dvc
dud
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dvc | dud | |
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109 | 14 | |
13,116 | 166 | |
1.4% | - | |
9.7 | 6.0 | |
5 days ago | 5 days ago | |
Python | Go | |
Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
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dvc
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My Favorite DevTools to Build AI/ML Applications!
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.
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Why bad scientific code beats code following "best practices"
What youโre describing sounds like DVC (at a higher-ishโ80%-solution level).
https://dvc.org/
See pachyderm too.
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First 15 Open Source Advent projects
10. DVC by Iterative | Github | tutorial
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
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.
- ML Experiments Management with Git
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Git Version Controlled Datasets in S3
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.
- Ask HN: How do your ML teams version datasets and models?
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Exploring MLOps Tools and Frameworks: Enhancing Machine Learning Operations
DVC (Data Version Control):
- Evaluate and Track Your LLM Experiments: Introducing TruLens for LLMs
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[D] Is there a tool to keep track of my ML experiments?
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.
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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๐ ๐พ 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 โ 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 โ 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.
What are some alternatives?
MLflow - Open source platform for the machine learning lifecycle
scalar - Scalar: A set of tools and extensions for Git to allow very large monorepos to run on Git without a virtualization layer
lakeFS - lakeFS - Data version control for your data lake | Git for data
docker-merge - Docker images as git repositories, so you can merge them.
Activeloop Hub - Data Lake for Deep Learning. Build, manage, query, version, & visualize datasets. Stream data real-time to PyTorch/TensorFlow. https://activeloop.ai [Moved to: https://github.com/activeloopai/deeplake]
Task - A task runner / simpler Make alternative written in Go
delta - An open-source storage framework that enables building a Lakehouse architecture with compute engines including Spark, PrestoDB, Flink, Trino, and Hive and APIs
oxen-release - Lightning fast data version control system for structured and unstructured machine learning datasets. We aim to make versioning datasets as easy as versioning code.
ploomber - The fastest โก๏ธ way to build data pipelines. Develop iteratively, deploy anywhere. โ๏ธ
pachyderm - Data-Centric Pipelines and Data Versioning
aim - Aim ๐ซ โ An easy-to-use & supercharged open-source experiment tracker.
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.