repo.macintoshgarden.org-fileset VS dvc

Sure, how would you like to get in touch? You have a discord, right? I actually was looking at your project and was thinking of opening a simple PR and Issue. (same username)

I have some more examples git-annex repos:

This is an annex repo I made of this popular abandonware website:

https://github.com/unqueued/repo.macintoshgarden.org-fileset

https://github.com/unqueued/ratholeradio-archive

What's cool is that people can use standard pull requests to add files to the repo. And the repo itself is small, but it can represent huge filesets. Datalad has some really fascinating medical imaging data repos that are massive (https://www.datalad.org/datasets.html).

If you wanna see a really good example of a repo with versioned binary files, check this out the git annex repo of previous git-annex binary releases:

https://downloads.kitenet.net/.git/

You can just use standard git workflows to see previous revisions of a file (well, previous hashes) but it is really easy to hook into.

I actually have thought about making a special remote specifically to diff images. But yeah, git-annex does a really amazing job at bridging the bag between binary and git.

Collaboration and version control are crucial in AI/ML development projects due to the iterative nature of model development and the need for reproducibility. GitHub is the leading platform for source code management, allowing teams to collaborate on code, track issues, and manage project milestones. DVC (Data Version Control) complements Git by handling large data files, data sets, and machine learning models that Git can't manage effectively, enabling version control for the data and model files used in AI projects.

What you’re describing sounds like DVC (at a higher-ish—80%-solution level).

https://dvc.org/

See pachyderm too.

10. DVC by Iterative | Github | tutorial

Platforms such as MLflow monitor the development stages of machine learning models. In parallel, Data Version Control (DVC) brings version control system-like functions to the realm of data sets and models.

I was using DVC (https://dvc.org/) for some time to help solve this but it was getting hard to manage the storage connections and I would run into cache issues a lot, but this solves it using git-lfs itself.

DVC (Data Version Control):

I have been using DVC and MLflow since then DVC had only data tracking and MLflow only model tracking. I can say both are awesome now and maybe the only factor I would like to mention is that IMO, MLflow is a bit harder to learn while DVC is just a git practically.