）Cuda显卡驱动：文件名通常为 `libcuda.so.$DRIVER_VERSION` 例如驱动版本为440.10.15，文件名就是`libcuda.so.440.10.15`。
+
+（2）Cuda和Cudnn动态库：文件名通常为 `libcudart.so.$CUDA_VERSION`，和 `libcudnn.so.$CUDNN_VERSION`。例如Cuda9就是 `libcudart.so.9.0`，Cudnn7就是 `libcudnn.so.7`。Cuda和Cudnn与Serving的版本匹配参见[Serving所有镜像列表](DOCKER_IMAGES_CN.md#%E9%99%84%E5%BD%95%E6%89%80%E6%9C%89%E9%95%9C%E5%83%8F%E5%88%97%E8%A1%A8).
+
+  (3) Cuda10.1及更高版本需要TensorRT。安装TensorRT相关文件的脚本参考 [install_trt.sh](../tools/dockerfile/build_scripts/install_trt.sh).
 
 ## 部署问题
 
@@ -154,7 +224,7 @@ InvalidArgumentError: Device id must be less than GPU count, but received id is:
 
 **A:**:1)使用[GPU docker](https://github.com/PaddlePaddle/Serving/blob/develop/doc/RUN_IN_DOCKER.md#gpunvidia-docker)解决环境问题
 
-​	   2)修改anaconda的虚拟环境下安装的python的gcc版本[参考](https://www.jianshu.com/p/c498b3d86f77) 
+       2)修改anaconda的虚拟环境下安装的python的gcc版本[参考](https://www.jianshu.com/p/c498b3d86f77) 
 
 #### Q: paddle-serving是否支持本地离线安装 
 
@@ -221,9 +291,10 @@ client端的日志直接打印到标准输出。
 
 **A:** 1)警告是glog组件打印的，告知glog初始化之前日志打印在STDERR
 
-​	   2)一般采用GLOG_v方式启动服务同时设置日志级别。
+       2)一般采用GLOG_v方式启动服务同时设置日志级别。
 
 例如：
+
 ```
 GLOG_v=2 python -m paddle_serving_server.serve --model xxx_conf/ --port 9999 
 ```

python/examples/pipeline/simple_web_service/benchmark.py

+import sys
+import os
+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 gen_yml():
+    fin = open("config.yml", "r")
+    config = yaml.load(fin)
+    fin.close()
+    config["dag"]["tracer"] = {"interval_s": 5}
+    with open("config2.yml", "w") as fout: 
+        yaml.dump(config, fout, default_flow_style=False)
+
+def run_http(idx, batch_size):
+    print("start thread ({})".format(idx))
+    url = "http://127.0.0.1:18082/uci/prediction"    
+    start = time.time()
+    value = "0.0137, -0.1136, 0.2553, -0.0692, 0.0582, -0.0727, -0.1583, -0.0584, 0.6283, 0.4919, 0.1856, 0.0795, -0.0332"
+    all_value = ";".join([value for i in range(batch_size)])
+    data = {"key": ["x"], "value": [all_value]}
+    for i in range(1000):
+        r = requests.post(url=url, data=json.dumps(data))
+    print(r.json())
+    end = time.time()
+    return [[end - start]]
+
+def multithread_http(thread, batch_size):
+    multi_thread_runner = MultiThreadRunner()
+    result = multi_thread_runner.run(run_http , thread, batch_size)
+
+def run_rpc(thread, batch_size):
+    client = PipelineClient()
+    client.connect(['127.0.0.1:9998'])
+    value = "0.0137, -0.1136, 0.2553, -0.0692, 0.0582, -0.0727, -0.1583, -0.0584, 0.6283, 0.4919, 0.1856, 0.0795, -0.0332"
+    all_value = ";".join([value for i in range(batch_size)])
+    data = {"key": "x", "value": all_value}
+    for i in range(1000):
+        ret = client.predict(feed_dict={data["key"]: data["value"]}, fetch=["res"])
+    print(ret)
+
+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])
+        gen_yml()
+    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)
+
+    

python/examples/pipeline/simple_web_service/benchmark.sh

+# HTTP
+ps -ef | grep web_service | awk '{print $2}' | xargs kill -9 
+sleep 3
+python3 benchmark.py yaml local_predictor 1 
+
+for thread_num in 1 
+do
+for batch_size in 1
+do
+rm -rf PipelineServingLogs
+rm -rf cpu_utilization.py
+python3 web_service.py >web.log 2>&1 &
+sleep 3
+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
+echo "------------Fit a line pipeline benchmark (Thread: $thread_num) (BatchSize: $batch_size)"
+tail -n 25 PipelineServingLogs/pipeline.tracer 
+ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
+done
+done
+
+# RPC
+ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
+sleep 3
+python3 benchmark.py yaml local_predictor 1
+
+for thread_num in 1 
+do
+for batch_size in 1 
+do
+rm -rf PipelineServingLogs
+rm -rf cpu_utilization.py
+python3 web_service.py >web.log 2>&1 &
+sleep 3
+echo "import psutil\ncpu_utilization=psutil.cpu_percent(1,False)\nprint('CPU_UTILIZATION:', cpu_utilization)\n" > cpu_utilization.py
+python3 benchmark.py run rpc $thread_num $batch_size
+python3 cpu_utilization.py
+echo "------------Fit a line pipeline benchmark (Thread: $thread_num) (BatchSize: $batch_size)"
+tail -n 25 PipelineServingLogs/pipeline.tracer
+ps -ef | grep web_service | awk '{print $2}' | xargs kill -9
+done
+done

python/examples/pipeline/simple_web_service/web_service.py

@@ -25,32 +25,24 @@ _LOGGER = logging.getLogger()
 class UciOp(Op):
     def init_op(self):
         self.separator = ","
+        self.batch_separator = ";"
 
     def preprocess(self, input_dicts, data_id, log_id):
         (_, input_dict), = input_dicts.items()
         _LOGGER.error("UciOp::preprocess >>> log_id:{}, input:{}".format(
             log_id, input_dict))
-        x_value = input_dict["x"]
+        x_value = input_dict["x"].split(self.batch_separator)
+        x_lst = []
+        for x_val in x_value:
+            x_lst.append(np.array([float(x.strip()) for x in x_val.split(self.separator)]).reshape(1, 13))
+        input_dict["x"] = np.concatenate(x_lst, axis=0) 
         proc_dict = {}
-        if sys.version_info.major == 2:
-            if isinstance(x_value, (str, unicode)):
-                input_dict["x"] = np.array(
-                    [float(x.strip())
-                     for x in x_value.split(self.separator)]).reshape(1, 13)
-                _LOGGER.error("input_dict:{}".format(input_dict))
-        else:
-            if isinstance(x_value, str):
-                input_dict["x"] = np.array(
-                    [float(x.strip())
-                     for x in x_value.split(self.separator)]).reshape(1, 13)
-                _LOGGER.error("input_dict:{}".format(input_dict))
-
         return input_dict, False, None, ""
 
     def postprocess(self, input_dicts, fetch_dict, log_id):
         _LOGGER.info("UciOp::postprocess >>> log_id:{}, fetch_dict:{}".format(
             log_id, fetch_dict))
-        fetch_dict["price"] = str(fetch_dict["price"][0][0])
+        fetch_dict["price"] = str(fetch_dict["price"])
         return fetch_dict, None, ""
 
 
@@ -61,5 +53,5 @@ class UciService(WebService):
 
 
 uci_service = UciService(name="uci")
-uci_service.prepare_pipeline_config("config.yml")
+uci_service.prepare_pipeline_config("config2.yml")
 uci_service.run_service()