Docker and Containerization for ML Projects
Chapter 1: Docker Concepts for Machine Learning
Why Containerize ML Projects?
Docker Image Fundamentals
Container Lifecycle Management
Introduction to Dockerfiles
Docker Registries and Repositories
Hands-on practical: Running Pre-built ML Images
Quiz for Chapter 1
Chapter 2: Building Custom ML Environments with Dockerfiles
Structuring your Dockerfile
Choosing the Right Base Image
Managing Python Dependencies (pip)
Managing Python Dependencies (Conda)
Working with Environment Variables
Copying Code and Artifacts
Setting the Working Directory and Entrypoint
Hands-on practical: Build a Scikit-learn Environment
Quiz for Chapter 2
Chapter 3: Managing Data and Models in Containers
Understanding Container Storage
Using Bind Mounts for Development
Using Docker Volumes for Persistence
Comparing Bind Mounts and Volumes
Accessing Cloud Storage from Containers
Packaging Models Inside Images vs. Volumes
Hands-on practical: Mounting Datasets and Saving Models
Quiz for Chapter 3
Chapter 4: Containerizing ML Training Workflows
Structuring Training Scripts for Containers
Passing Configuration and Hyperparameters
Running Training Jobs with
docker runManaging Training Logs
GPU Acceleration for Training
Introduction to Docker Compose for Training Stacks
Hands-on practical: Containerize and Run a Training Script
Quiz for Chapter 4
Chapter 5: Containerizing ML Models for Inference
Designing Inference Services
Building Inference APIs (Flask/FastAPI)
Optimizing Image Size: Multi-Stage Builds
Reducing Dependencies for Inference
Exposing Ports for API Access
Health Checks for Inference Containers
Hands-on practical: Containerize a Simple Inference API
Quiz for Chapter 5
Chapter 6: Streamlining Workflows with Docker Compose
Introduction to Docker Compose
Defining Services in
docker-compose.ymlNetworking Between Containers
Using Volumes with Compose
Environment Variables in Compose
Building Images with Compose
Common ML Stack Examples (e.g., API + DB)
Hands-on practical: Develop an ML App with Compose
Quiz for Chapter 6
Running Training Jobs with docker run
Was this section helpful?
- docker run, Docker Documentation, 2024 (Docker) - Covers the fundamental usage of the
docker runcommand, including command-line arguments, environment variables, resource allocation, and container lifecycle management, directly relevant to executing ML training jobs. - Use volumes, Docker Documentation, 2024 (Docker Inc.) - Details the principles and practical application of Docker volumes and bind mounts, important for managing data persistence and sharing between the host and containers during ML training.
- Docker Deep Dive, Nigel Poulton, 2023 (Nigel Poulton Ltd.) - A comprehensive guide to Docker, explaining containerization concepts and practical operations. This book supports understanding the technical foundation for running ML workflows in Docker containers.
- Building Machine Learning Powered Applications: Going from Idea to Product, Emmanuel Ameisen, 2020 (O'Reilly Media) - Presents practical guidance on the end-to-end process of developing and deploying machine learning applications, including methods for containerizing ML models and training environments to achieve reproducibility and efficient deployment.