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CS4300 - Flask Template


This Flask app template is intended to get you started with your project and launch it on Heroku, and assumes no prior experience with web development (but some patience). If you have any questions dont hesistate to ask the TAs or come to OH. In this README I will include an overview section with information on the flask app architecture and a step-by-step guide to loading up your app in dev and production (in Heroku) with instructions for (optional) EC2/EB add-ons addcoming soon. This README was written by Ilan Filonenko with help from Joseph Antonakakis.

Table of Contents




AWS Deployment


Overview of the project and Introduction to Flask

This will overview Flask development operations for setting up a new project with an emphasis on the Model-View-Controller design pattern.

This guide will be utilizing PostgreSQL to drive persistent storage on the backend.

Get PyPI

This guide depends on you being able to easily download Python modules. In order to do so, you should get PyPI. Follow the basic guide here.

Virtualenv - The Key to Python Projects

Before even touching Flask, you should be introduced into virtualenv (if you have not already seen this amazing tool). Virtualenv allows you to create an isolated environment to build and run a Python project in. All dependencies for the project can be freshly declared and utilized, and the project can, therefore, be built and executed in a modular and isolated fashion. In addition, if you download a preexisting Python project, you can create a virtual environment with virtualenv to install and store all dependencies for the project. Think of it like your node_modules file if you come from Node.js, or your project gems if you come from Ruby on Rails. To install, go here. For dead-simple usage, go here.

Once you have virtualenv setup, create an actual virtual environment with the following command:

virtualenv venv

In the above example, I chose to name the environment venv, but you can name it whatever you'd like.

To activate and enter the virtual environment, run the following:

source venv/bin/activate

The following command line prompt will indicate that you're in the virtual environment:

(venv) >

To deactivate the virtual environment, run the following:


I have inluded a requirements.txt file in the project. The typical workflow will be to install something via pip, (whatever you want) and then run the following:

pip freeze > requirements.txt

The command pip freeze actually lists the dependencies encapsulated by the virtual environment. The above command copies those into a text file that will allow one to run the following on downloading and using the Python project:

pip install -r requirements.txt

NOTE: The .env file is intentionally NOT .gitignore-ed.

Flask App


A Flask app has some utility scripts at the top-level, and has a modular organization when defining any sort of functionality. Dividing up a Flask app into modules allows one to separate resource / logic concerns.

The utility scripts at the top level include the following: # describes different environments that app runs in # holds functionality for migrating your database (changing its schema)    # runs the app on a port

The entire functional backend of a Flask app is housed in a parent module called app. You can create this by creating a directory app and populating it with an file. Then, inside that app directory, you can create modules that describe the resources of your app. These modules should be as de-coupled and reusable as possible. For example, let's say I need a bunch of user authentication logic described by a couple of endpoints and helper functions. These might be useful in another Flask app and can be comfortably separated from other functionality. As a result, I would make a module called accounts inside my app directory. Each module (including app) should also have a templates directory if you plan on adding any HTML views to your app.


The use of templates here is specifically for the purpose of mimicing the structure of an MVC application. In this application I have seperated the system into two seperate templates: accounts and irsystem, since some of you might need to leverage the database for user/session log flow so you would only use the irsystem template. The irsystem is what you will be manipulating for the purposes of your information retrevial. If you look at the file you can see that we are rendering the view with data being passed in. This data will the results from your IR system which you will customize accordingly. You may make more models/controllers for organization purposes.

Database Setup

In this example, as stated, PostgreSQL (or Postgres) will be the database leveraged. Postgres can be installed a multitude of ways, but if you're on OSX I recommend utilizing the Postgres App.

Once you have Postgres setup and have your $PATH configured accordingly, run the following:

# Enter postgres command line interface
$ psql
# Create your database
# Quit out

The above creates the actual database that will be used for this application and the name of the database is my_app_db which you can change, but make sure to change the .env and in your production app accordingly which I will talk about lower in this guide.

Rather than writing raw-SQL for this application, I have chosen to utilize SQLAlchemy (specifically, Flask-SQLAlchemy) as a database Object-Relational-Model (ORM, for short). In addition, for the purposes of serialization (turning these database entities into organized JSONs that we can send over the wire) and deserialization (turning a JSON into a entity once again), I have chosen to use Marshmallow (specifically, marshmallow-SQLAlchemy).

Several modules are needed to completely integrate Postgres into a Flask app, but several of these modules are co-dependent on one another. I have included all of these in the requirements.txt file, these modules include: flask-migrate marshmallow-sqlalchemy psycopg2.

The migration script, will be used to capture changes you make over time to the schemas of your various models.
This script will not work out of the gate and refers to components we have not yet defined in our app, but I will describe these below:

import os
from flask_script import Manager
from flask_migrate import Migrate, MigrateCommand
from app import app, db

migrate = Migrate(app, db)
manager = Manager(app)

manager.add_command("db", MigrateCommand)

if __name__ == "__main__":

In the above, app refers to the module we created above in the File Organization section. db refers to our reference to the database connection that we have yet to define in the app module.

This script can be used in the following way to migrate your database, on changing your models:

# Initialize migrations
python db init
# Create a migration
python db migrate
# Apply it to the DB
python db upgrade

You should run these methods after having the .env setup because it requires the APP_SETTINGS and DATABASE_URL to be defined. If you get errors in this section as a result of key-errors for APP_SETTINGS and DATABASE_URL go to the Environmental Variables section and make sure to delete the migrations folder that is already created with running python db init

Configuration Setup

Now that we have setup our database and have handled our script, we can create our script, which involves the database and various other configuration information specific to Flask. This file will be used in our initialization of the Flask app in the app module in the near future.

An example of a file that is used in the project looks like this:

import os
basedir = os.path.abspath(os.path.dirname(__file__))

# Different environments for the app to run in

class Config(object):
  DEBUG = False
  CSRF_SESSION_KEY = "secret"
  SECRET_KEY = "not_this"

class ProductionConfig(Config):
  DEBUG = False

class StagingConfig(Config):
  DEBUG = True

class DevelopmentConfig(Config):
  DEBUG = True

class TestingConfig(Config):
  TESTING = True

The above defines several classes used to instantiate configuration objects in the creation of a Flask app. Let's go through some of the variables:

  • DEBUG indicates whether or not debug stack traces will be logged by the server.
  • CSRF_ENABLED, CSRF_SESSION_KEY, and SECRET_KEY all relate to Cross-Site-Request-Forgery, which you can read more about here.
  • SQLALCHEMY_DATABASE_URI refers to the database URL (a server running your database). In the above example, I refer to an environment variable 'DATABASE_URL'. I will be discussing environment variables in the next section, so stay tuned.

Environmental Variables

Environment variables allow one to specify credentials like a sensitive database URL, API keys, secret keys, etc. These variables can be manually export-ed in the shell that you are running your server in, but that is a clunky approach. The tool autoenv solves this problem.

autoenv allows for environment variable loading on cd-ing into the base directory of the project. Follow the following command line arguments to install autoenv:

# Install the package from pip
pip install autoenv
# Override cd by adding this to your .?rc file (? = bash, zsh, fish, etc), I'll use
echo "source `which activate`" >> ~/.?rc
# Reload your shell
source ~/.?rc
# Make a .env file to hold variables
touch .env

As mentioned in the above code, your .env file will be where you hold variables, and will look something like this:

# Set the environment type of the app (see
export APP_SETTINGS=config.DevelopmentConfig
# Set the DB url to a local database for development
export DATABASE_URL=postgresql://localhost/my_app_db

As you can see above in the example, I reference a specific configuration class (DevelopmentConfig), meaning I plan on working in my development environment. I also have my database URL. Both of which are used heavily in the app. In local mode you will be maniuplating the .env file but in production you will be manipulating the Config Variables in your Heroku instance or you will modify the .env files in your AWS EC2/EB application.

NOTE: Be sure to gitignore your .env file.

Flask App Setup

Up until now, we haven't been able to run our server.

The configurations of the Flask app are contained in ./app/ The file should look like this:

# Gevent needed for sockets
from gevent import monkey

# Imports
import os
from flask import Flask, render_template
from flask_sqlalchemy import SQLAlchemy
from flask_socketio import SocketIO

# Configure app
socketio = SocketIO()
app = Flask(__name__)

# DB
db = SQLAlchemy(app)

# Import + Register Blueprints
# from import blue as blue_print
# app.register_blueprint(blue_print)

# Initialize app w/SocketIO

# HTTP error handling
def not_found(error):
  return render_template("404.html"), 404

Let's unpack this file piece by piece. The top initializes Gevent, a coroutine-based Python networking library for Socket.IO (a very useful socket library useful in adding real-time sockets to your app). The next section imports several libraries, initializes our Flask app, set our app up with the configurations from the appropriate class, creates the db connection pool, bootstraps Socket.IO to our Flask app, and sets the 404 error page that the app should present on not finding a resource. NOTE: the comments regarding registering a "blueprint" will be where we register our sub-modules with our main, parent Flask app.

Since we have involved Socket.IO and Gevent, you wil see it in requirements.txt

Finally, in order to actually start our server, you will run the script. This script can be placed at the root of our project:

from app import app, socketio

if __name__ == "__main__":
  print "Flask app running at", host="", port=5000)

Now, at the root of your application, you can run:


Your server is now running!

NOTE: If you get issues regarding APP_SETTINGS or DATABASE_URL, you should ensure your .env is setup properly, and you should cd out of and back into your project root.

That's it, for now...

This marks the end of project configuration for a well-constructed Flask app following MVC. However, for additional development-related advice regarding project setup, keep reading.

Accounts Blueprint

Now we will be diving into writing Models and Controllers for an accounts blueprint that will serve as reusable users-sessions module that can be added to any application desiring a sign-up system. We make some short-cuts along the way in order to increase this guide's brevity, while still providing meaningful sample code and explanations for the different components of the system.

We must create our module within app, such that it contains the following structure:

├── controllers
│   ├──
│   ├──
│   └──
└── models

Let's start with ./app/accounts/ This file contains a couple of lines of information specifying the Flask Blueprint information of this module, as well import controllers:

from flask import Blueprint

# Define a Blueprint for this module (mchat)
accounts = Blueprint('accounts', __name__, url_prefix='/accounts')

# Import all controllers
from controllers.users_controller import *
from controllers.sessions_controller import *

In addition, register your new blueprint in ./app/ by changing the lines:

# Import + Register Blueprints
# from import blue as blue_print
# app.register_blueprint(blue_print)


# Import + Register Blueprints
from app.accounts import accounts as accounts

The Models

The models created will correspond, field-by-field, to the database tables that will be setup as a result of you running the series of migration commands listed above in the Database Setup section.

Base Model and Imports

Before looking into the Base model (the abstract model parent class that all models will extend from) we will look at Werkzeug, included in your requirements.txt, which is a module that is required to hash user-passwords (you never want to store passwords in plain-text). We should, technically, salt the passwords too, but given that this is just an example, I'll leave that detail to your implementation.

In ./models/, (accessible to the entire models module) we should write the following:

from app import db # Grab the db from the top-level app
from marshmallow_sqlalchemy import ModelSchema # Needed for serialization in each model
from werkzeug import check_password_hash, generate_password_hash # Hashing
import hashlib # For session_token generation (session-based auth. flow)

class Base(db.Model):
  """Base PostgreSQL model"""
  __abstract__ = True
  id         = db.Column(db.Integer, primary_key =True)
  created_at = db.Column(db.DateTime, default    =db.func.current_timestamp())
  updated_at = db.Column(db.DateTime, default    =db.func.current_timestamp())

The above imports modules and objects necessary for use in your models, as well as defines a Base model class that every model should extend to inherit the book-keeping and necessary fields, id, created_at, and updated_at.

User Model

The User model in ./models/ will consist of the following:

from . import *

class User(Base):
  __tablename__ = 'users'

  email           = db.Column(db.String(128), nullable =False, unique =True)
  fname           = db.Column(db.String(128), nullable =False)
  lname           = db.Column(db.String(128), nullable =False)
  password_digest = db.Column(db.String(192), nullable =False)

  def __init__(self, ** kwargs):           = kwargs.get('email', None)
    self.fname           = kwargs.get('fname', None)
    self.lname           = kwargs.get('lname', None)
    self.password_digest = generate_password_hash(kwargs.get('password'), None)

  def __repr__(self):
    return str(self.__dict__)

class UserSchema(ModelSchema):
  class Meta:
    model = User

The above is pretty self-explanatory. It outlines several db fields, declares a constructor, defines a default string-based representation of the User model, and declares a UserSchema class that will be used to serialize and deserialize the User model to / from JSON.

Session Model

The Session model in ./models/ will consist of the following:

from . import *

class Session(Base):
  __tablename__ = 'sessions'

  user_id       = db.Column(db.Integer, db.ForeignKey(''), unique =True, index =True)
  session_token = db.Column(db.String(40))
  update_token  = db.Column(db.String(40))
  expires_at    = db.Column(db.DateTime)

  def __init__(self, ** kwargs):
    user = kwargs.get('user', None)
    if user is None:
      raise Exception() # Shouldn't be the case

    self.user_id       =
    self.session_token = self.urlsafe_base_64()
    self.update_token  = self.urlsafe_base_64()
    self.expires_at    = + datetime.timedelta(days=7)

  def __repr__(self):
    return str(self.__dict__)

  def urlsafe_base_64(self):
    return hashlib.sha1(os.urandom(64)).hexdigest()

class SessionSchema(ModelSchema):
  class Meta:
    model = Session

As we can see, the session model will belong to the user, and be part of a token-based authentication flow. Everything in the above code is self-explanatory, and serves as an example session implementation.

Exposing Models to the App

We must import our models into our app in order to have them be exposed to our script responsible for migrating our db to match our programmatic schema. In ./controllers/, I added the following (we'll throw in the imports while we're at it):

from functools import wraps
from flask import request, jsonify, abort
import os

# Import module models
from app.accounts.models.user import *
from app.accounts.models.session import *

For other models and controllers you add with database connection you can safely run the following in the root of your project to migrate your database:

# Initialize migrations
python db init
# Create a migration
python db migrate
# Apply it to the DB
python db upgrade

Now if you connect to your Postgres database, you should see two new tables, users and sessions! The migration also creates an index on foreign-key user_id in sessions, for fast access of sessions by their owning user's id.

That's it for models.

Additional Features Added

In addition to the flask application I have added some useful encoding features that can be leveraged by your application. Because we leverage numpy arrays all the time when calculating doc-by-vocab matricies I have included some encoding techniques for 2D numpy matricies which I will review soon.


If you are using Heroku or AWS EC2/EB you have a limited number of RAM and in-memory space to store your json data. As such it is recommended that you leverage SVDs on your doc-by-vocab matricies to reduce the dimensionality of your data. Because text-data is ALWAYS dimensionally reducible you should leverage the techniques covered in class in your application. To have fast responses and limited logic I would recommend to pre-process all of your data structures and numpy arrays and store them in some storage system. Two storage systems that I would recommend include: Amazon S3 and Redis.

Amazon S3

After setting up an AWS account and buying some space on your S3 server you can easily put data into your S3 bucket with this simple command:


I would recommend storing all your datastructures in json files and pushing those jsons to S3 in your pre-processing stages, and pulling from S3 at run-time be leveraging the encoding techniques that I have included in app.irsystem.models.helpers.

class NumpyEncoder(json.JSONEncoder):

    def default(self, obj):
        """If input object is an ndarray it will be converted into a dict
        holding dtype, shape and the data, base64 encoded.
        if isinstance(obj, np.ndarray):
            if obj.flags['C_CONTIGUOUS']:
                obj_data =
                cont_obj = np.ascontiguousarray(obj)
                obj_data =
            data_b64 = base64.b64encode(obj_data)
            return dict(__ndarray__=data_b64,
        # Let the base class default method raise the TypeError
        return json.JSONEncoder(self, obj)

def json_numpy_obj_hook(dct):
    """Decodes a previously encoded numpy ndarray with proper shape and dtype.
    :param dct: (dict) json encoded ndarray
    :return: (ndarray) if input was an encoded ndarray
    if isinstance(dct, dict) and '__ndarray__' in dct:
        data = base64.b64decode(dct['__ndarray__'])
        return np.frombuffer(data, dct['dtype']).reshape(dct['shape'])
    return dct

I will show you how to use these encoding techniques below:

from app.irsystem.models.helpers import NumpyEncoder as NumpyEncoder
from app.irsystem.models.helpers import json_numpy_obj_hook
# Dump numpy array into a json file
json.dump(NUMPY_ARRAY_NAME, open('NUMPY_ARRAY_NAME.json', 'w'), cls=NumpyEncoder)
# Read numpy array from a json file (where FILE_NAME is an S3 location or local file)
NUMPY_ARRAY_NAME = json.load(FILE_NAME, object_hook=json_numpy_obj_hook, encoding='utf8')

Redis is an in-memory data structure store, used as a database, cache and message broker. It supports data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs and geospatial indexes with radius queries. Redis has built-in replication, Lua scripting, LRU eviction, transactions and different levels of on-disk persistence, and provides high availability via Redis Sentinel and automatic partitioning with Redis Cluster. In this application I will be leveraging the python bindings provided by redis-py which allow for me to interact with the available redis cluster using python. You can read more about Redis and its useful for ML applications via its in-memory nature here. You can setup your redis cluster using either the Ansible script provided here or the Kubernetes Helm chart provided here. The importance of using the above setup logic is to include the redis-ml support for matrix manipulation. After deploying your cluster to your appropriate port (i.e. Check if redis-server is up by running and getting PONG as a result, hehe :)

$ redis-cli ping

You can modify the Redis DB by running:

$ redis-cli
redis> ping
redis> set mykey somevalue
redis> get mykey

At this point in time you are able to use the RedisConnector provided by CuAppDev. To create a Redis connection to the redis cluster you will execute the following:

from appdev.connectors import MySQLConnector, RedisConnector
redis = RedisConnector('entry_checkpoint')

The normal TCP socket based connection will be available by calling:

connection = redis._single_connect()

And if you want to leverage connection pools to manage connections to the redis server with finer grain control and client side sharding you can use this by calling:

pool = redis._connect_pool(5) # for max 5 connections

You can now run the following cmomands to store a numpy matrix into Redis

# Create RedisConn Class
redis = RedisConnector('entry_checkpoint')
# Grab TCP Connection
connection = redis._single_connect()
# Store key information that we will be using to define the shape of the numpy array
# Or store a dictionary
entry_redis_key = 'training_entries'
redis.dump_dictionary(connection, {entry_redis_key: entries})
# Now you can get the matrix or dictionary by running the following
entries = redis.get_matrix(get_matrix,"example_numpy")
entries_dict = redis.get_dictionary(connection, entry_redis_key)

You should leverage the pipeline() feature if you are going to be calling more than one (non 2D numpy array) value from Redis. Pipelines are a subclass of the base Redis class that provide support for buffering multiple commands to the server in a single request. They can be used to dramatically increase the performance of groups of commands by reducing the number of back-and-forth TCP packets between the client and server. In the example above there is only 1 in the array, but you can get any number of values you want, in order of requested, given the keys.


(IN PROGRESS) But you may use MySQL for the cool connector available here

Step-By-Step Guide

1. Cloning the repository from Git

git clone
cd CS4300_Flask_template

2. Setting up your virtual environment

To install, go here or for dead-simple usage go here

# I would recommend to install virtualenv with Mac's built-in version of python because
# Anaconda is causing problems
/usr/local/bin/pip2.7 install virtualenv
# My virtual environment here will be called: venv
virtualenv venv. The python version is 2.7
# Activate the environment
source venv/bin/activate
# Pip install AppDev specific dependencies for Redis / MySQL connectors
pip install git+ --upgrade
# Install all dependencies into the virtual environment, this will be done by Heroku and AWS as well
pip install -r requirements.txt

If you wish to add any dependencies for future development just do this:

pip install <MODULE_NAME>
pip freeze > requirements.txt

3. Ensuring environment variables are present

You will now be setting up autoenv so that everytime you enter the directory all enviromental variables are set immeditaly. As such you must have autoenv installed which means that you must be inside of the virtualenv environment we created above.

# Override cd by adding this to your .?rc file (? = bash, zsh, fish, etc),
# according to your current CLI. I'll use bash in this example:
$ echo "source `which`" >> ~/.bashrc
# Reload your shell
$ source ~/.bashrc
# You should have a .env file, if not touch .env and add the
# approriate APP_SETTINGS And DATABASE_URL linking you to your local postgresDB
# After running this you should get the APP_SETTINGS by running echo
# Reactivate the environment because you just reloaded the shell
$ source venv/bin/activate

4. Setting up Postgres Backend (if interested in Postgres)

First, either install the PostgresApp if you are using a Mac here or here if you wish to install it manually on your Mac or Windows. Then run the following code after Postgres server is up:

# Enter postgres command line interface
$ psql
# Create your database which I will call my_app_db in this example, but you can change accordingly
# Quit out

5. Check to see if app runs fine by running in localhost:


At this point the app should be running on http://localhost:5000/. Navigate to that URL in your browser.

6. Push to heroku

I have included the Procile which leverages gunicorn which you can read more about here for deployment. If you get errors finding modules on Heroku this is because you have not defined a runtime.txt file that specifies python2.7. It seems that Heroku defaults to 3.6 now.

To setup heroku and push this app to there you will run the following: First you must install the heroku-cli; the installation instructions can be found here After, with your github located at the remote origin you will run the following commands to push to your heroku app.

# Login with your heroku credentials
$ heroku auth:login
Enter your Heroku credentials:
# This create logic might be deprecated so
# navigate to Heroku Dashboard and create app manually
$ heroku create <YOUR_WEBSITE_NAME>
$ git push heroku master

Before being able to interact with this application you will go to your Heroku dashboard and find your app. This will probably be here:<YOUR_WEBSITE_NAME>. On that page you will need to modify your environmental variabls (remember your .env??) by navigating to<YOUR_WEBSITE_NAME>/settings, clicking Reveal Config Vars and in left box below DATABASE_URL write: APP_SETTINGS and in the box to the right write: config.ProductionConfig. In essence you are writing export APP_SETTINGS=config.ProductionConfig in .env using Heroku's UI. You lastly will run:

heroku ps:scale web=1

You may now navigate to https://<YOUR_WEBSITE_NAME> and see your app in production. From now on, you can continue to push to Heroku and have a easy and well-managed dev flow into production.

Next steps are optional*

7. Setting up RedisML on localhost for you to interact with for pre-processing

Build using a Ansible Build or a Kubernetes Helm Chart both of which available here

Getting Started

After forking the repo make sure to name your repo cs4300sp2018-##### with your netids substituting the #####.

To being interacting with the service, modify the app/irsystem/controllers/ file where you can see the params we are passing into the rendered view.

The view is seen in app/irsystem/templates/search.html and the data is hardcoded to be range(0,5) right now, but that is what you will modify for your system.

Ensure that you swap out my dummy Project Name and NetID field for your group.

You can check out the example herokuapp: here

Deploy to EC2 Quick

Welcome to the world of automation. Get ready to be blown away :)

To get started we must install Vagrant here

With vagrant installed: check success of installation with vagrant help

Next, you will install ansible which can be done with this command: sudo pip install ansible

Now you are ready to have some fun! Follow these steps exactly to deploy and test your app:

$ git clone
$ cd CS4300_Flask_template
$ cd vagrant
$ vagrant up
$ vagrant provision
TASK [Make sure nginx is running] **********************************************
ok: [default] => {"changed": false, "name": "nginx", "state": "started"}

RUNNING HANDLER [restart nginx] ************************************************
changed: [default] => {"changed": true, "name": "nginx", "state": "started"}

PLAY RECAP *********************************************************************
default                    : ok=16   changed=12   unreachable=0    failed=0

Now navigate to and you will see the app loaded up!

Let's deploy this AWS now!

First step is to launch an EC2 instance (on the Oregon Availability Zone)

This EC2 instance should be using Ubuntu Server 14.04 LTS (HVM), SSD Volume Type - ami-7c22b41c as an AMI. This AMI will be the same type of OS that we used for our VM.

I would recommend that you choose the t2.micro, which is a small, free tier-eligible instance type.

Make your security group one with these configs:

Ports Protocol Source
80 tcp, ::/0
22 tcp, ::/0
443 tcp, ::/0

After, launching download the the key-pair and name it a4keypair.

Place the a4keypair.pem inside the vagrant folder.

Ensure, that your vagrant folder looks like this:

$ ls
Vagrantfile     a4keypair.pem   ansible.cfg     cs.nginx.j2     hosts           site.yml        upstart.conf.j2
$ chmod 700 a4keypair.pem

Your next step will be to take the public IP found when clicking on your instance in the EC2 terminal under: IPv4 Public IP and putting that into the hosts file. So the hosts file should look like this, with <YOUR_PUBLIC_IP> being replaced by that IPv4 Public IP value:

 <YOUR_PUBLIC_IP> ansible_ssh_user=ubuntu

Insure that you had the the private key by running ssh-keygen:

$ ssh-keygen
enerating public/private rsa key pair.
Enter file in which to save the key (/Users/<YOUR_USERNAME>/.ssh/id_rsa):
Enter passphrase (empty for no passphrase):
Enter same passphrase again:
Your identification has been saved in /Users/<YOUR_USERNAME>/.ssh/id_rsa.
Your public key has been saved in /Users/<YOUR_USERNAME>/.ssh/

After this you are ready to push have an automated script push to your EC2 instance, just execute this:

$ ansible -m ping webservers --private-key=a4keypair.pem --inventory=hosts --user=ubuntu
    "changed": false,
    "ping": "pong"
$ ansible-playbook -v site.yml
PLAY [Starting a Simple Flask App] ******************************************************************************************************************************************************************

TASK [Gathering Facts] ******************************************************************************************************************************************************************************
TASK [Make sure nginx is running] *******************************************************************************************************************************************************************
ok: [<YOUR_PUBLIC_IP>] => {"changed": false, "name": "nginx", "state": "started"}

PLAY RECAP ******************************************************************************************************************************************************************************************
<YOUR_PUBLIC_IP>             : ok=15   changed=2    unreachable=0    failed=0

Boom! You are done :) How easy was that!

Google Cloud (DB, Docker, Kubernetes)

Setting up Google Cloud

We are very lucky to have gotten Google Cloud Credits. Follow the directions on Piazza to setup your account.


You can setup a database REALLY easily by doing the following:

  1. Open up the Google Cloud Platform Console
  2. Go to Storage > SQL
  3. Create Instance

And everything else is self-explainitory. Upon creation it will give you an instance link that you will use for the DATABASE_URL.

Dockerizing your App

An example Dockerfile is seen below (taken from /kubernetes)

# Read from Ubuntu Base Image
FROM python:2.7
RUN mkdir -p /service
# Copy over all the files of interest
ADD app /service/app
ADD /service/
ADD /service/
ADD /service/
ADD requirements.txt /service/requirements.txt
WORKDIR /service/
RUN pip install -r requirements.txt

The contents of this are pretty self-explainitory and you can see how easy Docker is. All you have to worry about is the application files and dependencies.

So let us install Docker here to get setup.

Now let's walk through pushing this Docker image:

> pwd
> ls
Procfile         app            kubernetes       requirements.txt vagrant           config.pyc        runtime.txt      venv
> docker build -t ifilonenko/flask-template:v3 -f kubernetes/Dockerfile .

You will replace ifilonenko with your own Docker username so that you can push the image to your public Docker hub account. The image name is flask-template which you can also replace and v3 is the image tag which you change to :latest if you do not want to version.

Now that you have the docker image built we will push the image to a public repo.

> docker build -t ifilonenko/flask-template:v3 -f kubernetes/Dockerfile .
Sending build context to Docker daemon  107.7MB
Step 1/10 : FROM python:2.7
 ---> 2863c80c418c
Step 2/10 : RUN mkdir -p /service
 ---> Using cache
 ---> 43f4bea7a248
Step 3/10 : ADD app /service/app
 ---> Using cache
 ---> b5cd6d716b2c
Step 4/10 : ADD /service/
 ---> Using cache
 ---> 1c5280948d59
Step 5/10 : ADD /service/
 ---> Using cache
 ---> 7d13c7549ec8
Step 6/10 : ADD /service/
 ---> Using cache
 ---> 233cd10e0fd4
Step 7/10 : ADD requirements.txt /service/requirements.txt
 ---> Using cache
 ---> 385d0fa43691
Step 8/10 : WORKDIR /service/
 ---> Using cache
 ---> c0f4dec97809
Step 9/10 : RUN pip install -r requirements.txt
 ---> Using cache
 ---> 4065e4f7fcb6
Step 10/10 : CMD python -u $APP_SETTINGS $DATABASE_URL
 ---> Using cache
 ---> 4dc171e1c0f2
Successfully built 4dc171e1c0f2
Successfully tagged ifilonenko/flask-template:v3
> docker push ifilonenko/flask-template:v3

Now we have a publicly accessible Docker image which you can version and update by re-building and re-push as much as you want. How do we test this locally? You need to run a docker-compose script to bring the image up. An example script is provided in kubernetes/docker-compose.yml. This script is super easy:

    image: ifilonenko/flask-template:v3
      - "5000:5000"
    - APP_SETTINGS=config.DevelopmentConfig
    - DATABASE_URL=postgresql://localhost/my_app_db

And can be run with the following:

> pwd
> ls
Dockerfile         docker-compose.yml run-deployment.yml
> docker-compose up
kubernetes_flask_1 is up-to-date
Attaching to kubernetes_flask_1
flask_1  | Flask app running at

You can now navigate to see if your app is running correctly.

But we need to put this app online behind a load-balanced service so we can interact with it publicly. So now we deploy to Kubernetes

For Kubernetes you will:

  1. Open up the Google Cloud Platform Console
  2. Go to Compute > Kubernetes Engine
  3. Click Create Cluster
  4. Name the cluster and set a description.
  5. Set Zone to us-east4-a.
  6. Leave Cluster Version to be 1.8.8-gke.0 (default).
  7. Set Machine Type to small(1 shared vCPU) 1.7 GB memory
  8. Leave default Container-Optimized OS (cos)
  9. Set Size to 1
  10. Click Create

Now you will wait for the cluster to come up. When you see a green check mark next to the cluster name then you are ready to continue.

Deploying to Kubernetes:

  1. Click Connect
  2. Execute gcloud container clusters get-credentials cluster-1 --zone us-east4-a --project YOUR_PROJECT_HERE in shell. You will need to install kubectl here and gcloud here for this. This sets up your kubectl bindings to communicate with your cluster.

Now you can run the following commands:

# This sets up the cluster
> gcloud container clusters get-credentials cluster-1 --zone us-east4-a --project <YOUR_PNAME>
Fetching cluster endpoint and auth data.
kubeconfig entry generated for cluster-1.
# This checks the nodes. We should only see 1
> kubectl get nodes
NAME                                       STATUS    ROLES     AGE       VERSION
gke-cluster-1-default-pool-a7da8d2d-2g8c   Ready     <none>    3m        v1.8.8-gke.0
# Check pods running. There are none atm.
> kubectl get pods
No resources found.

Now we want to deploy a Kubernetes pod with the image. This is defined by a .yml file like the one below, taken from '/kubernetes':

apiVersion: apps/v1beta2
kind: Deployment
  name: flask
      app: flask
  replicas: 1
        app: flask
      - name: flask
        image: ifilonenko/flask-template:v3
        - name: APP_SETTINGS
          value: config.DevelopmentConfig
        - name: DATABASE_URL
          value: postgresql://localhost/my_app_db
        - containerPort: 5000

As you can see we are naming this Pod and Deployment flask and we are defining the image to be: ifilonenko/flask-template:v3. You customize this version here. You also set the environment variables here. Make sure to open up the port to 5000 so that you are able to expose that port via a Kubernetes service. We are leverage a Deployment here so that we can update the deployment in real-time to edit the version tag on the image. (This is highly useful for swapping between prototypes) while the Deployment handles the A/B swap between versions, so that there is no down-time in your app. Furthermore, Deployments allow for your system to use a replica set (in the case that you want to scale up your app) but for now we will only have 1 replicat.

# We will now launch this pod
> kubectl create -f kubernetes/run-deployment.yml
deployment "flask" created
# List pods
> kubectl get pods
NAME      READY     STATUS              RESTARTS   AGE
flask     0/1       ContainerCreating   0          18s
# Wait about a minute
> kubectl get pods
flask     1/1       Running   0          1m
> kubectl get deployments
flask     1         1         1            1           1m
# Here we see the services running. The only one that is running is the Kubernetes Master
> kubectl get svc
kubernetes   ClusterIP   <none>        443/TCP   11m
# Now we deploy the service for our pod: flask
> kubectl expose deployment flask --type=LoadBalancer
service "flask" exposed
> kubectl get svc
NAME         TYPE           CLUSTER-IP      EXTERNAL-IP   PORT(S)          AGE
flask        LoadBalancer   <pending>     5000:31380/TCP   9s
kubernetes   ClusterIP     <none>        443/TCP          11m
# Wait a minute
> kubectl get svc
NAME         TYPE           CLUSTER-IP      EXTERNAL-IP    PORT(S)          AGE
flask        LoadBalancer 5000:31380/TCP   46s
kubernetes   ClusterIP     <none>         443/TCP          12m

Now if we go to: you can see the page up.

Updating your app in real time

Now that you have your workflow setup. Let us say that you want to update your current version. After updating the files in the directory that houses your code (via git). You will push your production versions to docker with the familiar Docker command above:

# Note the v4 instead of the v3.. showing an updated version
> docker build -t ifilonenko/flask-template:v4 -f kubernetes/Dockerfile .
> docker push ifilonenko/flask-template:v4

With docker updated you now will update the deployment (only the deployment... the service is still running fine) live with the following command:

# This will take you to a vi portal which will
# allow you to edit the run-deployment.yml file.
# You simply need to update the version number on the image config and BOOM
> kubectl edit deployment flask