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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

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

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

Chapter 4: Containerizing ML Training Workflows

Structuring Training Scripts for Containers

Passing Configuration and Hyperparameters

Running Training Jobs with `docker run`

Managing Training Logs

GPU Acceleration for Training

Introduction to Docker Compose for Training Stacks

Hands-on practical: Containerize and Run a Training Script

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

Chapter 6: Streamlining Workflows with Docker Compose

Introduction to Docker Compose

Defining Services in `docker-compose.yml`

Networking 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

Docker and Containerization for ML Projects

Prerequisites: Familiarity with basic Machine Learning concepts and Python programming. Prior exposure to Docker fundamentals is recommended.

Level: Intermediate

What You'll Learn

Reproducible Environments
Create consistent and shareable development and execution environments for ML projects using Dockerfiles.
Dependency Management
Manage complex Python dependencies (like TensorFlow, PyTorch, scikit-learn) within containers.
Containerizing Workflows
Package ML training scripts and inference applications into Docker containers.
Data Handling in Containers
Implement strategies for managing datasets and model artifacts with Docker volumes and bind mounts.
Optimized ML Images
Build efficient Docker images for ML applications using techniques like multi-stage builds.
GPU Acceleration
Configure Docker containers to utilize NVIDIA GPUs for accelerated ML tasks.

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