distribution-spec VS dive

Back in 2013, a little company called Docker made it really easy to start using containers to package up applications. A big key to their success was the OCI (you can learn about that here), an industry wide initiative to have standards around how we package up our applications. Because of OCI standards, we have hundreds (maybe thousands?) of tools that can be combined to manage and deploy applications. So why aren’t we using this for packaging up Notebooks and AI models as well? It would make deploying, sharing, and managing our models easier for everyone involved.

Kubernetes on the backend used to utilize docker for much of its container runtime solutions. One of the modular features of Kubernetes is the ability to utilize a Container Runtime Interface or CRI. The problem was that Docker didn't really meet the spec properly and they had to maintain a shim to translate properly. Instead users could utilize the popular containerd or cri-o runtimes. These follow the Open Container Initiative or OCI's guidelines on container formats.

OCI stands for Open Container Initiative, and its goal as an organization is to define a specification for container formats and runtime.

https://opencontainers.org/

Here is Containerfile from the repo: https://github.com/ublue-os/bazzite/blob/main/Containerfile

apko allows us to build OCI container images from .apk packages.

Flagger's load testing service can be installed via a Kustomization resource based on manifests packaged as an artifact in an Open Container Initiative (OCI) registry

CRI-O is a lightweight container runtime for Kubernetes. It is an implementation of Kubernetes CRI to use Open Container Initiative (OCI) compatible runtimes for running pods. It supports runc and Kata Containers as the container runtimes, but any OCI-compatible runtime can be integrated.

So somebody else created runj (runj is an experimental, proof-of-concept OCI-compatible runtime for FreeBSD jails.) https://github.com/samuelkarp/runj

Dive is an open-source tool that allows you to explore the various layers of a Docker image. It shows you the content of each layer and helps you identify voluminous or unnecessary parts.

Dive is a great tool for debugging this. I like image reduction work just because it gives me a chance to play with Dive: https://github.com/wagoodman/dive

One easy low hanging fruit I see a LOT for ballooning image sizes is people including the kitchen sink SDK/CLI for their cloud provider (like AWS or GCP), when they really only need 1/100 of that. The full versions of both of these tools are several hundred mb each

This post is going to be shorter. I'd like to highlight a tool that I really enjoy working with called "Dive" It is an essential tool when working to build and optimize docker containers.

Whether you work with Docker regularly or even create your own Docker containers, Dive is a great tool for streamlining image sizes, potentially helping you save storage costs and speed up deployments.