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Using DVC Commands

The core of DVC is a command line tool. These pages contain the specifications, self-contained descriptions, and comprehensive usage examples for dvc commands. Use dvc -h to list them.

To run DVC commands in a specific directory, use dvc --cd <path> ... before the actual command and its options/arguments (this does not change directories in your terminal).

New! DVC is also available for the VS Code IDE, which adds many DVC operations to the Command Palette.

Typical DVC workflow

  • Initialize a DVC project in a Git repo with dvc init or dvc exp init.
  • Copy data files or dataset directories for modeling into the project and use dvc add to tell DVC to cache and track them.
  • Create a simple dvc.yaml file to codify a data processing pipeline. It uses your own source code and specifies further data outputs for DVC to control.
  • Execute or restore any version of your pipeline using dvc repro, or experiment on it with dvc exp features.
  • Sharing the repository will not include locally cached data. Use remote storage with dvc push and dvc pull to share data artifacts.

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