產品環境模型部署，創建簡單Web APP，用戶上傳圖像，運行Inception模型，實現圖像自動分類。

搭建TensorFlow服務開發環境。安裝Docker，https://docs.docker.com/engine/installation/ 。用配置文件在本地創建Docker鏡像，docker build --pull -t $USER/tensorflow-serving-devel https://raw.githubusercontent.com/tensorflow/serving/master/tensorflow_serving/tools/docker/Dockerfile.devel 。鏡像運行容器，docker run -v $HOME:/mnt/home -p 9999:9999 -it $USER/tensorflow-serving-devel ，在home目錄加載到容器/mnt/home路徑，在終端工作。用IDE或編輯器編輯代碼，用容器運行構建工具，主機通過9999端口訪問，構建服務器。exit命令退出容器終端，停止運行。

TensorFlow服務程序C++寫，使用Google的Bazel構建工具。容器運行Bazel。Bazel代碼級管理第三方依賴項。Bazel自動下載構建。項目庫根目錄定義WORKSPACE文件。TensorFlow模型庫包含Inception模型代碼。

TensorFlow服務在項目作為Git子模塊。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規則定義第三方依賴為本地存儲文件。項目導入tf_workspace規則初始化TensorFlow依賴項。

 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",
 )

導出訓練好的模型，導出數據流圖及變量，給產品用。模型數據流圖，必須從占位符接收輸入，單步推斷計算輸出。Inception模型(或一般圖像識別模型)，JPEG編碼圖像字符串輸入，與從TFRecord文件讀取輸入不同。定義輸入占位符，調用函數轉換占位符表示外部輸入為原始推斷模型輸入格式，圖像字符串轉換為各分量位于[0, 1]內像素張量，縮放圖像尺寸，符合模型期望寬度高度，像素值變換到模型要求區間[-1, 1]內。調用原始模型推斷方法，依據轉換輸入推斷結果。

推斷方法各參數賦值。從檢查點恢復參數值。周期性保存模型訓練檢查點文件，文件包含學習參數。最后一次保存訓練檢查點文件包含最后更新模型參數。下去載預訓練檢查點文件：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類導出模型。傳入保存器實例創建實例，用exporter.classification_signature創建模型簽名。指定input_tensor、輸出張量。classes_tensor 包含輸出類名稱列表、模型分配各類別分值(或概率)socres_tensor。類別數多模型，配置指定僅返田大口tf.nntop_k選擇類別，模型分配分數降序排列前K個類別。調用exporter.Exporter.init方法簽名，export方法導出模型，接收輸出路徑、模型版本號、會話對象。Exporter類自動生成代碼存在依賴，Doker容器內部使用中bazel運行導出器。代碼保存到bazel工作區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)

一個構建規則BUILD文件。在容器命令運行導出器，cd /mnt/home/serving_example, hazel run:export /tmp/inception-v3 ，依據/tmp/inception-v3提到的檢查點文件在/tmp/inception-v3/{currenttimestamp}/創建導出器。首次運行要對TensorFlow編譯。load從外部導入protobuf庫，導入cc_proto_library規則定義，為proto文件定義構建規則。通過命令bazel run :server 9999 /tmp/inception-v3/export/{timestamp}，容器運行推斷服務器。

 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++",
         ],
 )

定義服務器接口。TensorFlow服務使用gRPC協議(基于HTTP/2二進制協議)。支持創建服務器和自動生成客戶端存根各種語言。在protocol buffer定義服務契約，用于gRPC IDL(接口定義語言)和二進制編碼。接收JPEG編碼待分類圖像字符串輸入，返回分數排列推斷類別列表。定義在classification_service.proto文件。接收圖像、音頻片段、文字服務可用可一接口。proto編譯器轉換proto文件為客戶端和服務器類定義。bazel build:classification_service_proto可行構建，通過bazel-genfiles/classification_service.grpc.pb.h檢查結果。推斷邏輯，ClassificationService::Service接口必須實現。檢查bazel-genfiles/classification_service.pb.h查看request、response消息定義。proto定義變成每種類型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);
 }

實現推斷服務器。加載導出模型，調用推斷方法，實現ClassificationService::Service。導出模型，創建SessionBundle對象，包含完全加載數據流圖TF會話對象，定義導出工具分類簽名元數據。SessionBundleFactory類創建SessionBundle對象，配置為pathToExportFiles指定路徑加載導出模型，返回創建SessionBundle實例unique指針。定義ClassificationServiceImpl，接收SessionBundle實例參數。

加載分類簽名，GetClassificationSignature函數加載模型導出元數據ClassificationSignature，簽名指定所接收圖像真實名稱的輸入張量邏輯名稱，以及數據流圖輸出張量邏輯名稱映射推斷結果。將protobuf輸入變換為推斷輸入張量，request參數復制JPEG編碼圖像字符串到推斷張量。運行推斷，sessionbundle獲得TF會話對象，運行一次，傳入輸入輸出張量推斷。推斷輸出張量變換protobuf輸出，輸出張量結果復制到ClassificationResponse消息指定形狀response輸出參數格式化。設置gRPC服務器，SessionBundle對象配置，創建ClassificationServiceImpl實例樣板代碼。

 #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;
 }

通過服務器端組件從webapp訪問推斷服務。運行Python protocol buffer編譯器，生成ClassificationService Python protocol buffer客戶端：pip install grpcio cython grpcio-tools, python -m grpc.tools.protoc -I. --python_out=. --grpc_python_out=. classification_service.proto。生成包含調用服務stub classification_service_pb2.py 。服務器接到POST請求，解析發送表單，創建ClassificationRequest對象 。分類服務器設置一個channel，請求提交，分類響應渲染HTML，送回用戶。容器外部命令python client.py，運行服務器。瀏覽器導航http://localhost:8080 訪問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()

產品準備，分類服務器應用產品。編譯服務器文件復制到容器永久位置，清理所有臨時構建文件。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，狀態提交新Docker鏡像，創建記錄虛擬文件系統變化快照。容器外，docker ps, dock commit <container id>。圖像推送到自己偏好docker服務云，服務。

參考資料：
《面向機器智能的TensorFlow實踐》

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