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Using DVC Commands

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

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

Typical DVC workflow

  • Initialize a DVC project in a Git repo with dvc 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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