So far, we have focused on building and running individual Docker containers for specific tasks like training or inference. However, many Machine Learning development and deployment scenarios involve multiple interacting components. For instance, you might need an inference API container, a database container to store metadata, and perhaps a message queue container for asynchronous tasks. Managing the setup, networking, and lifecycle of these individual containers using separate docker run commands can become inefficient and error prone.

This chapter introduces Docker Compose, a tool designed specifically to simplify the definition and management of multi container Docker applications. You will learn to use a single configuration file, typically named docker-compose.yml, to define all the services that make up your application stack. We will cover defining services, establishing networks for inter container communication, managing persistent data with volumes, configuring services using environment variables, and building container images directly within the Compose workflow. By the end of this chapter, you will be able to use Docker Compose to set up and run complex ML development environments locally.