Hey everyone! I'm looking for some guidance on managing large Python virtual environments in Kubernetes. I've been trying to containerize them, but I'm running into issues with image sizes—one of my images with machine learning libraries ballooned to 10GB! Even with multi-stage builds and cleanups, this just isn't sustainable for my workflow. Is it a good idea to install these environments on shared storage like NFS and then mount that volume in the pod? What strategies do others use for this?
5 Answers
Thanks for the feedback, everyone! I'm definitely leaning towards using NFS to simplify things and keep my images manageable.
One option is using NFS to mount a pre-built environment into your pods. This way, you speed up the pod startup and avoid having Kubernetes pull the entire 12GB of libraries every time you update your code.
You could also build your dependencies into a separate OCI image and then mount that as a volume if you're using Kubernetes version 1.32 or later. It's worth exploring what fits best for your use case.
If your libraries are that hefty, consider building the common dependencies into a base image. This way, your applications can reuse layers across different nodes, and it might help you manage your image sizes better.
And just to add, image volumes have reached beta status in Kubernetes 1.33, so you might want to look into that as a solution!
Related Questions
Set Wordpress Featured Image Using Javascript
How To Fix PHP Random Being The Same
Why no WebP Support with Wordpress
Replace Wordpress Cron With Linux Cron
Customize Yoast Canonical URL Programmatically
[Centos] Delete All Files And Folders That Contain a String