Sharing Your Python Projects with Docker: Jupyter Notebooks and Datasets Included

A Tutorial on how to save and load Docker images

Photo by airfocus on Unsplash

When collaborating on data science or machine learning projects, getting someone else up and running with your code can be painful: dependency hell, version mismatches, missing datasets, and broken notebooks. Sound familiar?

Docker solves that problem by letting you package everything — your Jupyter notebooks, your Python environment, and your datasets — into a single image that “just works” anywhere.

In this tutorial, you’ll learn how to:

• Create a Docker image that includes your Jupyter notebook, dataset, and Python environment.
• Save the image to a file.
• Share it with someone else so they can run your exact environment locally.

Let’s dive in.

🛠 Scenario Setup

Imagine this: Alice is working on a machine learning project. She has a notebook train_model.ipynb, a dataset data.csv, and uses a few Python libraries. She wants to share this with Bob, who should be able to run everything without installing packages or chasing bugs.

We’ll walk through:

1. Alice creating a…