学习笔记TF022:产品环境模型部署、Docker镜像、Bazel工作区、导出模型、服务器、客户端

产品环境模型部署，创建简单Web APP，用户上传图像，运行Inception模型，实现图像自动分类。

搭建TensorFlow服务开发环境。安装Docker，https://docs.docker.com/engine/installation/ 。用配置文件在本地创建Docker镜像，docker build –pull -t USER/tensorflow−serving−develhttps://raw.githubusercontent.com/tensorflow/serving/master/tensorflowserving/tools/docker/Dockerfile.devel。镜像运行容器，dockerrun−vHOME:/mnt/home -p 9999:9999 -it $USER/tensorflow-serving-devel ，在home目录加载到容器/mnt/home路径，在终端工作。用IDE或编辑器编辑代码，用容器运行构建工具，主机通过9999端口访问，构建服务器。exit命令退出容器终端，停止运行。

TensorFlow服务程序C++写，使用Google的Bazel构建工具。容器运行Bazel。Bazel代码级管理第三方依赖项。Bazel自动下载构建。项目库根目录定义WORKSPACE文件。TensorFlow模型库包含Inception模型代码。

TensorFlow服务在项目作为Git子模块。mkdir ~/serving_example，cd ~/serving_example，git init，git submodule add https://github.com/tensorflow/serving.git ，tf_serving，git submodule update –init –recursive 。

WORKSPACE文件local_repository规则定义第三方依赖为本地存储文件。项目导入tf_workspace规则初始化TensorFlow依赖项。

 workspace(name = "serving") local_repository(     name = "tf_serving",     path = __workspace_dir__ + "/tf_serving", ) local_repository(     name = "org_tensorflow",     path = __workspace_dir__ + "/tf_serving/tensorflow", ) load('//tf_serving/tensorflow/tensorflow:workspace.bzl', 'tf_workspace') tf_workspace("tf_serving/tensorflow/", "@org_tensorflow") bind(     name = "libssl",     actual = "@boringssl_git//:ssl", ) bind(     name = "zlib",     actual = "@zlib_archive//:zlib", ) local_repository(     name = "inception_model",     path = __workspace_dir__ + "/tf_serving/tf_models/inception", )

导出训练好的模型，导出数据流图及变量，给产品用。模型数据流图，必须从占位符接收输入，单步推断计算输出。Inception模型(或一般图像识别模型)，JPEG编码图像字符串输入，与从TFRecord文件读取输入不同。定义输入占位符，调用函数转换占位符表示外部输入为原始推断模型输入格式，图像字符串转换为各分量位于[0, 1]内像素张量，缩放图像尺寸，符合模型期望宽度高度，像素值变换到模型要求区间[-1, 1]内。调用原始模型推断方法，依据转换输入推断结果。

推断方法各参数赋值。从检查点恢复参数值。周期性保存模型训练检查点文件，文件包含学习参数。最后一次保存训练检查点文件包含最后更新模型参数。下去载预训练检查点文件：http://download.tensorflow.org/models/imagenet/inception-v3-2016-03-01.tar.gz 。在Docker容器中，cd /tmp, curl -0 http://download.tensorflow.org/models/image/imagenet/inception-v3-2016-03-01.tar.gz, tar -xzf inception-v3-2016-03-01.tar.gz 。

tensorflow_serving.session_bundle.exporter.Exporter类导出模型。传入保存器实例创建实例，用exporter.classification_signature创建模型签名。指定input_tensor、输出张量。classes_tensor 包含输出类名称列表、模型分配各类别分值(或概率)socres_tensor。类别数多模型，配置指定仅返田大口tf.nntop_k选择类别，模型分配分数降序排列前K个类别。调用exporter.Exporter.init方法签名，export方法导出模型，接收输出路径、模型版本号、会话对象。Exporter类自动生成代码存在依赖，Doker容器内部使用中bazel运行导出器。代码保存到bazel工作区exporter.py。

 import time import sys import tensorflow as tf from tensorflow_serving.session_bundle import exporter from inception import inception_model NUM_CLASSES_TO_RETURN = 10 def convert_external_inputs(external_x):     image = tf.image.convert_image_dtype(tf.image.decode_jpeg(external_x, channels=3), tf.float32)     images = tf.image.resize_bilinear(tf.expand_dims(image, 0), [299, 299])     images = tf.mul(tf.sub(images, 0.5), 2)     return images def inference(images):     logits, _ = inception_model.inference(images, 1001)     return logits external_x = tf.placeholder(tf.string) x = convert_external_inputs(external_x) y = inference(x) saver = tf.train.Saver() with tf.Session() as sess:     ckpt = tf.train.get_checkpoint_state(sys.argv[1])     if ckpt and ckpt.model_checkpoint_path:         saver.restore(sess, sys.argv[1] + "/" + ckpt.model_checkpoint_path)     else:         print("Checkpoint file not found")         raise SystemExit     scores, class_ids = tf.nn.top_k(y, NUM_CLASSES_TO_RETURN)     classes = tf.contrib.lookup.index_to_string(tf.to_int64(class_ids),         mapping=tf.constant([str(i) for i in range(1001)]))     model_exporter = exporter.Exporter(saver)     signature = exporter.classification_signature(         input_tensor=external_x, classes_tensor=classes, scores_tensor=scores)     model_exporter.init(default_graph_signature=signature, init_op=tf.initialize_all_tables())     model_exporter.export(sys.argv[1] + "/export", tf.constant(time.time()), sess)

一个构建规则BUILD文件。在容器命令运行导出器，cd /mnt/home/serving_example, hazel run:export /tmp/inception-v3 ，依据/tmp/inception-v3提到的检查点文件在/tmp/inception-v3/{currenttimestamp}/创建导出器。首次运行要对TensorFlow编译。load从外部导入protobuf库，导入cc_proto_library规则定义，为proto文件定义构建规则。通过命令bazel run :server 9999 /tmp/inception-v3/export/{timestamp}，容器运行推断服务器。

 py_binary(     name = "export",     srcs = [         "export.py",     ],     deps = [         "@tf_serving//tensorflow_serving/session_bundle:exporter",         "@org_tensorflow//tensorflow:tensorflow_py",         "@inception_model//inception",     ], ) load("@protobuf//:protobuf.bzl", "cc_proto_library") cc_proto_library(     name="classification_service_proto",     srcs=["classification_service.proto"],     cc_libs = ["@protobuf//:protobuf"],     protoc="@protobuf//:protoc",     default_runtime="@protobuf//:protobuf",     use_grpc_plugin=1 ) cc_binary(     name = "server",     srcs = [         "server.cc",         ],     deps = [         ":classification_service_proto",         "@tf_serving//tensorflow_serving/servables/tensorflow:session_bundle_factory",         "@grpc//:grpc++",         ], )

定义服务器接口。TensorFlow服务使用gRPC协议(基于HTTP/2二进制协议)。支持创建服务器和自动生成客户端存根各种语言。在protocol buffer定义服务契约，用于gRPC IDL(接口定义语言)和二进制编码。接收JPEG编码待分类图像字符串输入，返回分数排列推断类别列表。定义在classification_service.proto文件。接收图像、音频片段、文字服务可用可一接口。proto编译器转换proto文件为客户端和服务器类定义。bazel build:classification_service_proto可行构建，通过bazel-genfiles/classification_service.grpc.pb.h检查结果。推断逻辑，ClassificationService::Service接口必须实现。检查bazel-genfiles/classification_service.pb.h查看request、response消息定义。proto定义变成每种类型C++接口。

 syntax = "proto3"; message ClassificationRequest {    // bytes input = 1;    float petalWidth = 1;    float petalHeight = 2;    float sepalWidth = 3;    float sepalHeight = 4; }; message ClassificationResponse {    repeated ClassificationClass classes = 1; }; message ClassificationClass {    string name = 1;    float score = 2; } service ClassificationService {    rpc classify(ClassificationRequest) returns (ClassificationResponse); }

实现推断服务器。加载导出模型，调用推断方法，实现ClassificationService::Service。导出模型，创建SessionBundle对象，包含完全加载数据流图TF会话对象，定义导出工具分类签名元数据。SessionBundleFactory类创建SessionBundle对象，配置为pathToExportFiles指定路径加载导出模型，返回创建SessionBundle实例unique指针。定义ClassificationServiceImpl，接收SessionBundle实例参数。

加载分类签名，GetClassificationSignature函数加载模型导出元数据ClassificationSignature，签名指定所接收图像真实名称的输入张量逻辑名称，以及数据流图输出张量逻辑名称映射推断结果。将protobuf输入变换为推断输入张量，request参数复制JPEG编码图像字符串到推断张量。运行推断，sessionbundle获得TF会话对象，运行一次，传入输入输出张量推断。推断输出张量变换protobuf输出，输出张量结果复制到ClassificationResponse消息指定形状response输出参数格式化。设置gRPC服务器，SessionBundle对象配置，创建ClassificationServiceImpl实例样板代码。

 #include <iostream> #include <memory> #include <string> #include <grpc++/grpc++.h> #include "classification_service.grpc.pb.h" #include "tensorflow_serving/servables/tensorflow/session_bundle_factory.h" using namespace std; using namespace tensorflow::serving; using namespace grpc; unique_ptr<SessionBundle> createSessionBundle(const string& pathToExportFiles) {    SessionBundleConfig session_bundle_config = SessionBundleConfig();    unique_ptr<SessionBundleFactory> bundle_factory;    SessionBundleFactory::Create(session_bundle_config, &bundle_factory);    unique_ptr<SessionBundle> sessionBundle;    bundle_factory->CreateSessionBundle(pathToExportFiles, &sessionBundle);    return sessionBundle; } class ClassificationServiceImpl final : public ClassificationService::Service {   private:    unique_ptr<SessionBundle> sessionBundle;   public:     ClassificationServiceImpl(unique_ptr<SessionBundle> sessionBundle) :    sessionBundle(move(sessionBundle)) {};    Status classify(ServerContext* context, const ClassificationRequest* request,                ClassificationResponse* response) override {       ClassificationSignature signature;       const tensorflow::Status signatureStatus =         GetClassificationSignature(sessionBundle->meta_graph_def, &signature);       if (!signatureStatus.ok()) {          return Status(StatusCode::INTERNAL, signatureStatus.error_message());       }       tensorflow::Tensor input(tensorflow::DT_STRING, tensorflow::TensorShape());       input.scalar<string>()() = request->input();       vector<tensorflow::Tensor> outputs;       const tensorflow::Status inferenceStatus = sessionBundle->session->Run(          {{signature.input().tensor_name(), input}},          {signature.classes().tensor_name(), signature.scores().tensor_name()},          {},          &outputs);       if (!inferenceStatus.ok()) {          return Status(StatusCode::INTERNAL, inferenceStatus.error_message());       }       for (int i = 0; i < outputs[0].NumElements(); ++i) {          ClassificationClass *classificationClass = response->add_classes();          classificationClass->set_name(outputs[0].flat<string>()(i));          classificationClass->set_score(outputs[1].flat<float>()(i));       }         return Status::OK;     } }; int main(int argc, char** argv) {     if (argc < 3) {        cerr << "Usage: server <port> /path/to/export/files" << endl;       return 1;     }    const string serverAddress(string("0.0.0.0:") + argv[1]);    const string pathToExportFiles(argv[2]);    unique_ptr<SessionBundle> sessionBundle = createSessionBundle(pathToExportFiles);    ClassificationServiceImpl classificationServiceImpl(move(sessionBundle));     ServerBuilder builder;     builder.AddListeningPort(serverAddress, grpc::InsecureServerCredentials());     builder.RegisterService(&classificationServiceImpl);     unique_ptr<Server> server = builder.BuildAndStart();     cout << "Server listening on " << serverAddress << endl;     server->Wait();     return 0; }

通过服务器端组件从webapp访问推断服务。运行Python protocol buffer编译器，生成ClassificationService Python protocol buffer客户端：pip install grpcio cython grpcio-tools, python -m grpc.tools.protoc -I. –python_out=. –grpc_python_out=. classification_service.proto。生成包含调用服务stub classification_service_pb2.py 。服务器接到POST请求，解析发送表单，创建ClassificationRequest对象 。分类服务器设置一个channel，请求提交，分类响应渲染HTML，送回用户。容器外部命令python client.py，运行服务器。浏览器导航http://localhost:8080 访问UI。

 from BaseHTTPServer import HTTPServer, BaseHTTPRequestHandler import cgi import classification_service_pb2 from grpc.beta import implementations class ClientApp(BaseHTTPRequestHandler):     def do_GET(self):         self.respond_form()     def respond_form(self, response=""):         form = """         <html><body>         <h1>Image classification service</h1>         <form enctype="multipart/form-data" method="post">         <div>Image: <input type="file" name="file" accept="image/jpeg"></div>         <div><input type="submit" value="Upload"></div>         </form>         %s         </body></html>         """         response = form % response         self.send_response(200)         self.send_header("Content-type", "text/html")         self.send_header("Content-length", len(response))         self.end_headers()         self.wfile.write(response)     def do_POST(self):         form = cgi.FieldStorage(             fp=self.rfile,             headers=self.headers,             environ={                 'REQUEST_METHOD': 'POST',                 'CONTENT_TYPE': self.headers['Content-Type'],             })         request = classification_service_pb2.ClassificationRequest()         request.input = form['file'].file.read()         channel = implementations.insecure_channel("127.0.0.1", 9999)         stub = classification_service_pb2.beta_create_ClassificationService_stub(channel)         response = stub.classify(request, 10) # 10 secs timeout         self.respond_form("<div>Response: %s</div>" % response) if __name__ == '__main__':     host_port = ('0.0.0.0', 8080)     print "Serving in %s:%s" % host_port     HTTPServer(host_port, ClientApp).serve_forever()

产品准备，分类服务器应用产品。编译服务器文件复制到容器永久位置，清理所有临时构建文件。容器中，mkdir /opt/classification_server, cd /mnt/home/serving_example, cp -R bazel-bin/. /opt/classification_server, bazel clean 。容器外部，状态提交新Docker镜像，创建记录虚拟文件系统变化快照。容器外，docker ps, dock commit 。图像推送到自己偏好docker服务云，服务。

参考资料：
《面向机器智能的TensorFlow实践》

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