How to choose the right Python base image for containerizing your ML application

One often-overlooked aspect of deploying Machine Learning solutions with Docker is selecting the right base image. Discovering vulnerabilities in base images post-deployment in security-conscious organizations may result in building the ML models again. This can introduce additional time investment due to potential changes in library dependencies.

So, here are the two key factors I would consider in choosing the python image (also attached an image that illustrates the same):

1. Security:

   Minimize attack surface: Start with the most lightweight base image that fulfills your requirements. Images like "python: alpine" and "python: slim" offer a solid foundation without unnecessary bloatware. Remember, more libraries increase the potential attack surface. Popular "slim" variants include "bullseye" and "bookworm." You can always install missing libraries using apt or apk within the container. I often favor 'slim' variants over Alpine Linux. While Alpine offers a smaller footprint, 'slim' typically includes some pre-installed, commonly used libraries, reducing the need for additional installation steps.

   Version Control: Use explicit version tags instead of the generic "latest" tag. The "latest" tag can point to different images over time, causing inconsistencies and potentially introducing vulnerabilities. Choose a base image with a known version that has been scanned for security issues. Consider official base images from trusted sources like Docker Hub.

   And you might ask how to find vulnerabilities in your already deployed solution. Usually, enterprises use plugins like PRISMA to help identify vulnerabilities. If your enterprise doesn't offer such tools, here is a Docker command you can run to find the vulnerabilities.

    docker scout <docker-image>
   

2. Size Optimization:

   Minimize footprint: Smaller base images lead to smaller container sizes, which translates to faster deployment and lower resource consumption. This is particularly important for large-scale deployments.

   Let's see how to keep these factors in mind when choosing a base image from Docker Hub. The image below shows the vulnerabilities and sizes of the popular base images available on Docker Hub. Using the 'latest' tag can be risky in production because you don't know what might go wrong as the vulnerabilities are yet to be scanned. 'Slim' variants usually have a reasonable size and fewer vulnerabilities. For example, the 'Slim' variant has 0 critical and 2 high vulnerabilities. In some cases, you can remove some of these vulnerabilities.

   

   By focusing on minimizing the attack surface and optimizing the size of your base image, you can build a more secure and performant solution. Always use explicit version tags and rely on trusted sources for your base images. Regularly scan for vulnerabilities and stay informed about updates to maintain a robust deployment. With these considerations in mind, you can confidently deploy your ML applications with Docker, knowing they are secure and optimized.