GraphCanon updated 2w · GitHub synced 2w
Decision brief
DVC is a command-line tool for reproducible ML projects, enabling data versioning, lightweight pipelines, experiment tracking, comparison, and sharing.
Good fit when
- Need to manage large datasets while only syncing version information with Git
- Frequent iterations required without rerunning unaffected pipeline steps
Avoid when
- Looking for a GUI-focused tool for data versioning and analysis
- Require full cloud orchestration services beyond basic DVCS capabilities
Observed Jul 12, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Very active (3d since push)
- As of 2w
- Provenance
- Not a fork · Organization account
- As of 2w
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install dvcSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
DVC is a command-line tool that enables reproducible machine learning projects by versioning data and models, iterating with lightweight pipelines, tracking experiments in Git, comparing various aspects of experiments, and sharing experiments for automatic reproduction.
Capability facts
- CLI
- CLI entrypoint
Source: pyproject.toml:[project.scripts] · Aug 3, 2026
- Languages
- python
Source: github.language+pyproject.toml · Aug 3, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 3, 2026)
|CI| |Python Version| |Coverage| |VS Code| |DOI|Source link
Source: README excerpt (regex_v1, Aug 3, 2026)
• `VS Code Extension`_Source link
Tags
README
|Banner|
Website <https://dvc.org>_
• Docs <https://dvc.org/doc>_
• Blog <http://blog.dataversioncontrol.com>_
• Tutorial <https://dvc.org/doc/get-started>_
• Related Technologies <https://dvc.org/doc/user-guide/related-technologies>_
• How DVC works_
• VS Code Extension_
• Installation_
• Contributing_
• Community and Support_
|CI| |Python Version| |Coverage| |VS Code| |DOI|
|PyPI| |PyPI Downloads| |Packages| |Brew| |Conda| |Choco| |Snap|
|
Data Version Control or DVC is a command line tool and VS Code Extension_ to help you develop reproducible machine learning projects:
#. Version your data and models. Store them in your cloud storage but keep their version info in your Git repo.
#. Iterate fast with lightweight pipelines. When you make changes, only run the steps impacted by those changes.
#. Track experiments in your local Git repo (no servers needed).
#. Compare any data, code, parameters, model, or performance plots.
#. Share experiments and automatically reproduce anyone's experiment.
Quick start
Please read our `Command Reference <https://dvc.org/doc/command-reference>`_ for a complete list.
A common CLI workflow includes:
+-----------------------------------+----------------------------------------------------------------------------------------------------+
| Task | Terminal |
+===================================+====================================================================================================+
| Track data | | $ git add train.py params.yaml |
| | | $ dvc add images/ |
+-----------------------------------+----------------------------------------------------------------------------------------------------+
| Connect code and data | | $ dvc stage add -n featurize -d images/ -o features/ python featurize.py |
| | | $ dvc stage add -n train -d features/ -d train.py -o model.p -M metrics.json python train.py |
+-----------------------------------+----------------------------------------------------------------------------------------------------+
| Make changes and experiment | | $ dvc exp run -n exp-baseline |
| | | $ vi train.py |
| | | $ dvc exp run -n exp-code-change |
+-----------------------------------+----------------------------------------------------------------------------------------------------+
| Compare and select experiments | | $ dvc exp show |
| | | $ dvc exp apply exp-baseline |
+-----------------------------------+----------------------------------------------------------------------------------------------------+
| Share code | | $ git add . |
| | | $ git commit -m 'The baseline model' |
| | | $ git push |
+-----------------------------------+-------------------------------------------------------------------------------------
For agents
This page has a .md twin and JSON over the API.