--- title: "AI Deploy - Tutorial - Deploy a simple app with Flask" description: "How to build and use a custom Docker image containing a Flask application" url: https://docs.ovhcloud.com/it/guides/public-cloud/ai-machine-learning/ai-deploy-build-use-flask-image lang: it lastUpdated: 2025-06-27 --- # AI Deploy - Tutorial - Deploy a simple app with Flask :::info AI Deploy is covered by **[OVHcloud Public Cloud Special Conditions](https://storage.gra.cloud.ovh.net/v1/AUTH_325716a587c64897acbef9a4a4726e38/contracts/d2a208c-Conditions_particulieres_OVH_Stack-WE-9.0.pdf)**. ::: ## Objective [Flask](https://flask.palletsprojects.com/en/2.0.x/) 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 - An **AI Deploy project** created inside a [Public Cloud project](https://www.ovhcloud.com/it/public-cloud/) in your OVHcloud account; - A [user for AI Deploy](/it/guides/public-cloud/ai-machine-learning/ai-users.md); - [Docker](https://www.docker.com/get-started) installed on your local computer; - Some knowledge about building image and [Dockerfile](https://docs.docker.com/engine/reference/builder/). ## Instructions ### Write a simple Flask application Create a simple Python file with the name `app.py`. Inside that file, import your required modules: ```python from flask import Flask ``` Create Flask app: ```python app = Flask(__name__) ``` Define a simple function: ```python @app.route('/') def index(): return 'Web App with Python Flask using AI Deploy!' ``` Start your app: ```python if __name__ == '__main__': # starting app app.run(debug=True,host='0.0.0.0') ``` - More information about Flask can be found [here](https://flask.palletsprojects.com/en/2.0.x/). - Direct link to the full python file can be found here [here](https://github.com/ovh/ai-training-examples/tree/main/apps/flask/hello-world). ### 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`. ```console 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. ```console FROM python ``` Install the `requirements.txt` file which contains your needed Python modules using a `pip install ...` command: ```console RUN pip install -r requirements.txt ``` Define your default launching command to start the application. ```console CMD [ "python" , "/workspace/app.py" ] ``` Give correct access rights to ovhcloud user `(42420:42420)`: ```console RUN chown -R 42420:42420 /workspace ENV HOME=/workspace ``` - More information about Dockerfiles can be found [here](https://docs.docker.com/engine/reference/builder/). - Direct link to the full Dockerfile can be found here [here](https://github.com/ovh/ai-training-examples/blob/main/apps/flask/hello-world/Dockerfile). ### Build the Docker image from the Dockerfile From the directory containing your **Dockerfile**, run one of the following commands to build your application image: ```console # 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` :::info 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** `:`. 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: ```console docker run --rm -it -p 5000:5000 --user=42420:42420 flask-app:latest ``` :::info The `-p 5000:5000` argument indicates that you want to execute a port redirection 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. ::: :::warning Don't forget the `--user=42420:42420` argument if you want to simulate the exact same behavior that will occur on **AI Deploy apps**. 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 :::warning The shared registry should only be used for testing purposes. Please consider creating and attaching your own registry. More information about this can be found [here](/it/guides/public-cloud/ai-machine-learning/ai-manage-registries.md). The images pushed to this registry are for AI Tools workloads only, and will not be accessible for external uses. ::: Find the address of your shared registry by launching this command: ```console ovhai registry list ``` Log in on the shared registry with your usual AI Platform user credentials: ```console docker login -u -p ``` Push the compiled image into the shared registry: ```console docker tag flask-app:latest /flask-app:latest docker push /flask-app:latest ``` ### Launch the AI Deploy app The following command starts a new app running your Flask application: ```console ovhai app run --default-http-port 5000 --cpu 1 /flask-app:latest ``` :::info `--default-http-port 5000` indicates that the port to reach on the app url is the `5000`. `--cpu 1` indicates that we request 1 cpu for that app. Consider adding the `--unsecure-http` attribute if you want your application to be reachable without any authentication. ::: Once the app is running you can access your Flask application directly from the app's URL. ![image](/images/public-cloud/ai-machine-learning/deploy-tuto-02-flask/flask-app.png) ## Go further - To go further with **Flask**, imagine creating an app to deploy an Object Detection model. Refer to this [tutorial](/it/guides/public-cloud/ai-machine-learning/ai-deploy-web-service-yolov5.md). - **Flask** allows you to do sentiment classification on texts using Hugging Face models. [Here it is](/it/guides/public-cloud/ai-machine-learning/ai-deploy-flask-hugging-face-sentiment-analysis.md). If you need training or technical assistance to implement our solutions, contact your sales representative or click on [this link](https://www.ovhcloud.com/it/professional-services/) 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](https://discord.gg/ovhcloud)