🚀 Docker Image Optimization: 1.07GB → 58MB A small change in your Dockerfile can make a massive difference in image size and performance. Instead of building everything in one large image, use Multi-Stage Builds. 🔴 The Problem (Bloated Image – 1.07GB) Using a full Ubuntu image and installing everything in a single stage adds unnecessary packages and build tools to the final container. 🟢 The Solution (Optimized Image – 58MB) Use a builder stage to install dependencies and compile packages, then copy only the required artifacts into a lightweight runtime image. Benefits: ✅ Smaller image size ✅ Faster builds & deployments ✅ Reduced attack surface ✅ Lower storage and bandwidth usage 💡 Key Idea: Build heavy dependencies in the builder stage, ship only what you need in the final stage. This simple pattern is one of the easiest ways to improve your containerized applications. What’s the biggest Docker image size you’ve optimized so far? 👇 #Docker #DevOps #CloudNative #SoftwareEngineering #BackendDevelopment #Python #Containers
Manish Gupta Totally agree. I ran into the same thing while working on my NLP project. Initially the Docker image was exploding in size because of heavy ML/NLP dependencies. Using multi-stage builds helped a lot — I kept the compilers, build tools, and dependency installation in the builder stage and only copied the required runtime artifacts to the final stage. I also optimized a lot of the Dockerfile commands by chaining them into single "RUN" layers and cleaning the build cache ("apt", pip cache, etc.) during the build itself. That made a big difference. The result was a huge reduction — the image went from roughly 20–30GB down to about ~4GB, and the CI pipeline became much faster to build and push.
If you want to try this on a real Linux server, I have just released this scenario: https://www.learnbyfixing.com/scenarios/17/
Love this! Many people think this is not important until they have 1000 units to update OTA on the field with terrible connectivity. Keeping Docker images as low weight as possible is incredibly necessary. Also using the right base image is very important going from Ubuntu to Python Slim in your example is also playing a bit role on the final image size.
Yeah, sometimes when we try to optimize certain utilities aren't supported with specific base images, any suggestions how to handle such situations?
Thanks very useful , if we using multistage docker instead of simple dockerfile the image size will reduced from gib -> mib
Ami toh jantam e naa. Lol
Spot on about the bloat! To add to the reusability side of things, I've found that using an ARG for the Python version(instead of hard coding) helps keep the Dockerfile 'evergreen' and easier to maintain across different projects. Great stuff!
Best way is using distroless images. It's more secure and even last space.
Try using python:3.11-alpine for runtime which might get you even tinier image and slim for builder. Alpine images are tiny (~5–20 MB base vs ~40–60 MB for slim).