treeverse/dvc

Health v2 · maintenance only

Adoption not part of the score

15843 stars · 1323 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

DVC (Data Version Control) is an open-source command line tool and VS Code extension for versioning data and models, building lightweight ML pipelines, and tracking experiments in Git. It stores large data in cloud or remote storage while keeping version metadata in the Git repository, enabling reproducible machine learning projects.

Use cases

• version large datasets and ML models alongside code in Git
• track and compare machine learning experiments locally
• build reproducible data science pipelines with dependency-aware stages
• share and reproduce teammates' experiments
• store data in S3, GCS, Azure, or other remote storage without committing to Git
• run CI/CD for machine learning projects

When to choose

• you need Git-like version control for datasets and models too large for Git
• you want lightweight, local-first ML experiment tracking without servers
• you build reproducible ML pipelines with incremental re-runs
• you work in Python-based data science workflows and want minimal overhead

When to avoid

• you need petabyte-scale, branch-and-merge data lake versioning (consider lakeFS)
• your project has no data or model artifacts to version
• you need a full hosted experiment tracking UI with team dashboards out of the box

Facets

cli-tool · maturity active

version-control developer-tools workflow-automation data-science machine-learning configuration-management data-science machine-learning developer-tools version-control windows python cli cross-platform data-version-control mlops experiment-tracking pipelines reproducibility git vs-code-extension data-engineering linux macos

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markdown · JSON · MCP: product_card(name="treeverse/dvc")