Reproducible Environments with Docker for ML
Learners will write a Dockerfile that installs Python, pinned ML libraries, and copies a training script, then build and run the container to confirm bit-for-bit reproducibility.
The Reproducibility Problem in ML
A model trained on your laptop may produce different results on a colleague's machine because of different Python versions, library versions, or system libraries. Docker solves this by packaging your entire environment — Python, ML libraries, and your training script — into a container that runs identically on any machine. Containerised ML training is the foundation of reproducible research and reliable CI/CD pipelines.
Docker Concepts: Image, Container, Layer
A Docker image is a read-only template defined by a Dockerfile. It consists of stacked layers: each instruction in the Dockerfile adds a layer. A container is a running instance of an image. Images are portable and versioned via tags like my-ml-image:v1.2. The key insight is that the same image tag produces bitwise-identical runtime environments everywhere, from your laptop to cloud GPU instances.
# Basic Docker commands for ML workflows
# Build an image from Dockerfile in current directory
# docker build -t my-ml-trainer:v1 .
# Run a training script inside the container
# docker run --rm --gpus all my-ml-trainer:v1 python train.py
# List running containers
# docker ps
# Inspect image layers (shows what changed at each step)
# docker history my-ml-trainer:v1All lessons in this course
- Experiment Tracking with MLflow: Log Params, Metrics, and Artifacts
- Reproducible Environments with Docker for ML
- Model Registry: Staging, Production, and Archiving
- Automated Retraining Pipelines with GitHub Actions