I built software to cut docker image sizes by more than 80%. It uses dynamic analysis and and has a LOT more room for improvement. The demo here shows an ETL workflow docker image, cut down from 1.28GB to 193MB (-85%)! If large docker image sizes are becoming a... production problem, I'd be happy to explore how we can help.show more

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This tool could come in handy for exploring layers of docker that can be shrinked:

dive is awesome!

this is a common issue we face when running production containers on GPUs, we don’t even have a model in the image just a lot of dependencies. cool stuff would love to check it out

thats the biggest problem with ML dependencies, they are extensible and bloated + dependencies creates a hoard of unused package. static analysis can only get you so far. would love to chat more if this is a pressing problem. Here is a quick explainer -

The idea of watching for which parts of the FS are actually touched during execution reminds me of @playablequotes /

Interesting, thanks for sharing. I usually settle with distroless

distroless is the next best thing, my tool is more aggressive than distroless

Simply reducing the size of the Docker image and confirming that it “still works fine” is not sufficient to guarantee that the image is free of defects. Could you provide more details on the additional testing methods you have used to validate its functionality and stability?

good question, the ETL tool minimal image did precisely the same transformations as the larger image. For images with broader scope, tests on the application are needed to validate whether functionality is working. luckily, most orgs have tests like this. did you have any other ideas?