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The Django-Scikit-Learn image provides a robust, ready-to-use foundation for developers building data-driven web applications. Built on the secure and stable Ubuntu 24.04 LTS “Noble Numbat,” this stack eliminates the overhead of manual environment configuration. This image is ideal for data scientists and developers looking to quickly transition from model development to web-based service delivery, or anyone needing a stable starting point for Python-based machine learning projects.

Software Included

This Marketplace listing does not include a detailed software list.

Creating an App using the Control Panel

Click the Deploy to DigitalOcean button to create a Droplet based on this 1-Click App. If you aren’t logged in, this link will prompt you to log in with your DigitalOcean account.

Creating an App using the API

In addition to creating a Droplet from the Django-Scikitlearn on Ubuntu24-04 1-Click App using the control panel, you can also use the DigitalOcean API. As an example, to create a 4GB Django-Scikitlearn on Ubuntu24-04 Droplet in the SFO2 region, you can use the following curl command. You need to either save your API access token to an environment variable or substitute it in the command below.

curl -X POST -H 'Content-Type: application/json' \
         -H 'Authorization: Bearer '$TOKEN'' -d \
        '{"name":"choose_a_name","region":"sfo2","size":"s-2vcpu-4gb","image":"apps4rent-djangoscikitlear"}' \
        "https://api.digitalocean.com/v2/droplets"

Getting Started After Deploying Django-Scikitlearn on Ubuntu24-04

Once your Droplet is deployed, follow these steps to verify your installation and begin your development:

1. Connect to your Droplet

   Access your server via SSH using the terminal:

ssh root@your_droplet_public_ipv4

2. Activate the virtual environment

   The application uses a Python virtual environment to manage dependencies. Activate it by running:

source venv/bin/activate

3. To check the installed version of Django:

pip show django

4. To check the installed version of Scikit-learn

pip show scikit-learn

5. To come out of the virtual environment:

deactivate