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-devel https://raw.githubusercontent.com/tensorflow/serving/master/tensorflow_serving/tools/docker/Dockerfile.devel 。镜像运行容器，docker run -v $HOME:/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 <container id>。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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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
原文：http://www.cnblogs.com/libinggen/p/6971400.html