oxen-release
mandala
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oxen-release | mandala | |
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22 | 8 | |
831 | 228 | |
9.9% | - | |
9.0 | 6.3 | |
27 days ago | about 1 month ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
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
mandala
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Mandala: A little plaground for testing pixel logic patterns
I was so confused, expecting this to be some trickery related to the computational-graph-memoization-and-exploration tool "mandala" https://github.com/amakelov/mandala
- Mandala: Notebook memoization on steroids, used by Anthropic
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Improve Jupyter Notebook Reruns by Caching Cells
This is neat and self-contained! But as someone running experiments with a high degree of interactivity, I often have an orthogonal requirement: add more computations to the same cell without recomputing previous computations done in the cell (or in other cells).
For a concrete example, often in an ML project you want to study how several quantities vary across several parameters. A straightforward workflow for this is: write some nested loops, collect results in python dictionaries, finally put everything together in a dataframe and compare (by plotting or otherwise).
However, after looking at the results, maybe you spot some trend and wonder if it will continue if you tweak one of the parameters by using a new value for it; of course, you also want to look at the previous values and bring everything together in the same plot(s). You now have a problem: either re-run the cell (thus losing previous work, which is annoying even if you have to wait 1 minute - you know it's a wasted minute!), or write the new computation in a new cell, possibly with a lot of redundancy (which over time makes the notebook hard to navigate and keep consistent).
So, this and other considerations eventually convinced me that the function is more natural than the cell as an interface/boundary at which caching should be implemented, at least for my use cases (coming from ML research). I wrote a framework based on this idea, with lots of other features (some quite experimental/unusual) to turn this into a feasible experiment management tool - check it out at https://github.com/amakelov/mandala
P.S.: I notice you use `pickle` for the hashing - `joblib.dump` is faster with objects containing numpy arrays, which covers a lot of useful ML things
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ML Experiments Management with Git
Another option, that manages versioning of your computational graph and its results and provides extremely elegant query-able memoization is Mandala https://github.com/amakelov/mandala
It is a much simpler and much more magical piece of software that truly expanded how I think about writing, exploring, and experimenting with code. Even if you never use it, you probably would really enjoy reading the blog posts the author wrote about the design of the tool https://amakelov.github.io/blog/pl/
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Snakemake – A framework for reproducible data analysis
You might like mandala (https://github.com/amakelov/mandala) - it is not a build recipe tool, rather it is a tool that tracks the history of how your builds / computational graph has changed, and ties it to how the data looked like at each such step.
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Piper: A proposal for a graphy pipe-based build system
u/rust4yy: I've been building mandala, a Python framework for (among other things) incremental computing. One way to think of it is "a build system for Python objects", except the units of computation are Python functions.
What are some alternatives?
VFSForGit - Virtual File System for Git: Enable Git at Enterprise Scale
snakemake-wrappers - This is the development home of the Snakemake wrapper repository, see
gpt-2-output-dataset - Dataset of GPT-2 outputs for research in detection, biases, and more
beaver - Simple, but capable build system and command runner for any project
dvc - 🦉 ML Experiments and Data Management with Git
sdk - Metadata store for Production ML
dud - A lightweight CLI tool for versioning data alongside source code and building data pipelines.
make-booster - Utility routines to simplify using GNU make and Python
dolt - Dolt – Git for Data
aim - Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.
extremely-linear - Extremely Linear Git History // git-linearize
scidataflow - Command line scientific data management tool