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Training & fine-tuning

DVC

Keeps track of versions of the data, models and experiments behind a machine-learning project.

What it does

DVC, short for Data Version Control, helps teams keep machine-learning projects organised and repeatable. Tools like Git track changes to code, but not to large data files and models. DVC fills that gap.

Your data and models are stored in cloud storage, while DVC records which version goes with which code. It also runs project steps as pipelines, re-running only what changed, and tracks experiments so you can compare results, settings and charts.

Anyone on the team can share and reproduce an experiment. It's a command-line tool with a VS Code extension.

Who it's for

Data scientists and machine-learning engineers who want to track and reproduce their experiments.

Can I use it without coding?

Developers only. It's a command-line tool that works alongside Git. New to this? Start here.

Can I run it?

Cost
FreeNothing to pay or sign up for.
Runs on
Mac, Windows, Linux
Computer
An ordinary computer is fine
Privacy
Runs on your own device: your data stays with you
API key
Not needed

Try it

  1. Install DVC with Homebrew on Mac, Chocolatey on Windows, or Snap on Linux.
  2. Use it inside a Git project to track versions of your data files and models.
  3. Optionally install the DVC extension for VS Code to use it with buttons.

Using Claude Code, Cursor or another AI coding assistant? Paste the prompt and it will walk you through the setup.New to this?

Before you install

  • Only download from the official GitHub page. Copies with similar names exist for many popular projects.
  • License (Apache-2.0): Permissive: free to use, change and share, including for business.
  • More on staying safe

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