AI Training - Tutorial - Deploy a simple app with Flask

How to build and use a custom Docker image containing a Flask application

Last updated 1st March, 2022.

Objective

Flask is an open-source micro framework for web development in Python.

The purpose of this tutorial is to show you how to build and use a custom Docker image for a Flask application.

Requirements

Instructions

Write a simple Flask application

Create a simple Python file with name app.py.

Inside that file, import your required modules:

from flask import Flask

Create Flask app:

app = Flask(__name__)

Define a simple function:

@app.route('/')
def index():
    return 'Web App with Python Flask using AI Training!'

Start your app:

if __name__ == '__main__':
    # starting app
    app.run(debug=True,host='0.0.0.0')
  • More information about Flask can be found here.
  • Direct link to the full python file can be found here here.

Write the requirements.txt for your applications

The requirements.txt file will allow us to write all the modules needed to make our application work. This file will be useful when writing the Dockerfile.

Flask==1.1.2

Write the Dockerfile for your application

Your Dockerfile should start with the the FROM instruction indicating the parent image to use. In our case we choose to start from a classic Python image.

FROM python

Install the requirements.txt file which contains your needed Python modules using a pip install ... command:

RUN pip install -r requirements.txt

Define your default launching command to start the application.

CMD [ "python" , "/workspace/app.py" ]

Give correct access rights to ovhcloud user (42420:42420):

RUN chown -R 42420:42420 /workspace
ENV HOME=/workspace
  • More information about Dockerfiles can be found here.
  • Direct link to the full Dockerfile can be found here here.

Build the Docker image from the Dockerfile

Launch the following command from the Dockerfile directory to build your application image:

docker build . -t flask-app:latest

The dot . argument indicates that your build context (place of the Dockerfile and other needed files) is the current directory.

The -t argument allows you to choose the identifier to give to your image. Usually image identifiers are composed of a name and a version tag <name>:<version>. For this example we chose flask-app:latest.

Test it locally (optional)

Launch the following docker command to launch your application locally on your computer:

docker run --rm -it -p 5000:5000 --user=42420:42420 flask-app:latest

The -p 5000:5000 argument indicates that you want to execute a port rediction from the port 5000 of your local machine into the port 5000 of the docker container. The port 5000 is the default port used by Flask applications.

Don't forget the --user=42420:42420 argument if you want to simulate the exact same behavior that will occur on AI TRAINING jobs. It executes the docker container as the specific OVHcloud user (user 42420:42420).

Once started, your application should be available on http://localhost:5000.

Push the image into the shared registry

The shared registry of AI Training should only be used for testing purposes. Please consider attaching your own docker registry. More information about this can be found here.

Find the address of your shared registry by launching this command:

ovhai registry list

Log in on the shared registry with your usual OpenStack credentials:

docker login -u <user> -p <password> <shared-registry-address>

Push the compiled image into the shared registry:

docker tag flask-app:latest <shared-registry-address>/flask-app:latest
docker push <shared-registry-address>/flask-app:latest

Launch the job

The following command starts a new job running your Flask application:

ovhai job run --default-http-port 5000 --cpu 1 <shared-registry-address>/flask-app:latest

--default-http-port 5000 indicates that the port to reach on the job url is the 5000.

--cpu 1 indicates that we request 4 cpu for that job.

Consider adding the --unsecure-http attribute if you want your application to be reachable without any authentication.

Once the job is running you can access your Flask application directly from the job's URL.

image

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