dud
oxen-release
| dud | oxen-release | |
|---|---|---|
| 14 | 22 | |
| 166 | 831 | |
| - | 1.6% | |
| 6.0 | 9.0 | |
| 6 days ago | 30 days ago | |
| Go | Python | |
| BSD 3-clause "New" or "Revised" License | Apache License 2.0 |
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dud
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Ask HN: How do your ML teams version datasets and models?
I've used DVC in the past and generally liked its approach. That said, I wholeheartedly agree that it's clunky. It does a lot of things implicitly, which can make it hard to reason about. It was also extremely slow for medium-sized dataset (low 10s of GBs).
In response, I created a command-line tool that addresses these issues[0]. To reduce the comparison to an analogy: Dud : DVC :: Flask : Django.
[0]: https://github.com/kevin-hanselman/dud
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🐂 🌾 Oxen.ai - Blazing Fast Unstructured Data Version Control, built in Rust
There is also https://github.com/kevin-hanselman/dud
- Data Version Control
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Tup – an instrumenting file-based build system
I very much agree with you about DVC's feature creep. The other issue I have with it is speed. DVC has left me scratching my head at its sluggishness many times. Because of these factors, I've been working on an alternative that focuses on simplicity and speed[0]. My tool is often five to ten times faster than DVC[1]. I'd love to hear what you think.
[0]: https://github.com/kevin-hanselman/dud
[1]: https://kevin-hanselman.github.io/dud/benchmarks/
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Non-Obvious Docker Uses
I don't know about replacing Make with Docker, but I use the two together to good effect. One of my favorite hacks is adding a 'docker-%' rule in my Makefile to run make commands in a Docker image[1]. It's a bit mind-bending, and there's a few gotchas, but it works surprisingly well for simple rules.
[1]: https://github.com/kevin-hanselman/dud/blob/e98de8fcdf7ad564...
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Git-annex – Managing large files with Git
Thanks for sharing your experience. It's non-trivial and surprising behavior like this that drove me to build a custom system[0] myself. When I started researching version control tools for large files, I remember feeling like git-annex and Git LFS were awkwardly bolted onto Git; Git simply wasn't designed for large files. Then I found DVC[1], and its approach rang true for me. However, after using DVC for a year or so, I grew tired of DVC's many puzzling behaviors (most of which are outlined in the README at [0]). In the end, I built the tool I wanted for the job -- one that is exceptionally simple and fast.
[0]: https://github.com/kevin-hanselman/dud
- Alternative to Git LFS or DVC
- Show HN: A small and simple alternative to Git LFS or DVC
- Dud: a lightweight tool for versioning data alongside source code and building data pipelines.
- Dud: a tool for versioning data alongside source code. A faster and simpler alternative to DVC.
oxen-release
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Ask HN: Can we do better than Git for version control?
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.
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Snakemake – A framework for reproducible data analysis
Super cool! Would love to see an integration with Oxen and their data version control https://github.com/Oxen-AI/oxen-release
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Ask HN: Data Management for AI Training
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
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A tale of Phobos – how we almost cracked a ransomware using CUDA
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!
- Oxen.ai: Fast Unstructured Data Version Control
- A versioning system for ML data sets
- Oxen - Version control for your machine learning datasets
What are some alternatives?
dvc - 🦉 ML Experiments and Data Management with Git
VFSForGit - Virtual File System for Git: Enable Git at Enterprise Scale
scalar - Scalar: A set of tools and extensions for Git to allow very large monorepos to run on Git without a virtualization layer
gpt-2-output-dataset - Dataset of GPT-2 outputs for research in detection, biases, and more
docker-merge - Docker images as git repositories, so you can merge them.
Task - A task runner / simpler Make alternative written in Go
mandala - A powerful and easy to use Python framework for experiment tracking and incremental computing
pachyderm - Data-Centric Pipelines and Data Versioning
dolt - Dolt – Git for Data
Git - Git Source Code Mirror - This is a publish-only repository but pull requests can be turned into patches to the mailing list via GitGitGadget (https://gitgitgadget.github.io/). Please follow Documentation/SubmittingPatches procedure for any of your improvements.
extremely-linear - Extremely Linear Git History // git-linearize