学学笔记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

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

搭建TensorFlow服务支付条件。安装Docker,https://docs.docker.com/engine/installation/
。用配备文件于地方创建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,在终点工作。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,停止运作。

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项。Bazel自动下充斥构建。项目库根目录定义WORKSPACE文件。TensorFlow模型库包含Inception模型代码。

TensorFlow服务在品种作为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规则定义第三着依也地面存储文件。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”,
)

导出训练好之模子,导出数据流图及变量,给活从而。模型数据流图,必须于占位符接收输入,单步推断计算输出。Inception模型(或貌似图像识别模型),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老推断模型输入格式,图像字符串转换为每重在[0,
1]内像素张量,缩放图像尺寸,符合模型期望宽度高度,像素值变换到范要求区间[-1,
1]外。调用原始模型测算方法,依据转换输入想结果。

推测方法各参数赋值。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。最后一糟保存训练检查点文件包含最后更新模型参数。下去载预训检查点文件: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
包含输出接近名称列表、模型分配各类别分值(或概率)socres_tensor。类别数大抵模子,配置指定只返田大口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类自动生成代码有依靠,Doker容器内部以中bazel运行导出器。代码保存及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事关的检查点文件在/tmp/inception-v3/{currenttimestamp}/创建导出器。首坏运行而针对性TensorFlow编译。load从表导入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++”,
],
)

概念服务器接口。TensorFlow服务使gRPC协议(基于HTTP/2二上前制协议)。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)和亚迈入制编码。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、文字服务可用可同等接口。proto编译器转换proto文件为客户端与劳务器类定义。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音定义。proto定义成每种型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);
}

心想事成推断服务器。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,包含了加载数据流图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,配置也pathToExportFiles指定路线加载导出模型,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,签名指定所吸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输入变换为想输入张量,request参数复制JPEG编码图像字符串到想张量。运行推断,sessionbundle获得TF会话对象,运行一破,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;
}

由此劳务器端组件从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
。服务器收到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,送转用户。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()

出品准备,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>。图像推送到好偏好docker服务云,服务。

参考资料:
《面向机器智能的TensorFlow实践》

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