dvc VS spock

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.

I use DVC, which works decently well and can be hooked into Git.

(Note: I wrote a library called spock that was originally based on dataclasses and then shifted to attrs. In the end attrs was just the better and more fully fledged library for what I needed so I’ve always preferred attrs over dataclasses since then)

Spock-Config allows one to create OO configuration files. That's how I roll. I currently use PYdantic settings and it's great. But it does not offer command-line re-configuration of what you have in the OO config file.

Check out more in the docs or on GitHub

We wrote Spock which actually sits in the middle ground between Hydra and OmegaConf (I’m of the same opinion that Hydra does a little too much feature wise). You can do hierarchical composition within the markdown of any JSON, YAML, or TOML files by simply using the config argument. No code needed to merge. Docs are here if you’re interested.

Spock