--- title: "AI Deploy - Tutoriel - Construire & utiliser une image Streamlit (EN)" description: "Comment construire et utiliser votre propre image Docker contenant une application Streamlit" url: https://docs.ovhcloud.com/fr/guides/public-cloud/ai-machine-learning/ai-deploy-build-use-streamlit-image lang: fr lastUpdated: 2025-06-27 --- # AI Deploy - Tutoriel - Construire & utiliser une image Streamlit (EN) :::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 [Streamlit](https://streamlit.io/) is a python framework that turns scripts into shareable web application. The purpose of this tutorial is to provide a concrete example on how to build and - On the use a custom Docker image for a Streamlit applications. ## Requirements - an **AI Deploy project** created inside a **Public Cloud** project - a [user for AI Deploy](/fr/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 Streamlit application Create a simple python file with name `simple_app.py`. Inside that file, import your required modules: ```python import streamlit as st import pandas as pd ``` Display all information you want on your Streamlit application: ```python st.title('My first app') st.write("Here's our first attempt at using data to create a table:") st.write(pd.DataFrame({ 'first column': [1, 2, 3, 4], 'second column': [10, 20, 30, 40] })) ``` - More information about Streamlit capabilities can be found [here](https://docs.streamlit.io/en/stable/) - Direct link to the full python file can be found here [here](https://github.com/ovh/ai-training-examples/tree/main/apps/streamlit/simple-app/simple_app.py) ### 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:3.8 ``` Install your needed python module using a `pip install ...` command. In our case we only need these 2 modules: - streamlit - pandas ```console RUN pip install streamlit pandas ``` Install your application inside your image. In our case, we just copy our python file inside the `/opt` directory. ```console COPY simple_app.py /opt/simple_app.py ``` Define your default launching command to start the application: ```console CMD [ "streamlit" , "run" , "/opt/simple_app.py", "--server.address=0.0.0.0" ] ``` :::warning In order to access the app from the outside world, don't forget to add the `--server.address=0.0.0.0` instruction on your `streamlit run ...` command. By doing this you indicate to the process that it have to bind on all network interfaces and not only the `localhost`. ::: Create the home directory of the **ovhcloud user** (`42420:42420`) and give it correct access rights: ```console RUN mkdir /workspace && chown -R 42420:42420 /workspace ENV HOME /workspace WORKDIR /workspace ``` :::info This last step is mandatory because Streamlit needs to be able to write inside the `HOME` directory of the owner of the process in order to work properly. ::: - 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/tree/main/apps/streamlit/simple-app/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 streamlit-example:latest # Build image targeting the linux/amd64 architecture docker buildx build --platform linux/amd64 -t streamlit-example: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 **streamlit-example:latest**. ::: ### Test it locally (optional) Launch the following **docker command** to launch your application locally on your computer: ```console docker run --rm -it -p 8501:8501 --user=42420:42420 streamlit-example:latest ``` :::info The `-p 8501:8501` argument indicates that you want to execute a port rediction from the port **8501** of your local machine into the port **8501** of the docker container. The port **8501** is the default port used by **streamlit** 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:8501`. ### 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](/fr/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 ``` Login 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 streamlit-example:latest ``/streamlit-example:latest docker push ``/streamlit-example:latest ``` ### Launch the AI Deploy app The following command starts a new app running your Streamlit application: ```console ovhai app run --default-http-port 8501 --cpu 1 ``/streamlit-example:latest ``` :::info `--default-http-port 8501` indicates that the port to reach on the app URL is the `8501`. ::: :::info `--cpu 1` indicates that we only request 1 CPU for that app. ::: :::info Consider adding the `--unsecure-http` attribute if you want your application to be reachable without any authentication. ::: Once the AI Deploy app is running you can access your Streamlit application directly from the app's URL. ![image](/images/public-cloud/ai-machine-learning/deploy-tuto-01-streamlit/streamlit.png) ## Go further - Do you want to use **Streamlit** to deploy an AI model for audio classification task? [Here it is](/fr/guides/public-cloud/ai-machine-learning/ai-deploy-streamlit-sounds-classification.md). - You can imagine deploying an AI model with an other tool: **Flask**. Refer to this [tutorial](/fr/guides/public-cloud/ai-machine-learning/ai-deploy-build-use-flask-image.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/fr/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)