jj
dvc
jj | dvc | |
---|---|---|
88 | 109 | |
6,673 | 13,116 | |
- | 1.4% | |
10.0 | 9.7 | |
about 3 hours ago | 6 days ago | |
Rust | Python | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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.
jj
- Why Don't I Like Git More?
-
Twenty Years Is Nothing
Jujutsu is along the lines of what you describe: https://github.com/martinvonz/jj
You can drop it in and work seamlessly from git repos
-
Git Branches as a Social Construct
Pull Requests (or Merge Requests) are merged only when (1) all of the automated tests pass; and (2) enough necessary reviewers have indicated approval.
Git doesn't tell you when it's necessary to have full test coverage and manual infosec review in development cycles that produce releases, and neither do Pull Requests.
https://westurner.github.io/hnlog/#comment-19552164 ctrl-f hubflow
It looks like datasift's gitflow/hubflow docs are 404'ing, but the original nvie blog post [1] has the Git branching workflow diagrams; which the wpsharks/hubflow fork [3] of datasift/gitflow fork [2] of gitflow [1]has a copy of in the README:
[1] https://github.com/nvie/gitflow
[2] https://github.com/datasift/gitflow
[3] https://github.com/wpsharks/hubflow?tab=readme-ov-file
https://learngitbranching.js.org/ is still a great resource, and it could work on mobile devices.
The math of VCS deltas and mutable and immutable content-addressed DAG nodes identified by 2^n bits describing repo/$((2*inf)) bits ;
>> "ugit – Learn Git Internals by Building Git in Python" https://www.leshenko.net/p/ugit/
SLSA.dev is a social construct atop e.g. git, which is really a low-level purpose-built tool and Perl and now Python porcelain.
jj (jujutsu) is a git-compatible VCS CLI: https://github.com/martinvonz/jj
"Ask HN: Best Git workflow for small teams" (2016)
-
PyPy has moved to Git, GitHub
You will probably like Jujutsu, which takes much inspiration from Mercurial: https://github.com/martinvonz/jj
It isn't a 1-to-1 clone, either. But tools like revsets are there, cset evolution is "built in" to the design, etc. There is no concept of phases, we might think about adding that, but there is a concept of immutable commits (so you don't overwrite public ones.)
It also has many novel features that make it stand out. We care a lot about performance and usability. Give it a shot. I think you might be pleasantly surprised.
Disclosure: I am a developer of Jujutsu. I do it in my spare time.
-
Ask HN: Can we do better than Git for version control?
I have created a discussion. Thank you both
https://github.com/martinvonz/jj/discussions/2691
-
I (kind of) killed Mercurial at Mozilla
> why don't version control systems (especially ones that can change history) have undo/redo functionality out of the box?
It's true. And Jujutsu has undo functionality out of the box, too. It's not just Sapling. :) https://github.com/martinvonz/jj
- Confusing Git Terminology
-
Things I just don't like about Git
Git made the only choice a popular VCS can make. History rewrites will exist, period. If you're opposed to history rewrites, then git gives you the tools to ensure the repos you control are not rewritten, and that's all it can do in a world where people have control of their own computers.
If Fossil ever becomes as popular as git, people will create software that allows history rewriting in Fossil, and that's fine. People will do what they want on their own computer, and I think it's morally wrong to try and stop that.
Another user in this thread linked to jj [0], an alternative git client that does some pretty weird things. For example, it replaces the working tree with a working commit and commits quite often. I like git and that seems weird to me, but I'm not offended, people can do what they want on their own computer and I have the tools to ensure repos under my control are not effected. That's all I can hope for.
[0]: https://github.com/martinvonz/jj
-
Pijul: Version-Control Post-Git • Goto 2023
I recently found out about another project called jj: https://github.com/martinvonz/jj. It takes inspiration from Pijul and others but is git-compatible.
-
A beginner's guide to Git version control
https://github.com/martinvonz/jj
I think maybe both fossil and bitkeeper are more intuitive too.
Did you try any of those?
dvc
-
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.
-
Why bad scientific code beats code following "best practices"
What you’re describing sounds like DVC (at a higher-ish—80%-solution level).
https://dvc.org/
See pachyderm too.
-
First 15 Open Source Advent projects
10. DVC by Iterative | Github | tutorial
-
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
-
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?
-
Exploring MLOps Tools and Frameworks: Enhancing Machine Learning Operations
DVC (Data Version Control):
- Evaluate and Track Your LLM Experiments: Introducing TruLens for LLMs
-
[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.
What are some alternatives?
git-branchless - High-velocity, monorepo-scale workflow for Git
MLflow - Open source platform for the machine learning lifecycle
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.
lakeFS - lakeFS - Data version control for your data lake | Git for data
forgit - :zzz: A utility tool powered by fzf for using git interactively.
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]
EdenSCM - A Scalable, User-Friendly Source Control System. [Moved to: https://github.com/facebook/sapling]
delta - An open-source storage framework that enables building a Lakehouse architecture with compute engines including Spark, PrestoDB, Flink, Trino, and Hive and APIs
pre-commit - A framework for managing and maintaining multi-language pre-commit hooks.
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
git-imerge - Incremental merge for git
aim - Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.