5 Docker Best Practices for Faster Builds and Smaller Images

2026-04-22 · Source: KDnuggets · Field: Technology & Digital — Software Development & Engineering, Cloud Computing & IT Infrastructure · Depth: Intermediate, medium

Summary

This article outlines five Docker best practices to significantly reduce image size and accelerate build times, addressing common inefficiencies like large base images and redundant rebuilds. It details how selecting `slim` or `alpine` base images, such as `python:3.11-slim`, can cut image size by hundreds of megabytes. The content also explains optimizing Docker layer caching by ordering `COPY` and `RUN` instructions from least to most frequently changed, ensuring dependencies are cached. Multi-stage builds are introduced as a method to exclude build-time tools like compilers from the final image, while same-layer cleanup for package managers like `apt-get` prevents temporary files from bloating intermediate layers. Finally, the importance of `.dockerignore` files is highlighted for reducing build context size and preventing sensitive data from being included in images.

Key takeaway

For MLOps Engineers or Software Engineers building containerized applications, adopting these Docker best practices is crucial. You should prioritize `slim` base images, strategically order Dockerfile layers to maximize cache hits, and implement multi-stage builds to minimize final image size. Consistently using `.dockerignore` and same-layer cleanup will further reduce build times and enhance security by preventing unwanted files and secrets from entering your images.

Key insights

Optimizing Dockerfiles with specific practices yields significantly smaller images and faster builds.

Principles

• Start with minimal base images.
• Cache stable layers first.
• Exclude build-only dependencies.

Method

Employ `slim` or `alpine` base images, order Dockerfile layers by change frequency, use multi-stage builds, perform cleanup in the same `RUN` instruction, and implement `.dockerignore` files.

In practice

• Use `python:3.1x-slim` as a default base image.
• Copy `requirements.txt` before application code.
• Add `rm -rf /var/lib/apt/lists/*` to `apt-get install` commands.

Topics

• Docker Best Practices
• Docker Image Optimization
• Dockerfile Layer Caching
• Multi-Stage Builds
• Base Images

Best for: Software Engineer, DevOps Engineer, MLOps Engineer

Related on AIssential

• How to Make Docker Builds Smaller and Faster — Optimizing Docker builds reduces image size and accelerates development workflows.
• Designing for “Future Me”: Docker Compose and AWS — A well-structured Docker Compose setup ensures development reproducibility and rapid project resumption.
• Reducing container cold start times using SOCI index on DLAMI and DLC — SOCI snapshotter dramatically cuts container cold start and image pull times for large AI/ML images on AWS DLAMI/DLC.
• How to Dockerize Your Application and Deploy It to Sevalla — Docker ensures application consistency across environments by packaging all dependencies into a portable image.
• 5 Docker Containers for Small Business — Containerization with Docker provides small businesses an affordable, integrated data infrastructure solution.
• Pinterest Engineers Eliminate CPU Zombies to Resolve Production Bottlenecks — Unused default agents can leak kernel state, causing hidden CPU bottlenecks and system instability.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by KDnuggets.