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
Choosing the Right Base Image
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- Dockerfile reference, Docker, 2024 (Docker) - The official guide to Dockerfile instructions, including the
FROMinstruction and considerations for efficient image building. - python - Official Images, Docker Hub, 2024 - Provides details on the official Python Docker images, covering standard, slim, and Alpine variants, and their underlying libc differences (glibc vs. musl libc).
- nvidia/cuda - Official Images, NVIDIA, 2024 - Official information for NVIDIA CUDA Docker images, differentiating between
base,runtime, anddevelvariants for GPU acceleration. - Docker: Up & Running: Building Scalable and Secure Cloud Applications, Karl Matthias, Sean P. Kane, 2022 (O'Reilly Media) - A comprehensive guide to Docker, offering practical advice on Dockerfile structure, image optimization, and security aspects influencing base image choice.
- MLOps: Continuous Delivery and Automation in Machine Learning, Vishnu Lakshmanan, Vignesh Sukumaran, 2021 (O'Reilly Media) - Provides context on integrating Docker into ML workflows, highlighting how base image decisions affect reproducibility and deployment in an MLOps setting.