As outlined previously, managing interconnected services like an API, a database, or a processing worker using individual docker run commands quickly becomes cumbersome. You need to manually configure networks for them to communicate, manage the startup order, handle port mappings consistently, and repeat these steps every time you need to start or stop your application environment. This manual process is not only tedious but also prone to errors, especially as the number of services grows.

Docker Compose offers a more structured and efficient approach. It is a tool specifically built for defining and running multi-container Docker applications. Instead of issuing multiple docker run commands with complex flags, you describe your entire application stack, including all its services, networks, and volumes, in a single configuration file.

This configuration file, by convention named docker-compose.yml, uses the YAML (YAML Ain't Markup Language) format, which is designed to be human-readable. Within this file, you declaratively define what your application should look like. You specify which images to use for each service (like your ML inference API or a PostgreSQL database), how they should be networked together, which ports should be exposed, and how data should persist using volumes.

The primary advantage of using Docker Compose is simplification. With a single command, typically docker-compose up, Compose reads your docker-compose.yml file and takes care of creating (and optionally building) the necessary images, setting up networks, creating volumes, and starting all the defined containers in the correct order and with the right configuration. Similarly, docker-compose down stops and removes the containers, networks, and volumes associated with your application stack.

Diagram illustrating the shift from multiple docker run commands to a single docker-compose up command managing an application defined in docker-compose.yml.

While Docker Compose can be used in various environments, it is particularly effective for setting up local development and testing environments for multi-component ML applications. It allows developers to quickly spin up the entire stack required for their work, ensuring consistency across different machines. In the following sections, we will examine the structure of the docker-compose.yml file and learn how to define services, networks, and volumes for typical Machine Learning workflows.