EdenSCM VS dvc

Kind of weird that https://github.com/facebookexperimental/eden (linked in mononoke) redirects to sapling. Does that mean it's replacing eden?

Maybe at their scale it makes more sense to switch to a VCS like Eden? https://github.com/facebookexperimental/eden

Eden's equivalent of 'git status' should run almost instantaneous, as checkouts are hosted by a virtual file system (FUSE) that tracks changes.

Meta having moved on from Mercurial may be a factually true comment, but it looks like they still have fondness for Mercurial's ways:

https://github.com/facebookexperimental/eden

> - A build system / package manager like Nix [1] but with a better user experience / more straightforward command-line tooling.

Working on it :)

> - A version control system which scales to petabytes or more. Something that I could put large video files in without thinking twice about it. Something a large company could use for their monorepo—or even their data warehouse.

https://github.com/facebookexperimental/eden

> A note-taking tool that allows me to organize notes in a graph with links between them

https://www.orgroam.com/

Eden - a cross-platform, scalable source control management system from Meta.

Eden\ (202 comments)

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.