make-booster VS oxen-release

For a very different approach, check out make-booster:

https://github.com/david-a-wheeler/make-booster

Make-booster provides utility routines intended to greatly simplify data processing (particularly a data pipeline) using GNU make. It includes some mechanisms specifically to help Python, as well as general-purpose mechanisms that can be useful in any system. In particular, it helps reliably reproduce results, and it automatically determines what needs to run and runs only that (producing a significant speedup in most cases). Released as open source software.

https://github.com/david-a-wheeler/make-booster

I think a lot of hate on make is due to poor use. If your makefile is complex, refactor it. Auto-generate dependencies (it only takes a few lines in GNU make). And don't use recursive make, that way lies madness. I also think GNU make is the wiser tool; POSIX make lacks too much in many cases.

https://github.com/david-a-wheeler/make-booster

From its readme:

"This project (contained in this directory and below) provides utility routines intended to greatly simplify data processing (particularly a data pipeline) using GNU make. It includes some mechanisms specifically to help Python, as well as general-purpose mechanisms that can be useful in any system. In particular, it helps reliably reproduce results, and it automatically determines what needs to run and runs only that (producing a significant speedup in most cases)."

"For example, imagine that Python file BBB.py says include CC, and file CC.py reads from file F.txt (and CC.py declares its INPUTS= as described below). Now if you modify file F.txt or CC.py, any rule that runs BBB.py will automatically be re-run in the correct order when you use make, even if you didn't directly edit BBB.py."

This is NOT functionality directly provided by Python, and the overhead with >1000 files was 0.07seconds which we could live with :-).

We've been working on a data version control system called "oxen" optimized for large unstructured datasets that we are seeing more and more with the advent of many of the generative AI techniques.

Many of these datasets have many many images, videos, audio files, text as well as structured tabular datasets that git or git-lfs just falls flat on.

Would love anyone to kick the tires on it and let us know what you think:

https://github.com/Oxen-AI/oxen-release

The commands are mirrored after git so it is easy to learn, but optimized under the hood for larger datasets.

Super cool! Would love to see an integration with Oxen and their data version control https://github.com/Oxen-AI/oxen-release

We have been working on a data version control tool called Oxen that is tackling many of your needs. Feel free to check it out here:

https://github.com/Oxen-AI/oxen-release#-oxen

Going down your list of requirements, Oxen has:

* Data versioning, similar paradigm to git, but built from the ground up for large ML datasets

We've been working on some open source tooling called "oxen" that was built for large datasets of images, video, audio, text etc. We wanted to solve the exact problem you're flagging here with git.

Feel free to check it out here https://github.com/Oxen-AI/oxen-release#-oxen would love any feedback!