Linear optimization with or-tools: containerizing a gunicorn web application

Previously, we left our app working with our local python+gunicorn+nginx installation. In order to get there we had to do quite a bit of configuration and if we wanted to deploy this in a server or send it to a friend, we would have to go through a very error-prone process subject to version changes and missing libraries. A potential nightmare if we contemplate switching from one operating system to another. Is there a way in which we could combine our code and configuration in a single easy to deploy multi-platform package?

Get the code here

One solution for this is to create a single Docker container that, when run, will create the environment and deploy our code in a controlled environment.

In the Docker hub you will find thousands of preconfigured containers. The best way to start is to find the closest one that would suit us and customize it. That way you avoid laying the ground work and just focus on the specifics of your application.

I tend to trust the containers built by larger vendors, organizations or open-source projects, because I find that they usually keep their containers up to date and —most importantly— they are heavily battle-tested in dev and production.

In this case, I chose a gunicorn container created by the Texas Tribune. To start, you download and install Docker, and then download your chosen container to your machine.

The way to customize a Docker container is to edit the Dockerfile. There you will specify commands to install, copy or run files specific to your project. In our case, I added an installation of python-dev, falcon and the google or-tools:

#install whats neccessary for or-tools
RUN pip install --upgrade pip
RUN pip install --upgrade wheel setuptools virtualenv
RUN apt-get -y install flex bison
RUN apt-get -y --fix-missing install autoconf libtool zlib1g-dev texinfo help2man gawk g++ curl texlive cmake subversion

#install gunicorn and falcon for providing API endpoints
RUN pip install gunicorn==19.6
RUN pip install falcon

#install or-tools
#https://github.com/google/or-tools/releases/download/v5.0/or-tools_python_examples_v5.0.3919.tar.gz
ADD or-tools_python_examples_v5.0.3919.tar.gz /app
RUN mv /app/ortools* /app/ortools && cd /app/ortools/ && python setup.py install --user

 

Then I created separate configuration files for gunicorn and nginx, and a couple of supervisor configurations. Supervisor will restart the services in case one of them goes down, which might happen if I introduce an unrecoverable error in the python script:

#copy configuration files
ADD gunicorn_conf.py /app/
ADD gunicorn.supervisor.conf /etc/supervisor/conf.d/
ADD nginx.conf /app/
ADD nginx.supervisor.conf /etc/supervisor/conf.d/


After the initial configuration, we build using the docker build command:

docker build --no-cache -t danielpradilla/or-tools .

And then, we run the container as a daemon:

docker run -p 5000:80 -d --name or-tools-gunicorn danielpradilla/or-tools

The web server port is specified as a parameter. This maps port 5000 in localhost to port 80 in the container.

Now, time to install our code. You can copy your code to the Docker container, but what I prefer is to have my code in a local folder in my machine, outside##italics of the Docker container. That way, I don’t need to copy the code to the container every time I change it, and I keep a single unmistakable copy of the code.

To do this, you mount the local folder as an extra folder inside the container. Change the Dockerfile and add

VOLUME ["/app/logs","/app/www"]

And then when, you run the container, you specify the location of your local folder:

docker run -v :/app/www -p 5000:80 -d --name or-tools-gunicorn danielpradilla/or-tools

This will allow you to experiment with multiple versions of the code (production and development) with a simple parameter change. You can run two docker containers pointing to different folders and opening different ports, and then compare the results side by side!

 

Get the code here

Linear Optimization with or-tools — building a web front-end with falcon and gunicorn

In a previous post, I put together a script for solving a linear optimisation problem using Google’s OR-tools. This python script is callable from the command line and you kinda need to know what you are doing and how to organize the parameters.

So, in order to address this difficulty, I wanted to build a web front-end for this script, so that regular humans can interact with it.

We can generalize this problem as how to build a web interface for a python script.

First of all, we can split the concerns. What we need is a web page that serves as a user interface, and a web API connecting the webpage to the python backend.

Ideally, this API should be as light as possible. After all, the heavy lifting is going to be performed by the backend. Using Django would be easy but also overkill. I was looking for one of these microframeworks, you know, like Flask. And that’s how I got to Falcon.

Falcon is like 10 times faster than Flask, it is as bare bones as you can get insomuch as you need to bring your own WSGI server, like gunicorn (but you can use Waitress in Windows, or uWSGI).

 

TL;DR

Get the code here

 

1 Installing the dependencies

pip install falcon cython gunicorn

2 Creating a JSON output for the script

My plan was to use JSON to exchange data between the API and the webpage. So I needed a JSON response builder. I could add this functionality to the previously created python script, but I prefer to have it in a separate file which basically calls the main method of the solver script and returns a JSON payload.

Source code

import json
import interview_grocery_startup as igs


def get_json_response(cfg, what):
    results = igs.main(cfg, what)

    solver = results['solver']


    if results['result_status'] == solver.OPTIMAL:
        response = {'result_status': 'optimal answer'}

        variable_list = results['variable_list']

        print solver.wall_time()
        print solver.Objective().Value()

        response['wall_time'] = solver.wall_time()
        response['objective_value']= solver.Objective().Value()

        response['variables'] = dict()

        response['variables_sum']=0
        for variable in variable_list:
            response['variables'][variable.name()]= variable.solution_value()
            response['variables_sum']+=variable.solution_value()

    elif results['result_status'] == solver.INFEASIBLE:
           response = {'result_status': 'infeasible'}
      elif results['result_status'] == solver.POSSIBLE_OVERFLOW:
        response = {'result_status': 'overflow'}

    json_response = json.dumps(response, sort_keys=True)

    return json_response

def main(cfg, what):
    json_response = get_json_response(cfg, what)
    print(json_response)
    return json_response

 

3 Coding the API

The principle for creating an API in Falcon is very easy to understand: you define a class and then instantiate it and link it to a route. This ends up being very convenient and clear. You can define the routes and the methods as you please.

Source code

import falcon
import json
import interview_grocery_startup_json as igsj 

class InterviewGroceryStartupResource(object):
    def on_get(self, req, resp):
        #Handles GET requests
        
        resp.status = falcon.HTTP_200  # This is the default status

        resp.body =  igsj.main(cfg,cfg['what'])

    def on_post(self, req, resp):
        try:
            body = req.stream.read()
            body_json = json.loads(body.decode('utf-8'))
            cfg = body_json["cfg"]
        except KeyError:
            raise falcon.HTTPBadRequest(
            'Missing Config',
            'A config (cfg) must be submitted in the request body.')

        resp.status = falcon.HTTP_200
        resp.body = igsj.main(cfg,cfg['what'])

# falcon.API instances are callable WSGI apps
app = application = falcon.API()

# Resources are represented by long-lived class instances
igsapi = InterviewGroceryStartupResource()

# ApiTestResource will handle all requests to the '/apitest' URL path
app.add_route('/igsapi', igsapi)

As you may see in the class, I added two methods. on_get is not doing much, the interesting one is on_post. On each post to the specified route, the scripts decodes de body, extracts a JSON object, looks for the property cfg (config) and sends that to the JSON response builder.

(Yes, this means that whenever you POST, you need to send a JSON object in the body with a “cfg” attribute that looks more or less like this:

{
                    "cfg": {"what": "cost",
                            "maxWeight": 10,
                            "maxCost": 100,
                            "minCals": 14000,
                            "minShop": 0.25,
                            "total_cost": 0,
                            "food":  [["ham",650, 4],
                                        ["lettuce",70,1.5],
                                        ["cheese",1670,5],
                                        ["tuna",830,20],
                                        ["bread",1300,1.20]]
                        }
}

If you are running the API in a different machine than the one serving the webpage, you may have trouble with the same origin policy. In order to address this, you can enable cross-origin resource sharing, CORS.

Add the following class to the code above

ALLOWED_ORIGINS = ['http://localhost';]

class CorsMiddleware(object):
    def process_request(self, request, response):
        origin = request.get_header('Origin')
        if origin is not None and origin in ALLOWED_ORIGINS:
            response.set_header('Access-Control-Allow-Origin', origin)
        response.set_header('Access-Control-Allow-Origin', '*')

And call this class during the API instantiation:

app = application = falcon.API(middleware=[CorsMiddleware()])

Pluggable magic!

You may run this API in port 18000 (or the one you please) by calling gunicorn:

gunicorn interview_grocery_startup_api -b :18000 --reload

Check the gunicorn docs for more options

The Falcon documentation is here.

 

4 Testing the API

I use the wonderful Postman for testing all the APIs that I make

 

5 Coding the interface

The web interface can easily be an HTML+JS+CSS thingie. I tried to keep it simple creating a single page with 3 parts: a formulation, a data table and a fat green button to get the results.

From the functional perspective, the only thing you need to do is to perform POSTs to the /gunicorn/igsapi endpoint defined in the API script, and then process the response.

Here you can see the javascript file that does everything.

I keep a variable (igs.payload) constantly updated with the JSON payload I’m going to send. And then I just POST whenever I please:

    jQuery.ajax({
      type: "POST",
      url: "/gunicorn/igsapi",
      dataType: "json",
      data: JSON.stringify(igs.payload),

      error:function (xhr, ajaxOptions, thrownError){
          console.log(thrownError);
      },

      success:function(data, textStatus, jqXHR){
        console.log(data);
        igs.fillAnswerTable(data);
      }
    })

The result is sent to a very dirty fillAnswerTable function which builds and re-builds the HTML code for the solution table.

For the UI look and feel I used Semantic UI, my current favorite Bootstrap alternative.

I’m also using Bret Victor’s Tangle library to provide direct manipulation of the variables. Each manipulation fires an event that updates the igs.payload variable.

 

Next steps

We got our little web app, but it has so many moving pieces. Wouldn’t it be nice to package it in a way that it always works? This will be the subject of a future post.

Containerizing the solution with docker