AI Deploy - Tutorial - Deploy a simple app with Flask

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

• Access to the OVHcloud Control Panel;
• An AI Deploy project created inside a Public Cloud project in your OVHcloud account;
• A user for AI Deploy;
• Docker installed on your local computer;
• Some knowledge about building image and Dockerfile.

Instructions

Write a simple Flask application

Create a simple Python file with the 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 Deploy!'

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 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

From the directory containing your Dockerfile, run one of the following commands to build your application image:

# Build the image using your machine's default architecture
docker build . -t flask-app:latest

# Build image targeting the linux/amd64 architecture
docker buildx build --platform linux/amd64 -t flask-app:latest .

• The first command builds the image using your system’s default architecture. This may work if your machine already uses the linux/amd64 architecture, which is required to run containers with our AI products. However, on systems with a different architecture (e.g. ARM64 on Apple Silicon), the resulting image will not be compatible and cannot be deployed.

• The second command explicitly targets the linux/AMD64 architecture to ensure compatibility with our AI services. This requires buildx, which is not installed by default. If you haven’t used buildx before, you can install it by running: docker buildx install

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

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

Push the image into the shared registry

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

ovhai registry list

Log in on the shared registry with your usual AI Platform user 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 AI Deploy app

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

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

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

Go further

• To go further with Flask, imagine creating an app to deploy an Object Detection model. Refer to this tutorial.
• Flask allows you to do sentiment classification on texts using Hugging Face models. Here it is.

If you need training or technical assistance to implement our solutions, contact your sales representative or click on this link to get a quote and ask our Professional Services experts for a custom analysis of your project.

Feedback

Please send us your questions, feedback and suggestions to improve the service:

• On the OVHcloud Discord server