(Note) A web application that uses TensorFlow to infer recommended song names [Create an execution environment with docker-compose]

Introduction

This article is a continuation of ** (Note) Web application ** that uses TensorFlow to infer recommended song names. .. Create a TensorFlow + Keras environment in the local environment with docker-compose, I would like to organize up to the point where I hit the WEB API. Please note that this is an article I made for myself, so it may be difficult to understand, information, and technology may be out of date: bow: Also, I hope it will be helpful for those who want to make some kind of web application by themselves.

The actual web application looks like the GIF below. When I typed in a sentence in the search box, Mr. Humberd Humberd answered "same story": clap: $ \ tiny {* Since there is little learning data, only some songs will be hit. .. It's shabby} $: bow_tone1: $ \ tiny {* Click the score link to see part of the score, but it is out of the scope of the article} $: no_good_tone1:

References

I used it as a reference when creating this article: bow_tone1:

• A story about moving gunicorn + nginx + flask with docker-compose
• [With image] Nginx + gunicorn + Flask converted to Docker [Part 2]
• Rebuild Django's development environment with Docker! !! !! !!
• [Ready to use! Can be practiced in business! How to make AI / machine learning / deep learning apps with Python](https://www.amazon.co.jp/%E3%81%99%E3%81%90 % E3% 81% AB% E4% BD% BF% E3% 81% 88% E3% 82% 8B-% E6% A5% AD% E5% 8B% 99% E3% 81% A7% E5% AE% 9F% E8% B7% B5% E3% 81% A7% E3% 81% 8D% E3% 82% 8B-Python% E3% 81% AB% E3% 82% 88% E3% 82% 8B-AI% E3% 83% BB% E6% A9% 9F% E6% A2% B0% E5% AD% A6% E7% BF% 92% E3% 83% BB% E6% B7% B1% E5% B1% A4% E5% AD% A6% E7% BF% 92% E3% 82% A2% E3% 83% 97% E3% 83% AA% E3% 81% AE% E3% 81% A4% E3% 81% 8F% E3% 82% 8A% E6% 96% B9-% E3% 82% AF% E3% 82% B8% E3% 83% A9% E9% A3% 9B% E8% A1% 8C% E6% 9C% BA / dp / 4802611641)

TODO map

** (Note) A continuation of the web application ** that uses TensorFlow to infer recommended song names. This time, it is ** environment construction (execution environment) ** on the Web API side.

• I haven't organized the articles yet, so I will create them when I have time. You may die without starting: angel_tone2: Also, please note that the map may change as you write. ..

Environment ^{* I think that it will work even if it is not the following Ver, but please note that it is old: no_good_tone2: </ sup>}

Ubuntu version

$ cat /etc/os-release
NAME="Ubuntu"
VERSION="18.04.4 LTS (Bionic Beaver)"
ID=ubuntu
ID_LIKE=debian
PRETTY_NAME="Ubuntu 18.04.4 LTS"
VERSION_ID="18.04"
HOME_URL="https://www.ubuntu.com/"
SUPPORT_URL="https://help.ubuntu.com/"
BUG_REPORT_URL="https://bugs.launchpad.net/ubuntu/"
PRIVACY_POLICY_URL="https://www.ubuntu.com/legal/terms-and-policies/privacy-policy"
VERSION_CODENAME=bionic
UBUNTU_CODENAME=bionic

Docker version

$ docker version
Client: Docker Engine - Community
 Version:           19.03.8
 API version:       1.40
 Go version:        go1.12.17
 Git commit:        afacb8b7f0
 Built:             Wed Mar 11 01:25:46 2020
 OS/Arch:           linux/amd64
 Experimental:      false

Server: Docker Engine - Community
 Engine:
  Version:          19.03.8
  API version:      1.40 (minimum version 1.12)
  Go version:       go1.12.17
  Git commit:       afacb8b7f0
  Built:            Wed Mar 11 01:24:19 2020
  OS/Arch:          linux/amd64
  Experimental:     false
 containerd:
  Version:          1.2.13
  GitCommit:        7ad184331fa3e55e52b890ea95e65ba581ae3429
 runc:
  Version:          1.0.0-rc10
  GitCommit:        dc9208a3303feef5b3839f4323d9beb36df0a9dd
 docker-init:
  Version:          0.18.0
  GitCommit:        fec3683

Docker-Compose version

$ docker-compose version
docker-compose version 1.25.5, build unknown
docker-py version: 4.2.0
CPython version: 3.7.4
OpenSSL version: OpenSSL 1.1.1c  28 May 2019

^{* For some reason build unknown. I gave up because it seemed to take time: sob: </ sup>}

How to create an execution environment

To put it simply, docker-compose is installed properly, If you place the necessary files according to the [Directory structure](# directory structure) described below, Just run the following command:

Build&Start with background

docker-compose up -d --build

It takes time to build the first time, but after the container is created, if you hit the Web API, it will look like the GIF below.

Web_API execution example

http://localhost:7020/recommend/api/what-music/A song that is sad and wishes for someone's happiness

Web API execution example

Tools is various, so I think anything is fine, but like GIF It will be returned in JSON.

Directory structure

I'm making it properly $ \ tiny {* Don't stare at it} $: no_good_tone1: There are a lot of garbage files, but they are on Github. Source

Directory structure

dk_tensor_fw
├── app_tensor
│   ├── Dockerfile
│   ├── exeWhatMusic.py
│   ├── inputFile
│   │   └── ans_studyInput_fork.txt
│   ├── mkdbAndStudy.py
│   ├── requirements.txt
│   ├── studyModel
│   │   ├── genre-model.hdf5
│   │   ├── genre-tdidf.dic
│   │   ├── genre.pickle
│   ├── tfidfWithIni.py
│   └── webQueApiRunServer.py
├── docker-compose.yml
├── web_nginx
    ├── Dockerfile
    └── nginx.conf

Files required to create a local environment with docker-compose

docker-compose.yml

version: '3'
services:
###########App server settings###########
  app_tensor:
    container_name: app_tensor
    #Service restart policy
    restart: always
    #Directory containing docker files to build
    build: ./app_tensor
    volumes:
      #Directory to mount
      - ./app_tensor:/dk_tensor_fw/app_tensor
    ports:
      #Host side port: Container side port
      - 7010:7010
    networks:
      - nginx_network
###########App server settings###########

###########Web server settings###########
  web-nginx:
    container_name: web-nginx
    build: ./web_nginx
    volumes:
      #Directory to mount
      - ./web_nginx:/dk_tensor_fw/web_nginx
    ports:
      #Port forwarding from 7020 on the host PC to 7020 on the container
      - 7020:7020
    depends_on:
      #Specify the dependency. web-app before starting server-Will start the server
      - app_tensor
    networks:
      - nginx_network
###########Web server settings###########
networks:
  nginx_network:
    driver: bridge

• (Reference) The port number is specified above, but you can check it with the following command.

(Reference) How to check free ports

#Check free ports (free if nothing is displayed)
netstat -an | grep 7010

Dockerfile ← Ap server side(Gunicorn)

FROM ubuntu:18.04

WORKDIR /dk_tensor_fw/app_tensor
COPY requirements.txt /dk_tensor_fw/app_tensor

RUN apt-get -y update \
    && apt-get -y upgrade \
    && apt-get install -y --no-install-recommends locales curl python3-distutils vim ca-certificates \
    && curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py \
    && python3 get-pip.py \
    && pip install -U pip \
    && localedef -i en_US -c -f UTF-8 -A /usr/share/locale/locale.alias en_US.UTF-8 \
    && apt-get clean \
    && rm -rf /var/lib/apt/lists/* \
    && pip install -r requirements.txt --no-cache-dir

ENV LANG en_US.utf8

CMD ["gunicorn", "webQueApiRunServer:app", "-b", "0.0.0.0:7010"]

requirements.txt

Flask==1.1.0
gunicorn==19.9.0
Keras>=2.2.5
numpy==1.16.4
pandas==0.24.2
pillow>=6.2.0
python-dateutil==2.8.0
pytz==2019.1
PyYAML==5.1.1
requests==2.22.0
scikit-learn==0.21.2
sklearn==0.0
matplotlib==3.1.1
tensorboard>=1.14.0
tensorflow>=1.14.0
mecab-python3==0.996.2

Dockerfile ← Web server side(Nginx)

FROM nginx:latest

RUN rm /etc/nginx/conf.d/default.conf
COPY nginx.conf /etc/nginx/conf.d

nginx.conf

    upstream app_tensor_config {
        #If you specify the service name of the container, the name will be resolved.
        server app_tensor:7010;
    }

    server {
        listen 7020;
        root /dk_tensor_fw/app_tensor/;
        server_name localhost;

        location / {
            try_files $uri @flask;
        }

        location @flask {
            proxy_set_header Host $host;
            proxy_set_header X-Real-IP $remote_addr;
            proxy_redirect off;

            proxy_pass http://app_tensor_config;
        }

        # redirect server error pages to the static page /50x.html
        error_page   500 502 503 504  /50x.html;
        location = /50x.html {
            root   /usr/share/nginx/html;
        }

        #Static file request is routed statically ← It is unnecessary because it is not used.
        location /static/ {
            alias /dk_tensor_fw/app_tensor/satic/;
        }
    }

Confirmation of the completed environment

Start with build & background

$ docker-compose up -d --build

Display docker-compose image information

$ docker-compose images
Container          Repository           Tag       Image Id       Size  
-----------------------------------------------------------------------
app_tensor   dk_tensor_fw_app_tensor   latest   3b916ea797e0   2.104 GB
web-nginx    dk_tensor_fw_web-nginx    latest   175c2596bb8b   126.8 MB

^{Is it bad to make? It seems that the capacity is quite large: sweat: </ sup>}

List of containers

$ docker-compose ps
   Name                 Command               State               Ports             
------------------------------------------------------------------------------------
app_tensor   gunicorn webQueApiRunServe ...   Up      0.0.0.0:7010->7010/tcp        
web-nginx    nginx -g daemon off;             Up      0.0.0.0:7020->7020/tcp, 80/tcp

Connect to container (Ap server side)

$ docker-compose exec app_tensor /bin/bash
root@ba0ce565430c:/dk_tensor_fw/app_tensor#

I put it in a container on the Ap server side. .. ..

Check the contents to see if TensorFlow or Keras is included

^{I omitted some because the display of the output result is long: sweat: </ sup>}

root@ba0ce565430c:/dk_tensor_fw/app_tensor# pip3 list
Package                Version
---------------------- -----------
absl-py                0.9.0
Flask                  1.1.0
gunicorn               19.9.0
Keras                  2.3.1
Keras-Applications     1.0.8
Keras-Preprocessing    1.1.2
matplotlib             3.1.1
mecab-python3          0.996.2
numpy                  1.16.4
pandas                 0.24.2
Pillow                 7.1.2
pip                    20.1
python-dateutil        2.8.0
pytz                   2019.1
PyYAML                 5.1.1
requests               2.22.0
requests-oauthlib      1.3.0
rsa                    4.0
scikit-learn           0.21.2
six                    1.14.0
sklearn                0.0
tensorboard            2.2.1
tensorboard-plugin-wit 1.6.0.post3
tensorflow             2.2.0
tensorflow-estimator   2.2.0
(abridgement)

It seems that TensorFlow, Keras, etc. are all included. .. ..

Connect to the container on the web server side

$ docker-compose exec web-nginx /bin/bash
root@d6971e4dc05c:/# 

I also put it in the container on the web server side.

For the time being, check if the web server (Nginx) is running.

root@d6971e4dc05c:/# /etc/init.d/nginx status
[ ok ] nginx is running.

It seems that Nginx is also running. I have confirmed the execution environment so far. If you hit the WEB API as shown in the above [Web API execution example](# web-api execution example), you should have an execution environment on the WEB API side. .. ..

About the future

This time, I was able to organize the execution environment on the WEB API side a little. Also, I hope I can brush up and organize it little by little when I have time: sob: It is undecided, but next time I would like to organize the machine learning part.