ParrelSync VS dvc

Look into how ParalellSync sets it up. It's an extremely popular package that use symlinks to do multiplayer testing between multiple cloned Unity projects.

https://github.com/psf/black/issues/338 https://github.com/VeriorPies/ParrelSync/issues/61 https://github.com/prusa3d/PrusaSlicer/issues/5751 https://github.com/iterative/dvc/issues/2530 https://github.com/facebook/relay/issues/3647 And I know godmode9 at one point absolutely freaked when navigating into a symlink. It kinda depends on the app and what it's trying to load

ParrelSync makes testing multiplayer unity games locally much, much easier https://github.com/VeriorPies/ParrelSync

Update: I found a way to do it here: https://github.com/VeriorPies/ParrelSync/issues/77

Also, Mirror development in itself is a bit complicated in the beginning, so lots of testing is required. I installed the Parrel Sync asset (which allows to make clone projects instantly updated from the root project), but I cannot find a good way of using it for VR.

Very nice, I would definitely invest in visual tools for network, instead of just console logs.. For testing, I have always used Parallel Sync "https://github.com/VeriorPies/ParrelSync" but it needs 2 unity instances opened, and needs to compile scripts for the main + cloned project. Would this split screen work regardless of fake or real networking? When is this going to be on the asset store ^^

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.