Keras模型生产环境的部署[源码+教程]
兴趣方向：统计机器学习，深度学习，模型的线上化部署、网络爬虫，前端可视化。
个人博客：https://dataxujing.github.io/
项目地址：
https://github.com/DataXujing/tensorflow-serving-Wechat
1. 需要的系统环境
（1）一台生产服务器，系统为CentOs6.5（ubuntu只需在此基础上做简单修改即可完成部署）（2）默认为Python2.X（CentOs6.5默认的Python安装版本，特别提醒，不要卸载该版本，因为很多命令行函数基于Python2.X）（3）需要安装Python3.6环境
本文将演示从零开始把Keras训练的MNIST数据集项目部署到生产环境。
需要的模块：
tensorflow
Flask
gevent
gunicorn
keras
numpy
h5py
pillow
uwsgi
supervisor
2.核心代码分享
代码目录如下：
其中Keras模型部分如下(对应文件结构中的train.py):
'''
Gets to 98.78% test accuracy after 12 epochs
https://dataxujing.github.io/用CNN实现MNIST数据集/
'''
#python 2/3 compatibility
from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
# 模型结构画出来
from keras.utils.vis_utils import plot_model
batch_size = 128
num_classes = 10
epochs = 12
# 输入图片的大小
# 28x28 pixel images.
img_rows, img_cols = 28, 28
# 数据划分
(x_train, y_train), (x_test, y_test) = mnist.load_data()
#3D数据, "channels_last" assumes (conv_dim1, conv_dim2, conv_dim3, channels)
#while "channels_first" assumes (channels, conv_dim1, conv_dim2, conv_dim3).
if K.image_data_format() == 'channels_first':
x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
input_shape = (1, img_rows, img_cols)
else:
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
# 数据变换
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# one-hot我们的label
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
# 构建模型
model = Sequential()
# cov2D + Relu
model.add(Conv2D(32, kernel_size=(3, 3),
activation='relu',
input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
# Pooling
model.add(MaxPooling2D(pool_size=(2, 2)))
# Drop out
model.add(Dropout(0.25))
#flatten
model.add(Flatten())
#fully connected to get all relevant data
model.add(Dense(128, activation='relu'))
# Drop out
model.add(Dropout(0.5))
# softmax
model.add(Dense(num_classes, activation='softmax'))
#Adaptive learning rate (adaDelta)
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adadelta(),
metrics=['accuracy'])
#train
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_test, y_test))
# metric
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
#Save the model
# serialize model to JSON
model_json = model.to_json()
with open("model.json", "w") as json_file:
json_file.write(model_json)
# serialize weights to HDF5
model.save_weights("model.h5")
print("Saved model to disk")
# plot
plot_model(model, to_file='model_lx.png',show_shapes=True)
训练后的模型会保存在文件结构中的model文件夹，供Flask调用。
Flask serve部分(对应文件结构中的app.py部分):
from flask import Flask, render_template,request
#scientific computing library for saving, reading, and resizing images
from scipy.misc import imsave, imread, imresize
import numpy as np
import keras.models
import re
import sys
import os
import base64
#tell our app where our saved model is
sys.path.append(os.path.abspath("./model"))
from load import *
#initalize our flask app
app = Flask(__name__)
global model, graph
model, graph = init()
#decoding an image from base64 into raw representation
def convertImage(imgData1):
imgstr = re.search(b'base64,(.*)',imgData1).group(1) # 匹配第一个括号
with open('output.png','wb') as output:
output.write(base64.b64decode(imgstr))
@app.route('/')
def index():
return render_template("index.html")
@app.route('/predict/',methods=['GET','POST'])
def predict():
imgData = request.get_data()
convertImage(imgData)
#read the image into memory
x = imread('output.png',mode='L')
#compute a bit-wise inversion so black becomes white and vice versa
x = np.invert(x)
#make it the right size
x = imresize(x,(28,28))
x = x.reshape(1,28,28,1)
with graph.as_default():
#perform the prediction
out = model.predict(x)
print(out)
print(np.argmax(out,axis=1))
#convert the response to a string
response = np.array_str(np.argmax(out,axis=1))
return response
if __name__ == "__main__":
port = int(os.environ.get('PORT', 5060))
app.run(host='0.0.0.0', port=port)
运行app.py可以测试训练的Keras模型是否正常运行。为了部署Flask应用，采用nginx+uwsgi+supervisor的方式把上述Flask应用部署到centOs服务器。
3.安装和配置uwsgi
uwsgi的配置内容如下(对应文件结构中的keras_uwsgi.ini文件):
[uwsgi]
#application's base folder
base = /home/soft/Keras_model
#python module to import
app = app
module = %(app)
home = %(base)/envKeras
#home =/root/anaconda3
pythonpath = %(base)
#socket file's location
socket = 127.0.0.1:8001
#permissions for the socket file
chmod-socket = 777
#the variable that holds a flask application inside the module imported at line #6
callable = app
#location of log files
logto = /home/soft/Keras_model/log/%n.log
chdir = /home/soft/Keras_model/
# 处理器数
processes = 4
# 线程数
threads = 2
4.安装和配置nginx
首先服务器需要安装nginx,centOs运行一下命令即可安装：
yum install -y nginx
浏览器下测试是否安装成功:
安装成功后将下面配置项(对应文件结构中的keras_nginx.conf)复制到/etc/nginx/conf.d/中：
server {
listen 5060;
server_name XXX.XXX.XXX.XXX;
charset utf-8;
location / {
include uwsgi_params;
uwsgi_pass unix: 127.0.0.1:8001;
}
location ~ /static/ {
root /home/soft/Keras_model;
}
}
5.安装和配置supervisor
supervisor可以在后台启动Flask应用，并且可以配置当服务器宕机时仍可以自启动Flask应用。其配置项如下（对应文件结构中的keras_supervisor.conf):
[program:app]
# 启动命令入口
command=uwsgi --ini /home/soft/Keras_model/keras_uwsgi.ini
# 命令程序所在目录
#directory=/home/soft/keras_model
#运行命令的用户名
autostart=true
autorestart=true
#日志地址
stdout_logfile=/home/soft/Keras_model/log/uwsgi_supervisor.log
stderr_logfile=/home/soft/Keras_model/log/uwsgi_supervisor_error.log
[inet_http_server]
port = 127.0.0.1:9001
[supervisord]
nodaemon=true
user=root
[supervisorctl]
6. 测试
最后运行最终的run.sh的Shell脚本就可以启动我们的基于Keras的Flask的应用，其中run.sh的脚本内容如下:
#!/bin/bash
echo "Keras+Flask!"
nginx | uwsgi --ini /home/soft/Keras_model/keras_uwsgi.ini |
supervisord -c /home/soft/Keras_model/keras_supervisor.conf
我们可以在浏览器中测试我们的MNIST,手写数字识别应用：
7. 小结
本文我们基于Keras训练了一个简单的基于手写数字识别的CNN模型，并基于Flask把应用部署到我们的生产环境。其实这是一种最简单的基于Keras的部署方式。除了这种部署方式，你可以在我们项目地址：
https://github.com/DataXujing/tensorflow-serving-Wechat
中获得更多的部署方式，我们在该项目中详细讲解了基于Tensorflow和Keras的生产环境的部署方式(例如STFS,TFS,Keras.js,WebDNN,neocortex.js等)。欢迎大家Fork和issues。
项目地址:
https://github.com/DataXujing/tensorflow-serving-Wechat
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