├── dockercon2020-demo ├── db │ └── password.txt ├── app │ ├── requirements.txt │ ├── Dockerfile │ └── src │ │ └── server.py ├── web │ ├── Dockerfile │ ├── nginx.conf │ └── index.html └── docker-compose.yaml ├── ecsblog-demo ├── backend │ ├── requirements.txt │ ├── Dockerfile │ └── main.py ├── frontend │ ├── Dockerfile │ └── nginx.conf └── docker-compose.yml ├── README.md ├── wordsmith-demo ├── words │ ├── .dockerignore │ ├── src │ │ └── main │ │ │ ├── resources │ │ │ ├── table.sql │ │ │ └── words.sql │ │ │ └── java │ │ │ └── Main.java │ ├── Dockerfile │ └── pom.xml ├── img │ ├── dockercon_EU_17.jpg │ └── dockercon-barcelona-logo.svg ├── web │ ├── static │ │ ├── favicon.ico │ │ ├── fonts │ │ │ ├── font1.woff2 │ │ │ └── font2.woff2 │ │ ├── images │ │ │ ├── homes.png │ │ │ ├── lego_blue.png │ │ │ ├── lego_yellow.png │ │ │ ├── dockercon-log.png │ │ │ ├── lego_light_blue.png │ │ │ ├── dockercon-logo-2020.png │ │ │ └── logo.svg │ │ ├── app.js │ │ ├── index.html │ │ ├── style.css │ │ └── angular.min.js │ ├── Dockerfile │ └── dispatcher.go ├── docker-compose.yaml └── dev-docker-compose.yaml └── gpublog-demo ├── backend ├── requirements.txt ├── Dockerfile ├── server.py └── model.py ├── frontend ├── Dockerfile ├── nginx.conf └── index.html ├── docker-compose.dev.yaml ├── README.md └── docker-compose.prod.yaml /dockercon2020-demo/db/password.txt: -------------------------------------------------------------------------------- 1 | db-78n9n -------------------------------------------------------------------------------- /ecsblog-demo/backend/requirements.txt: -------------------------------------------------------------------------------- 1 | flask==1.1.2 -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # dockercon 2 | Demo resources for DockerCon 3 | -------------------------------------------------------------------------------- /wordsmith-demo/words/.dockerignore: -------------------------------------------------------------------------------- 1 | .idea 2 | target 3 | *.iml 4 | -------------------------------------------------------------------------------- /gpublog-demo/backend/requirements.txt: -------------------------------------------------------------------------------- 1 | Flask 2 | matplotlib 3 | sklearn 4 | requests 5 | -------------------------------------------------------------------------------- /ecsblog-demo/frontend/Dockerfile: -------------------------------------------------------------------------------- 1 | FROM nginx:alpine 2 | COPY nginx.conf /etc/nginx/conf.d/default.conf 3 | 4 | 5 | -------------------------------------------------------------------------------- /dockercon2020-demo/app/requirements.txt: -------------------------------------------------------------------------------- 1 | Flask==1.1.1 2 | mysql-connector==2.2.9 3 | # debugging only 4 | # ptvsd==4.3.2 5 | -------------------------------------------------------------------------------- /ecsblog-demo/frontend/nginx.conf: -------------------------------------------------------------------------------- 1 | server { 2 | listen 80; 3 | location / { 4 | proxy_pass http://backend:80; 5 | } 6 | } -------------------------------------------------------------------------------- /wordsmith-demo/img/dockercon_EU_17.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aiordache/demos/HEAD/wordsmith-demo/img/dockercon_EU_17.jpg -------------------------------------------------------------------------------- /wordsmith-demo/web/static/favicon.ico: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aiordache/demos/HEAD/wordsmith-demo/web/static/favicon.ico -------------------------------------------------------------------------------- /wordsmith-demo/web/static/fonts/font1.woff2: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aiordache/demos/HEAD/wordsmith-demo/web/static/fonts/font1.woff2 -------------------------------------------------------------------------------- /wordsmith-demo/web/static/fonts/font2.woff2: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aiordache/demos/HEAD/wordsmith-demo/web/static/fonts/font2.woff2 -------------------------------------------------------------------------------- /wordsmith-demo/web/static/images/homes.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aiordache/demos/HEAD/wordsmith-demo/web/static/images/homes.png -------------------------------------------------------------------------------- /wordsmith-demo/web/static/images/lego_blue.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aiordache/demos/HEAD/wordsmith-demo/web/static/images/lego_blue.png -------------------------------------------------------------------------------- /wordsmith-demo/web/static/images/lego_yellow.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aiordache/demos/HEAD/wordsmith-demo/web/static/images/lego_yellow.png -------------------------------------------------------------------------------- /dockercon2020-demo/web/Dockerfile: -------------------------------------------------------------------------------- 1 | FROM nginx:1.13-alpine 2 | COPY nginx.conf /etc/nginx/nginx.conf 3 | COPY index.html /usr/share/nginx/html/index.html -------------------------------------------------------------------------------- /wordsmith-demo/web/static/images/dockercon-log.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aiordache/demos/HEAD/wordsmith-demo/web/static/images/dockercon-log.png -------------------------------------------------------------------------------- /gpublog-demo/frontend/Dockerfile: -------------------------------------------------------------------------------- 1 | FROM nginx:1.13-alpine 2 | COPY nginx.conf /etc/nginx/nginx.conf 3 | COPY index.html /usr/share/nginx/html/index.html 4 | -------------------------------------------------------------------------------- /wordsmith-demo/web/static/images/lego_light_blue.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aiordache/demos/HEAD/wordsmith-demo/web/static/images/lego_light_blue.png -------------------------------------------------------------------------------- /wordsmith-demo/web/static/images/dockercon-logo-2020.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aiordache/demos/HEAD/wordsmith-demo/web/static/images/dockercon-logo-2020.png -------------------------------------------------------------------------------- /dockercon2020-demo/app/Dockerfile: -------------------------------------------------------------------------------- 1 | FROM python:3.8-alpine 2 | WORKDIR /src 3 | COPY requirements.txt . 4 | RUN pip install -r requirements.txt 5 | COPY ./src /src 6 | EXPOSE 5000 7 | CMD python server.py 8 | -------------------------------------------------------------------------------- /ecsblog-demo/docker-compose.yml: -------------------------------------------------------------------------------- 1 | services: 2 | frontend: 3 | build: frontend 4 | ports: 5 | - 80:80 6 | depends_on: 7 | - backend 8 | backend: 9 | build: backend 10 | -------------------------------------------------------------------------------- /wordsmith-demo/words/src/main/resources/table.sql: -------------------------------------------------------------------------------- 1 | CREATE TABLE IF NOT EXISTS nouns (word TEXT NOT NULL); 2 | CREATE TABLE IF NOT EXISTS verbs (word TEXT NOT NULL); 3 | CREATE TABLE IF NOT EXISTS adjectives (word TEXT NOT NULL); 4 | -------------------------------------------------------------------------------- /ecsblog-demo/backend/Dockerfile: -------------------------------------------------------------------------------- 1 | FROM python:3.7-alpine 2 | WORKDIR /app 3 | COPY requirements.txt /app 4 | RUN pip3 install -r requirements.txt 5 | COPY main.py /app/main.py 6 | ENTRYPOINT ["python3"] 7 | CMD ["/app/main.py"] 8 | -------------------------------------------------------------------------------- /gpublog-demo/frontend/nginx.conf: -------------------------------------------------------------------------------- 1 | events { } 2 | http { 3 | server { 4 | listen 80; 5 | root /usr/share/nginx/html; 6 | location /api/ { 7 | proxy_pass http://translator:5000/; 8 | } 9 | } 10 | } 11 | -------------------------------------------------------------------------------- /gpublog-demo/backend/Dockerfile: -------------------------------------------------------------------------------- 1 | FROM tensorflow/tensorflow:latest-gpu 2 | 3 | WORKDIR /code 4 | COPY requirements.txt /code/ 5 | RUN pip install -r requirements.txt 6 | COPY . /code/ 7 | 8 | EXPOSE 5000 9 | 10 | CMD python server.py 11 | -------------------------------------------------------------------------------- /wordsmith-demo/docker-compose.yaml: -------------------------------------------------------------------------------- 1 | version: '3.7' 2 | 3 | services: 4 | db: 5 | image: postgres:10.0-alpine 6 | 7 | words: 8 | image: wordsmithinc/sentences-api 9 | 10 | web: 11 | image: wordsmithinc/sentences-web 12 | ports: 13 | - "80:80" 14 | -------------------------------------------------------------------------------- /wordsmith-demo/dev-docker-compose.yaml: -------------------------------------------------------------------------------- 1 | version: '3.7' 2 | 3 | services: 4 | db: 5 | image: postgres:10.0-alpine 6 | 7 | words: 8 | build: words 9 | image: wordsmithinc/sentences-api 10 | 11 | web: 12 | build: web 13 | image: wordsmithinc/sentences-web 14 | ports: 15 | - "80:80" 16 | -------------------------------------------------------------------------------- /dockercon2020-demo/web/nginx.conf: -------------------------------------------------------------------------------- 1 | events { } 2 | http { 3 | server { 4 | listen 80; 5 | root /usr/share/nginx/html; 6 | # simple reverse-proxy 7 | # pass requests for dynamic content to the Flask server 8 | location /api/ { 9 | proxy_pass http://app:5000/; 10 | } 11 | } 12 | } 13 | 14 | -------------------------------------------------------------------------------- /gpublog-demo/docker-compose.dev.yaml: -------------------------------------------------------------------------------- 1 | services: 2 | 3 | training: 4 | build: backend 5 | command: python model.py 6 | volumes: 7 | - models:/checkpoints 8 | 9 | translator: 10 | build: backend 11 | volumes: 12 | - models:/checkpoints 13 | ports: 14 | - 5000:5000 15 | 16 | volumes: 17 | models: 18 | -------------------------------------------------------------------------------- /wordsmith-demo/web/Dockerfile: -------------------------------------------------------------------------------- 1 | # MULTI-STAGE BUILD 2 | FROM golang:1.14.2-alpine3.11 AS build 3 | 4 | WORKDIR /demo 5 | COPY dispatcher.go . 6 | RUN CGO_ENABLED=0 go build -o dispatcher dispatcher.go 7 | 8 | 9 | FROM nginx:alpine 10 | COPY static /static/ 11 | COPY --from=build /demo/dispatcher /dispatcher 12 | EXPOSE 80 13 | CMD ["/dispatcher"] 14 | 15 | -------------------------------------------------------------------------------- /ecsblog-demo/backend/main.py: -------------------------------------------------------------------------------- 1 | from flask import Flask 2 | app = Flask(__name__) 3 | 4 | @app.route("/") 5 | def hello(): 6 | return """ 7 | ## . 8 | ## ## ## == 9 | ## ## ## ## ## === 10 | /\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\___/ === 11 | { / ===- 12 | \______ O __/ 13 | \ \ __/ 14 | \____\_______/ 15 | 16 | Hello from Docker! 17 | 18 | """ 19 | 20 | if __name__ == '__main__': 21 | app.run(host='0.0.0.0', port=80) 22 | 23 | -------------------------------------------------------------------------------- /gpublog-demo/README.md: -------------------------------------------------------------------------------- 1 | ### DEMO GPU support in Docker Compose 2 | 3 | `model_generator.py` contains sample code from the Tensorflow docs at https://www.tensorflow.org/tutorials/text/nmt_with_attention 4 | 5 | When deploying on a host without an Nvidia GPU device, remove the `deploy` section from the Compose file, otherwise `docker-compose` will request the allocation of a GPU which will fail if the host does not have one. 6 | 7 | If no GPU is detected, the tensorflow framework will default to CPU. Training may take very long in this case. 8 | -------------------------------------------------------------------------------- /gpublog-demo/docker-compose.prod.yaml: -------------------------------------------------------------------------------- 1 | services: 2 | training: 3 | build: backend 4 | command: python model.py 5 | environment: 6 | PYTHONUNBUFFERED: 0 7 | volumes: 8 | - models:/checkpoints 9 | deploy: 10 | resources: 11 | reservations: 12 | memory: 16Gb 13 | devices: 14 | - driver: nvidia 15 | count: 1 16 | capabilities: [gpu] 17 | 18 | translator: 19 | build: backend 20 | volumes: 21 | - models:/checkpoints 22 | 23 | frontend: 24 | build: frontend 25 | ports: 26 | - 80:80 27 | depends_on: 28 | - translator 29 | 30 | volumes: 31 | models: 32 | -------------------------------------------------------------------------------- /wordsmith-demo/words/Dockerfile: -------------------------------------------------------------------------------- 1 | # BUILD 2 | FROM openjdk:8u171-jdk-alpine as build 3 | WORKDIR /home/lab 4 | RUN MAVEN_VERSION=3.5.0 \ 5 | && cd /usr/share \ 6 | && wget http://archive.apache.org/dist/maven/maven-3/$MAVEN_VERSION/binaries/apache-maven-$MAVEN_VERSION-bin.tar.gz -O - | tar xzf - \ 7 | && mv /usr/share/apache-maven-$MAVEN_VERSION /usr/share/maven \ 8 | && ln -s /usr/share/maven/bin/mvn /usr/bin/mvn 9 | 10 | 11 | COPY pom.xml . 12 | RUN mvn verify -DskipTests --fail-never 13 | 14 | COPY src ./src 15 | RUN mvn verify 16 | 17 | # RUN 18 | FROM openjdk:8u171-jre 19 | WORKDIR /app 20 | COPY src/main/resources /app/resources 21 | COPY --from=build /home/lab/target . 22 | 23 | ENTRYPOINT ["java", "-jar", "/app/words.jar"] 24 | EXPOSE 8080 -------------------------------------------------------------------------------- /dockercon2020-demo/docker-compose.yaml: -------------------------------------------------------------------------------- 1 | version: "3.7" 2 | services: 3 | db: 4 | image: mysql:8.0.19 5 | command: '--default-authentication-plugin=mysql_native_password' 6 | restart: always 7 | secrets: 8 | - db-password 9 | volumes: 10 | - db-data:/var/lib/mysql 11 | networks: 12 | - backend-network 13 | environment: 14 | - MYSQL_DATABASE=example 15 | - MYSQL_ROOT_PASSWORD_FILE=/run/secrets/db-password 16 | 17 | app: 18 | build: app 19 | restart: always 20 | secrets: 21 | - db-password 22 | networks: 23 | - backend-network 24 | - frontend-network 25 | 26 | web: 27 | build: web 28 | restart: always 29 | ports: 30 | - 80:80 31 | networks: 32 | - frontend-network 33 | volumes: 34 | db-data: 35 | secrets: 36 | db-password: 37 | file: db/password.txt 38 | networks: 39 | backend-network: 40 | frontend-network: 41 | -------------------------------------------------------------------------------- /gpublog-demo/backend/server.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | from flask import Flask, request, make_response, jsonify 3 | from model import TranslatorModel 4 | 5 | server = Flask(__name__) 6 | 7 | model = None 8 | 9 | 10 | @server.route('/reload', methods = ['GET']) 11 | def reload(): 12 | global model 13 | lang = request.args.get('lang', default = None, type = str) 14 | if not lang: 15 | return make_response('', 200) 16 | 17 | model = TranslatorModel(lang) 18 | return make_response('', 200) 19 | 20 | 21 | @server.route('/', methods = ['GET', 'POST']) 22 | def translate(): 23 | global model 24 | text = request.args.get('text', default = None, type = str) or request.form.get('text') 25 | if not text: 26 | return make_response('', 200) 27 | if not model: 28 | return make_response('No trained model found / training may be in progress...', 200) 29 | 30 | t = model.translate(text) 31 | return make_response(t, 200) 32 | 33 | 34 | 35 | if __name__ == '__main__': 36 | server.run(host='0.0.0.0', debug=False) 37 | -------------------------------------------------------------------------------- /wordsmith-demo/words/src/main/resources/words.sql: -------------------------------------------------------------------------------- 1 | 2 | INSERT INTO nouns(word) VALUES 3 | ('cloud'), 4 | ('elephant'), 5 | ('go language'), 6 | ('laptop'), 7 | ('container'), 8 | ('micro-service'), 9 | ('turtle'), 10 | ('whale'), 11 | ('gopher'), 12 | ('moby dock'), 13 | ('server'), 14 | ('bicycle'), 15 | ('viking'), 16 | ('mermaid'), 17 | ('fjord'), 18 | ('lego'), 19 | ('flodebolle'), 20 | ('smorrebrod'); 21 | 22 | INSERT INTO verbs(word) VALUES 23 | ('will drink'), 24 | ('smashes'), 25 | ('smokes'), 26 | ('eats'), 27 | ('walks towards'), 28 | ('loves'), 29 | ('helps'), 30 | ('pushes'), 31 | ('debugs'), 32 | ('invites'), 33 | ('hides'), 34 | ('will ship'); 35 | 36 | INSERT INTO adjectives(word) VALUES 37 | ('the exquisite'), 38 | ('a pink'), 39 | ('the rotten'), 40 | ('a red'), 41 | ('the serverless'), 42 | ('a broken'), 43 | ('a shiny'), 44 | ('the pretty'), 45 | ('the impressive'), 46 | ('an awesome'), 47 | ('the famous'), 48 | ('a gigantic'), 49 | ('the glorious'), 50 | ('the nordic'), 51 | ('the welcoming'), 52 | ('the delicious'); 53 | -------------------------------------------------------------------------------- /wordsmith-demo/web/static/app.js: -------------------------------------------------------------------------------- 1 | "use strict"; 2 | 3 | var lab = angular.module('lab', []); 4 | 5 | lab.controller('LabCtrl', function ($scope, $http, $timeout) { 6 | $scope.noun1 = ""; 7 | $scope.noun2 = ""; 8 | $scope.adjective1 = ""; 9 | $scope.adjective2 = ""; 10 | $scope.verb = ""; 11 | 12 | getWord($http, $timeout, '/words/noun', function(resp) { 13 | $scope.noun1 = word(resp); 14 | }); 15 | getWord($http, $timeout, '/words/noun', function(resp) { 16 | $scope.noun2 = word(resp); 17 | }); 18 | getWord($http, $timeout, '/words/adjective', function(resp) { 19 | var adj = word(resp); 20 | adj.word = adj.word.charAt(0).toUpperCase() + adj.word.substr(1) 21 | $scope.adjective1 = adj; 22 | }); 23 | getWord($http, $timeout, '/words/adjective', function(resp) { 24 | $scope.adjective2 = word(resp); 25 | }); 26 | getWord($http, $timeout, '/words/verb', function(resp) { 27 | $scope.verb = word(resp); 28 | }); 29 | }); 30 | 31 | function getWord($http, $timeout, url, callback) { 32 | $http.get(url).then(callback, function(resp) { 33 | $timeout(function() { 34 | console.log("Retry: " + url); 35 | getWord($http, $timeout, url, callback); 36 | }, 500); 37 | }); 38 | } 39 | 40 | function word(resp) { 41 | return { 42 | word: resp.data.word, 43 | hostname: resp.headers()["source"] 44 | }; 45 | } 46 | -------------------------------------------------------------------------------- /wordsmith-demo/web/dispatcher.go: -------------------------------------------------------------------------------- 1 | package main 2 | 3 | import ( 4 | "fmt" 5 | "io/ioutil" 6 | "log" 7 | "math/rand" 8 | "net" 9 | "net/http" 10 | "time" 11 | ) 12 | 13 | func main() { 14 | rand.Seed(time.Now().UnixNano()) 15 | 16 | fwd := &forwarder{"words", 8080} 17 | http.Handle("/words/", http.StripPrefix("/words", fwd)) 18 | http.Handle("/", http.FileServer(http.Dir("static"))) 19 | 20 | fmt.Println("Listening on port 80") 21 | err := http.ListenAndServe(":80", nil) 22 | if err != nil { 23 | fmt.Printf("Error while starting server: %v", err) 24 | } 25 | } 26 | 27 | type forwarder struct { 28 | host string 29 | port int 30 | } 31 | 32 | func (f *forwarder) ServeHTTP(w http.ResponseWriter, r *http.Request) { 33 | addrs, err := net.LookupHost(f.host) 34 | if err != nil { 35 | log.Println("Error", err) 36 | http.Error(w, err.Error(), 500) 37 | return 38 | } 39 | 40 | log.Printf("%s %d available ips: %v", r.URL.Path, len(addrs), addrs) 41 | ip := addrs[rand.Intn(len(addrs))] 42 | log.Printf("%s I choose %s", r.URL.Path, ip) 43 | 44 | url := fmt.Sprintf("http://%s:%d%s", ip, f.port, r.URL.Path) 45 | log.Printf("%s Calling %s", r.URL.Path, url) 46 | 47 | if err = copy(url, ip, w); err != nil { 48 | log.Println("Error", err) 49 | http.Error(w, err.Error(), 500) 50 | return 51 | } 52 | } 53 | 54 | func copy(url, ip string, w http.ResponseWriter) error { 55 | resp, err := http.Get(url) 56 | if err != nil { 57 | return err 58 | } 59 | 60 | w.Header().Set("source", ip) 61 | 62 | buf, err := ioutil.ReadAll(resp.Body) 63 | if err != nil { 64 | return err 65 | } 66 | 67 | _, err = w.Write(buf) 68 | return err 69 | } 70 | -------------------------------------------------------------------------------- /wordsmith-demo/web/static/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | Docker Compose demo 6 | 7 | 8 | 9 | 10 |
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44 | 45 | 46 | 47 | 48 | 49 | -------------------------------------------------------------------------------- /dockercon2020-demo/app/src/server.py: -------------------------------------------------------------------------------- 1 | import os 2 | import flask 3 | import json 4 | import mysql.connector 5 | 6 | # for debugging from Visual Studio Code -- turn off flask debugger first 7 | # import ptvsd 8 | # ptvsd.enable_attach(address=('0.0.0.0', 3000)) 9 | 10 | class DBManager: 11 | def __init__(self, database='example', host="db", user="root", password_file=None): 12 | pf = open(password_file, 'r') 13 | self.connection = mysql.connector.connect( 14 | user=user, 15 | password=pf.read(), 16 | host=host, 17 | database=database, 18 | auth_plugin='mysql_native_password' 19 | ) 20 | pf.close() 21 | self.cursor = self.connection.cursor() 22 | 23 | def populate_db(self): 24 | self.cursor.execute('DROP TABLE IF EXISTS blog') 25 | self.cursor.execute('CREATE TABLE blog (id INT AUTO_INCREMENT PRIMARY KEY, title VARCHAR(255))') 26 | self.cursor.executemany('INSERT INTO blog (id, title) VALUES (%s, %s);', [(i, 'Blog post #%d'% i) for i in range (1,5)]) 27 | self.connection.commit() 28 | 29 | def query_titles(self): 30 | self.cursor.execute('SELECT title FROM blog') 31 | rec = [] 32 | for c in self.cursor: 33 | rec.append(c[0]) 34 | return rec 35 | 36 | 37 | server = flask.Flask(__name__) 38 | conn = None 39 | 40 | @server.route('/blogs') 41 | def listBlog(): 42 | global conn 43 | if not conn: 44 | conn = DBManager(password_file='/run/secrets/db-password') 45 | conn.populate_db() 46 | rec = conn.query_titles() 47 | 48 | result = [] 49 | for c in rec: 50 | result.append(c) 51 | 52 | return flask.jsonify({"response": result}) 53 | 54 | @server.route('/') 55 | def hello(): 56 | return flask.jsonify({"response": "Hello from Docker!"}) 57 | 58 | 59 | if __name__ == '__main__': 60 | server.run(debug=True, host='0.0.0.0', port=5000) 61 | -------------------------------------------------------------------------------- /wordsmith-demo/web/static/style.css: -------------------------------------------------------------------------------- 1 | /* latin-ext */ 2 | @font-face { 3 | font-family: 'Raleway'; 4 | font-style: normal; 5 | font-weight: 400; 6 | src: local('Raleway'), local('Raleway-Regular'), url('fonts/font1.woff2') format('woff2'); 7 | unicode-range: U+0100-024F, U+1E00-1EFF, U+20A0-20AB, U+20AD-20CF, U+2C60-2C7F, U+A720-A7FF; 8 | } 9 | /* latin */ 10 | @font-face { 11 | font-family: 'Raleway'; 12 | font-style: normal; 13 | font-weight: 400; 14 | src: local('Raleway'), local('Raleway-Regular'), url('fonts/font2.woff2') format('woff2'); 15 | unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2212, U+2215; 16 | } 17 | 18 | body { 19 | text-align: center; 20 | margin: 0; 21 | padding: 0; 22 | background-color: #001f5b; 23 | } 24 | 25 | .logo { 26 | margin-top: 30px; 27 | } 28 | 29 | .footer { 30 | position: absolute; 31 | bottom: 0; 32 | width: 100%; 33 | z-index: -1; 34 | opacity: 0.5; 35 | } 36 | 37 | .footer img { 38 | max-width: 100%; 39 | vertical-align: middle; 40 | } 41 | 42 | .sentence { 43 | margin: 70px auto 0 auto; 44 | } 45 | 46 | .line { 47 | margin-bottom: 30px; 48 | transform: translateX(-100%) rotate(-20deg); 49 | } 50 | 51 | .slide-in { 52 | animation: slide-in .5s forwards ease-in; 53 | } 54 | 55 | .line3.slide-in { 56 | animation: slide-in 1s forwards ease-in; 57 | } 58 | 59 | .line2.slide-in { 60 | animation: slide-in 1.2s forwards ease-in; 61 | } 62 | 63 | @keyframes slide-in { 64 | 100% { 65 | transform: translateX(0%); 66 | } 67 | } 68 | 69 | .result { 70 | position: relative; 71 | display: inline-block; 72 | padding: 0 20px; 73 | margin: 0 10px; 74 | color: white; 75 | height: 175px; 76 | width: 330px; 77 | } 78 | 79 | .result .word { 80 | display: inline-block; 81 | font-family: 'Raleway', sans-serif; 82 | font-size: 45px; 83 | color: white; 84 | line-height: 155px; 85 | height: 175px; 86 | vertical-align: middle; 87 | margin-top: 20px; 88 | } 89 | 90 | .result .hostname { 91 | position: absolute; 92 | width: 100%; 93 | left: 0; 94 | bottom: 8px; 95 | font-size: 0.8em; 96 | height: 14px; 97 | } 98 | 99 | .noun { 100 | background-image: url('images/lego_blue.png') !important; 101 | } 102 | 103 | .verb { 104 | background-image: url('images/lego_yellow.png') !important; 105 | } 106 | 107 | .adjective { 108 | background-image: url('images/lego_light_blue.png') !important; 109 | } 110 | -------------------------------------------------------------------------------- /dockercon2020-demo/web/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | Hello! 5 | 6 | 7 | 46 | 47 | 48 | 49 |
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Hello there!

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Simple DEV environment setup with Docker and Docker Compose

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Set url path(default is '/'), then query the app service.

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Docker Compose DEMO!

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87 | 88 | 102 | 103 | -------------------------------------------------------------------------------- /wordsmith-demo/words/pom.xml: -------------------------------------------------------------------------------- 1 | 2 | 5 | 4.0.0 6 | 7 | codestory 8 | words 9 | 1.0-SNAPSHOT 10 | 11 | 12 | UTF-8 13 | 1.8 14 | 1.8 15 | 16 | 17 | 18 | words 19 | 20 | 21 | 22 | 23 | maven-clean-plugin 24 | 3.0.0 25 | 26 | 27 | maven-compiler-plugin 28 | 3.6.1 29 | 30 | 31 | maven-deploy-plugin 32 | 2.8.2 33 | 34 | 35 | maven-install-plugin 36 | 2.5.2 37 | 38 | 39 | maven-resources-plugin 40 | 3.0.2 41 | 42 | 43 | maven-site-plugin 44 | 3.6 45 | 46 | 47 | maven-release-plugin 48 | 2.5.3 49 | 50 | 51 | maven-surefire-plugin 52 | 2.19.1 53 | 54 | 55 | 56 | 57 | 58 | 59 | maven-dependency-plugin 60 | 3.0.0 61 | 62 | 63 | copy-dependencies 64 | package 65 | 66 | copy-dependencies 67 | 68 | 69 | 70 | 71 | 72 | maven-jar-plugin 73 | 3.0.2 74 | 75 | 76 | 77 | true 78 | dependency 79 | Main 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | com.google.guava 90 | guava 91 | 24.1.1 92 | 93 | 94 | org.postgresql 95 | postgresql 96 | 42.1.4 97 | 98 | 99 | 100 | -------------------------------------------------------------------------------- /wordsmith-demo/words/src/main/java/Main.java: -------------------------------------------------------------------------------- 1 | import com.google.common.base.Charsets; 2 | import com.google.common.base.Supplier; 3 | import com.google.common.base.Suppliers; 4 | import com.sun.net.httpserver.HttpHandler; 5 | import com.sun.net.httpserver.HttpServer; 6 | 7 | import java.lang.String; 8 | import java.nio.file.*; 9 | import java.io.*; 10 | import java.nio.charset.StandardCharsets; 11 | import java.net.InetSocketAddress; 12 | import java.sql.*; 13 | import java.util.NoSuchElementException; 14 | 15 | public class Main { 16 | public static int dbPopulateRetry; 17 | public static HttpServer server; 18 | public static void main(String[] args) throws Exception { 19 | dbPopulateRetry = 7; 20 | Class.forName("org.postgresql.Driver"); 21 | 22 | server = HttpServer.create(new InetSocketAddress(8080), 0); 23 | server.createContext("/noun", handler(Suppliers.memoize(() -> randomWord("nouns")))); 24 | server.createContext("/verb", handler(Suppliers.memoize(() -> randomWord("verbs")))); 25 | server.createContext("/adjective", handler(Suppliers.memoize(() -> randomWord("adjectives")))); 26 | server.start(); 27 | } 28 | private static void refreshContext() throws IOException { 29 | server.removeContext("/noun"); 30 | server.removeContext("/verb"); 31 | server.removeContext("/adjective"); 32 | server.createContext("/noun", handler(Suppliers.memoize(() -> randomWord("nouns")))); 33 | server.createContext("/verb", handler(Suppliers.memoize(() -> randomWord("verbs")))); 34 | server.createContext("/adjective", handler(Suppliers.memoize(() -> randomWord("adjectives")))); 35 | } 36 | private static String populateDB() throws IOException { 37 | 38 | try (Connection connection = DriverManager.getConnection("jdbc:postgresql://db:5432/postgres", "postgres", "")) { 39 | 40 | DatabaseMetaData dbm = connection.getMetaData(); 41 | ResultSet table = dbm.getTables(null, null, "nouns", null); 42 | if (table.next()) { 43 | return null; 44 | } 45 | try (Statement statement = connection.createStatement()) { 46 | String tables = new String(Files.readAllBytes(Paths.get("/app/resources/table.sql"))); 47 | statement.executeUpdate(tables); 48 | 49 | String words = new String(Files.readAllBytes(Paths.get("/app/resources/words.sql"))); 50 | statement.executeUpdate(words); 51 | } 52 | } catch (SQLException e) { 53 | e.printStackTrace(); 54 | } 55 | throw new NoSuchElementException("nouns"); 56 | } 57 | 58 | 59 | private static String randomWord(String table) { 60 | try (Connection connection = DriverManager.getConnection("jdbc:postgresql://db:5432/postgres", "postgres", "")) { 61 | try (Statement statement = connection.createStatement()) { 62 | try (ResultSet set = statement.executeQuery("SELECT word FROM " + table + " ORDER BY random() LIMIT 1")) { 63 | while (set.next()) { 64 | return set.getString(1); 65 | } 66 | } 67 | } 68 | } catch (SQLException e) { 69 | try { 70 | dbPopulateRetry = dbPopulateRetry - 1; 71 | if (dbPopulateRetry < 0) { 72 | throw new IOException("unable to populate DB"); 73 | } 74 | populateDB(); 75 | randomWord(table); 76 | } 77 | catch(IOException ie) { 78 | e.printStackTrace(); 79 | ie.printStackTrace(); 80 | } 81 | } 82 | throw new NoSuchElementException(table); 83 | } 84 | 85 | 86 | 87 | private static HttpHandler handler(Supplier word) { 88 | return t -> { 89 | String response = "{\"word\":\"" + word.get() + "\"}"; 90 | byte[] bytes = response.getBytes(Charsets.UTF_8); 91 | 92 | System.out.println(response); 93 | t.getResponseHeaders().add("content-type", "application/json; charset=utf-8"); 94 | t.sendResponseHeaders(200, bytes.length); 95 | 96 | try (OutputStream os = t.getResponseBody()) { 97 | os.write(bytes); 98 | } 99 | refreshContext(); 100 | }; 101 | } 102 | } 103 | -------------------------------------------------------------------------------- /wordsmith-demo/web/static/images/logo.svg: -------------------------------------------------------------------------------- 1 | 2 | 3 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | -------------------------------------------------------------------------------- /wordsmith-demo/img/dockercon-barcelona-logo.svg: -------------------------------------------------------------------------------- 1 | 2 | 3 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | -------------------------------------------------------------------------------- /gpublog-demo/backend/model.py: -------------------------------------------------------------------------------- 1 | import io 2 | import os 3 | import re 4 | import sys 5 | import time 6 | import unicodedata 7 | 8 | import numpy as np 9 | import tensorflow as tf 10 | from sklearn.model_selection import train_test_split 11 | import functools 12 | import requests 13 | 14 | # 15 | # This code sample is copied from the tensorflow docs at 16 | # https://www.tensorflow.org/tutorials/text/nmt_with_attention 17 | # 18 | 19 | # Converts the unicode file to ascii 20 | def unicode_to_ascii(s): 21 | return ''.join(c for c in unicodedata.normalize('NFD', s) if unicodedata.category(c) != 'Mn') 22 | 23 | def preprocess_sentence(w): 24 | w = unicode_to_ascii(w.lower().strip()) 25 | 26 | # creating a space between a word and the punctuation following it 27 | # eg: "he is a boy." => "he is a boy ." 28 | # Reference:- https://stackoverflow.com/questions/3645931/python-padding-punctuation-with-white-spaces-keeping-punctuation 29 | w = re.sub(r"([?.!,¿])", r" \1 ", w) 30 | w = re.sub(r'[" "]+', " ", w) 31 | 32 | # replacing everything with space except (a-z, A-Z, ".", "?", "!", ",") 33 | w = re.sub(r"[^a-zA-Z?.!,¿]+", " ", w) 34 | 35 | w = w.strip() 36 | 37 | # adding a start and an end token to the sentence 38 | # so that the model know when to start and stop predicting. 39 | w = ' ' + w + ' ' 40 | return w 41 | 42 | def loss_function(real, pred): 43 | loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True, reduction='none') 44 | 45 | mask = tf.math.logical_not(tf.math.equal(real, 0)) 46 | loss_ = loss_object(real, pred) 47 | 48 | mask = tf.cast(mask, dtype=loss_.dtype) 49 | loss_ *= mask 50 | 51 | return tf.reduce_mean(loss_) 52 | 53 | 54 | def tokenize(lang): 55 | lang_tokenizer = tf.keras.preprocessing.text.Tokenizer( 56 | filters='') 57 | lang_tokenizer.fit_on_texts(lang) 58 | tensor = lang_tokenizer.texts_to_sequences(lang) 59 | tensor = tf.keras.preprocessing.sequence.pad_sequences(tensor, padding='post') 60 | 61 | return tensor, lang_tokenizer 62 | 63 | class Encoder(tf.keras.Model): 64 | def __init__(self, vocab_size, embedding_dim, enc_units, batch_sz): 65 | super(Encoder, self).__init__() 66 | self.batch_sz = batch_sz 67 | self.enc_units = enc_units 68 | self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim) 69 | self.gru = tf.keras.layers.GRU(self.enc_units, 70 | return_sequences=True, 71 | return_state=True, 72 | recurrent_initializer='glorot_uniform') 73 | 74 | def call(self, x, hidden): 75 | x = self.embedding(x) 76 | output, state = self.gru(x, initial_state=hidden) 77 | return output, state 78 | 79 | def initialize_hidden_state(self): 80 | return tf.zeros((self.batch_sz, self.enc_units)) 81 | 82 | 83 | class BahdanauAttention(tf.keras.layers.Layer): 84 | def __init__(self, units): 85 | super(BahdanauAttention, self).__init__() 86 | self.W1 = tf.keras.layers.Dense(units) 87 | self.W2 = tf.keras.layers.Dense(units) 88 | self.V = tf.keras.layers.Dense(1) 89 | 90 | def call(self, query, values): 91 | # query hidden state shape == (batch_size, hidden size) 92 | # query_with_time_axis shape == (batch_size, 1, hidden size) 93 | # values shape == (batch_size, max_len, hidden size) 94 | # we are doing this to broadcast addition along the time axis to calculate the score 95 | query_with_time_axis = tf.expand_dims(query, 1) 96 | 97 | # score shape == (batch_size, max_length, 1) 98 | # we get 1 at the last axis because we are applying score to self.V 99 | # the shape of the tensor before applying self.V is (batch_size, max_length, units) 100 | score = self.V(tf.nn.tanh( 101 | self.W1(query_with_time_axis) + self.W2(values))) 102 | 103 | # attention_weights shape == (batch_size, max_length, 1) 104 | attention_weights = tf.nn.softmax(score, axis=1) 105 | 106 | # context_vector shape after sum == (batch_size, hidden_size) 107 | context_vector = attention_weights * values 108 | context_vector = tf.reduce_sum(context_vector, axis=1) 109 | 110 | return context_vector, attention_weights 111 | 112 | 113 | class Decoder(tf.keras.Model): 114 | def __init__(self, vocab_size, embedding_dim, dec_units, batch_sz): 115 | super(Decoder, self).__init__() 116 | self.batch_sz = batch_sz 117 | self.dec_units = dec_units 118 | self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim) 119 | self.gru = tf.keras.layers.GRU(self.dec_units, 120 | return_sequences=True, 121 | return_state=True, 122 | recurrent_initializer='glorot_uniform') 123 | self.fc = tf.keras.layers.Dense(vocab_size) 124 | 125 | # used for attention 126 | self.attention = BahdanauAttention(self.dec_units) 127 | 128 | def call(self, x, hidden, enc_output): 129 | # enc_output shape == (batch_size, max_length, hidden_size) 130 | context_vector, attention_weights = self.attention(hidden, enc_output) 131 | 132 | # x shape after passing through embedding == (batch_size, 1, embedding_dim) 133 | x = self.embedding(x) 134 | 135 | # x shape after concatenation == (batch_size, 1, embedding_dim + hidden_size) 136 | x = tf.concat([tf.expand_dims(context_vector, 1), x], axis=-1) 137 | 138 | # passing the concatenated vector to the GRU 139 | output, state = self.gru(x) 140 | 141 | # output shape == (batch_size * 1, hidden_size) 142 | output = tf.reshape(output, (-1, output.shape[2])) 143 | 144 | # output shape == (batch_size, vocab) 145 | x = self.fc(output) 146 | 147 | return x, state, attention_weights 148 | 149 | 150 | 151 | 152 | 153 | class TranslatorModel: 154 | BATCH_SIZE = 64 155 | embedding_dim = 256 156 | units = 1024 157 | url = 'http://storage.googleapis.com/download.tensorflow.org/data' 158 | 159 | 160 | def __init__(self, lang='eng-fra'): 161 | self.lang = lang 162 | self.optimizer = tf.keras.optimizers.Adam() 163 | 164 | self.checkpoint_dir = "/checkpoints/{}".format(lang) 165 | self.checkpoint_prefix = os.path.join(self.checkpoint_dir, "ckpt") 166 | 167 | 168 | self._load(num_examples = 30000) 169 | 170 | self.encoder = Encoder(self.vocab_inp_size, self.embedding_dim, self.units, self.BATCH_SIZE) 171 | self.decoder = Decoder(self.vocab_tar_size, self.embedding_dim, self.units, self.BATCH_SIZE) 172 | 173 | 174 | 175 | self.checkpoint = tf.train.Checkpoint(optimizer=self.optimizer, 176 | encoder=self.encoder, 177 | decoder=self.decoder) 178 | 179 | # restoring the latest checkpoint in checkpoint_dir 180 | self.checkpoint.restore(tf.train.latest_checkpoint(self.checkpoint_dir)) 181 | 182 | 183 | def _load_dataset(self, num_examples = None): 184 | lang1, lang2 = self.lang.split('-') 185 | if self.lang.startswith('eng-'): 186 | lang2, lang1 = self.lang.split('-') 187 | 188 | path_to_file = "/code/data/{}.txt".format(lang1) 189 | if not os.path.isfile(path_to_file): 190 | archive = '{}-{}.zip'.format(lang1, lang2) 191 | # Download the file 192 | path_to_zip = tf.keras.utils.get_file( 193 | archive, 194 | origin='{}/{}'.format(self.url, archive), 195 | extract=True) 196 | 197 | path_to_file = os.path.dirname(path_to_zip)+"/{}.txt".format(lang1) 198 | 199 | # load dataset 200 | lines = io.open(path_to_file, encoding='UTF-8').read().strip().split('\n') 201 | if num_examples is None: 202 | num_examples = len(lines) 203 | word_pairs = [[preprocess_sentence(w) for w in l.split( 204 | '\t')] for l in lines[:num_examples]] 205 | 206 | return zip(*word_pairs) 207 | 208 | 209 | def _load(self, num_examples = None): 210 | # creating cleaned input, output pairs 211 | if self.lang.startswith('eng-'): 212 | self.inp_lang, self.targ_lang = self._load_dataset(num_examples) 213 | else: 214 | self.targ_lang, self.inp_lang = self._load_dataset(num_examples) 215 | 216 | self.input_tensor, self.inp_lang_tokenizer = tokenize(self.inp_lang) 217 | self.target_tensor, self.targ_lang_tokenizer = tokenize(self.targ_lang) 218 | 219 | # Calculate max_length of the target tensors 220 | self.max_length_targ = self.target_tensor.shape[1] 221 | self.max_length_inp = self.input_tensor.shape[1] 222 | self.vocab_inp_size = len(self.inp_lang_tokenizer.word_index)+1 223 | self.vocab_tar_size = len(self.targ_lang_tokenizer.word_index)+1 224 | 225 | # Creating training and validation sets using an 80-20 split 226 | input_tensor_train, input_tensor_val, target_tensor_train, target_tensor_val = train_test_split( 227 | self.input_tensor, self.target_tensor, test_size=0.2) 228 | 229 | self.BUFFER_SIZE = len(input_tensor_train) 230 | self.steps_per_epoch = len(input_tensor_train)//self.BATCH_SIZE 231 | 232 | 233 | dataset = tf.data.Dataset.from_tensor_slices( 234 | (input_tensor_train, target_tensor_train)).shuffle(self.BUFFER_SIZE) 235 | self.dataset = dataset.batch(self.BATCH_SIZE, drop_remainder=True) 236 | 237 | @tf.function 238 | def train_step(self, inp, targ, enc_hidden): 239 | loss = 0 240 | 241 | with tf.GradientTape() as tape: 242 | enc_output, enc_hidden = self.encoder(inp, enc_hidden) 243 | dec_hidden = enc_hidden 244 | dec_input = tf.expand_dims( 245 | [self.targ_lang_tokenizer.word_index['']] * self.BATCH_SIZE, 1) 246 | 247 | # Teacher forcing - feeding the target as the next input 248 | for t in range(1, targ.shape[1]): 249 | # passing enc_output to the decoder 250 | predictions, dec_hidden, _ = self.decoder( 251 | dec_input, dec_hidden, enc_output) 252 | 253 | loss += loss_function(targ[:, t], predictions) 254 | 255 | # using teacher forcing 256 | dec_input = tf.expand_dims(targ[:, t], 1) 257 | 258 | batch_loss = (loss / int(targ.shape[1])) 259 | variables = self.encoder.trainable_variables + self.decoder.trainable_variables 260 | gradients = tape.gradient(loss, variables) 261 | self.optimizer.apply_gradients(zip(gradients, variables)) 262 | return batch_loss 263 | 264 | 265 | def train(self, epochs = 10): 266 | for epoch in range(epochs): 267 | start = time.time() 268 | 269 | enc_hidden = self.encoder.initialize_hidden_state() 270 | total_loss = 0 271 | 272 | for (batch, (inp, targ)) in enumerate(self.dataset.take(self.steps_per_epoch)): 273 | batch_loss = self.train_step(inp, targ, enc_hidden) 274 | total_loss += batch_loss 275 | 276 | if batch % 100 == 0: 277 | print('Epoch {} Batch {} Loss {:.4f}'.format(epoch + 1, batch, batch_loss.numpy())) 278 | # saving (checkpoint) the model every 2 epochs 279 | if (epoch + 1) % 2 == 0: 280 | self.checkpoint.save(file_prefix = self.checkpoint_prefix) 281 | 282 | print('Epoch {} Loss {:.4f}'.format(epoch + 1, total_loss / self.steps_per_epoch)) 283 | print('Time taken for 1 epoch {} sec\n'.format(time.time() - start)) 284 | 285 | 286 | 287 | 288 | 289 | def evaluate(self, sentence): 290 | sentence = preprocess_sentence(sentence) 291 | 292 | inputs = [self.inp_lang_tokenizer.word_index[i] for i in sentence.split(' ')] 293 | inputs = tf.keras.preprocessing.sequence.pad_sequences([inputs], 294 | maxlen=self.max_length_inp, 295 | padding='post') 296 | inputs = tf.convert_to_tensor(inputs) 297 | 298 | result = '' 299 | 300 | hidden = [tf.zeros((1, self.units))] 301 | enc_out, enc_hidden = self.encoder(inputs, hidden) 302 | 303 | dec_hidden = enc_hidden 304 | dec_input = tf.expand_dims([self.targ_lang_tokenizer.word_index['']], 0) 305 | 306 | for t in range(self.max_length_targ): 307 | predictions, dec_hidden, attention_weights = self.decoder(dec_input, 308 | dec_hidden, 309 | enc_out) 310 | predicted_id = tf.argmax(predictions[0]).numpy() 311 | if self.targ_lang_tokenizer.index_word[predicted_id] == '': 312 | break 313 | result += self.targ_lang_tokenizer.index_word[predicted_id] + ' ' 314 | # the predicted ID is fed back into the model 315 | dec_input = tf.expand_dims([predicted_id], 0) 316 | 317 | return result, sentence 318 | 319 | 320 | 321 | def translate(self, sentence): 322 | result, _ = self.evaluate(sentence) 323 | 324 | print('Input: %s' % (sentence)) 325 | print('Predicted translation: {}'.format(result)) 326 | return result 327 | 328 | 329 | 330 | 331 | 332 | if __name__ == '__main__': 333 | lang = "eng-fra" 334 | if len(sys.argv) > 1: 335 | lang = sys.argv[1] 336 | 337 | if len(sys.argv) > 2: 338 | epochs = int(sys.argv[2]) 339 | 340 | model = TranslatorModel(lang) 341 | epochs = 10 342 | print("Translator services is requested to reload the model every 10 epochs.") 343 | while True: 344 | model.train(epochs) 345 | print("Checkpoints saved in {}".format(model.checkpoint_dir)) 346 | response = requests.get("http://translator:5000/reload", params={'lang': lang}) 347 | print("Requested translator service to reload its model, response status: ", response.status_code) 348 | print("Exiting...") 349 | 350 | 351 | -------------------------------------------------------------------------------- /wordsmith-demo/web/static/angular.min.js: -------------------------------------------------------------------------------- 1 | /* 2 | AngularJS v1.5.3 3 | (c) 2010-2016 Google, Inc. http://angularjs.org 4 | License: MIT 5 | */ 6 | (function(T,P,u){'use strict';function O(a){return function(){var 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