dupver VS dvc

I work with a lot of uncompressed structured binary files so I finally broke down and wrote my own system based on the Restic chunker: https://github.com/akbarnes/dupver

I wound up writing dupver https://github.com/akbarnes/dupver after getting frustrated with the lack of versioning tools for binary files. One neat thing about .docx files and their ilk is that they are "just" zip files so it isn't hard to add special handling to pull out their contents and run deduplication over that.

DupVer https://github.com/akbarnes/dupver is a deduplicating version control system for large binary files. It's designed to keep state in a repository on the local machine separate from the working directory so it plays nice with cloud synchronization software.

I started it after constant headaches involving Git LFS and the corporate proxy. It's based around the Restic chunker library, with inspiration from both the Duplicacy backup software and Boar, another binary version control system for large binary files.

https://github.com/akbarnes/dupver

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.