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Cover image for Understanding Docker: part 4 – Docker images
Aurélie Vache
Aurélie Vache

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Understanding Docker: part 4 – Docker images

Understanding Docker can be difficult or time-consuming. In order to spread knowledges about Cloud technologies I started to create sketchnotes about Docker. I think it could be a good way, more visual, to explain Docker (and others technologies like Kubernetes and Istio).

We start the serie of Docker Sketchnotes with a focus about docker images.

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If you are interested, I published all the sketchnotes on Docker (and new ones!) to make a "book" of 93 pages: "Understanding Docker in a visual way".

As usual, if you like theses sketchnotes, you can follow me, and tell me what do you think. I will publish others sketchs shortly :-).

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🚀 pgai Vectorizer: SQLAlchemy and LiteLLM Make Vector Search Simple

We built pgai Vectorizer to simplify embedding management for AI applications—without needing a separate database or complex infrastructure. Since launch, developers have created over 3,000 vectorizers on Timescale Cloud, with many more self-hosted.

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Top comments (4)

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pavithraes profile image
Pavithra Eswaramoorthy

I love your sketchnotes!

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aurelievache profile image
Aurélie Vache

Thanks Pavithra! 😊

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bluefire153 profile image
Tien Do • Edited

In order to remove docker image, you need to stop all containers which related to that image first, don't you?

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aurelievache profile image
Aurélie Vache

Yes if you try to remove an image and a running container exists, rmi command will not be a success.

BUT you can force a remove image command :-) :

$ docker rmi ubuntu:latest
Error response from daemon: conflict: unable to remove repository reference "ubuntu:latest" (must force) - container 988cb10f88b8 is using its referenced image f643c72bc252

$ docker rmi ubuntu:latest --force
Untagged: ubuntu:latest
Untagged: ubuntu@sha256:c95a8e48bf88e9849f3e0f723d9f49fa12c5a00cfc6e60d2bc99d87555295e4c

Image of Timescale

🚀 pgai Vectorizer: SQLAlchemy and LiteLLM Make Vector Search Simple

We built pgai Vectorizer to simplify embedding management for AI applications—without needing a separate database or complex infrastructure. Since launch, developers have created over 3,000 vectorizers on Timescale Cloud, with many more self-hosted.

Read more