#4242nd of 56 in ML frameworks and libraries
dvc
Git-based versioning for data, models and ML pipelines
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- Stars
- 15.9k
- License
- Apache-2.0
- Last commit
- Aug 2026
- Last release
- Mar 2026
- Language
- Python
Missed the latest GitHub check; these facts may be older.
Overview
DVC is a Python command line tool that keeps data and model files in a cache and remote storage while tracking small metafiles in Git. It defines pipelines as stages with dependencies and outputs, reruns only the steps affected by a change, and tracks experiments in the local Git repo without a server. A VS Code extension provides a GUI for experiments and data management.
Who it is for: ML engineers who want reproducible data, pipelines and experiments in Git
Strengths
- No server needed: experiments and data versions live in your Git repo
- Remote storage on S3, Azure, Google Cloud, SSH and other backends
- Pipelines rerun only stages whose dependencies changed
- Installs via pip, brew, conda, choco, snap, deb or rpm
Weaknesses
- Command line first; the GUI is a separate VS Code extension that needs core DVC
- Remote backends require optional extras such as dvc[s3] or dvc[azure]
- Requires Git; no built-in hosted UI for team experiment comparison
- README gives no hardware requirements or scale limits
What it needs
- no GPU
- Needs Git, Remote storage (S3, Azure, Google Cloud, SSH, etc.)
Also in ML frameworks and libraries
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|---|---|---|
| 1 | transformersPython library defining open models for text, vision, audio and multimodal tasks | 95 out of 100 |
| 2 | pytorchPython tensor library with GPU support and tape-based autograd for deep learning | 94 out of 100 |
| 3 | ultralyticsPython library and CLI for training and running YOLO vision models | 92 out of 100 |
| #4 | supervisionPython toolkit for loading, annotating and counting computer vision detections | 89 out of 100 |
| #5 | pytorch-image-modelsPyTorch library of image models, pretrained weights and reference training scripts | 89 out of 100 |