example-get-started
Get started DVC project (by iterative)
dvc
🦉 ML Experiments and Data Management with Git (by iterative)
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| example-get-started | dvc | |
|---|---|---|
| 2 | 109 | |
| 167 | 13,116 | |
| 0.0% | 1.3% | |
| 0.0 | 9.7 | |
| 6 days ago | about 4 hours ago | |
| Python | Python | |
| - | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
example-get-started
Posts with mentions or reviews of example-get-started.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-06-27.
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VS Code extension to track ML experiments
Or open this project https://github.com/iterative/example-get-started in GitHub Codespaces as an example. It will run the extension in Codespaces automatically.
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Tuning Hyperparameters with Reproducible Experiments
We're going to be working with an existing NLP project. You can get the code we're working with in this repo. It already has DVC set up, but you can check out the Get Started docs if you want to know how the DVC pipeline was created.
dvc
Posts with mentions or reviews of dvc.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-04-23.
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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 you’re describing sounds like DVC (at a higher-ish—80%-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.