Multi-stage builds for smaller images
📖 Scenario: You are working on a machine learning project that requires a Docker image to run your model training script. You want to keep the final Docker image small to save storage and speed up deployment.Multi-stage builds in Docker help you do this by letting you use one image to build your code and another smaller image to run it.
🎯 Goal: Build a Dockerfile using multi-stage builds that first installs all dependencies and copies your training script, then creates a smaller final image that only contains the necessary runtime files.
📋 What You'll Learn
Create a first build stage named
builder using the python:3.12-slim imageIn the
builder stage, copy train.py and install scikit-learnCreate a second stage named
runtime using the python:3.12-alpine imageCopy only the
train.py file from the builder stage to the runtime stageSet the default command to run
python train.py in the runtime stage💡 Why This Matters
🌍 Real World
Multi-stage builds are used in real projects to reduce Docker image size, which saves bandwidth and speeds up deployment.
💼 Career
Understanding multi-stage builds is important for DevOps and MLOps roles to optimize containerized machine learning workflows.
Progress0 / 4 steps