You have learned how to construct Docker images and handle data within containers. This chapter shifts the focus to applying these techniques to the machine learning training process itself. Running training scripts directly often introduces variations across environments, making results difficult to reproduce. Containerization provides a solution by packaging the training code, its dependencies, and configuration into a single, portable unit.

In this chapter, you will learn practical methods for containerizing your ML training workflows. We will examine how to structure training scripts for container execution, pass configurations such as hyperparameters, and run training jobs using docker run. Techniques for managing training logs, leveraging NVIDIA GPUs for acceleration, and using Docker Compose for basic multi-container training setups will also be covered.