Get Started with DVC

Before we begin, settle on a directory for this guide. Everything we will do will be self contained there.

$ mkdir example-get-started
$ cd example-get-started
$ git init

Initializing a project

Inside your chosen directory, we will use our current working directory as a DVC project. Let's initialize it by running dvc init inside a Git project:

$ dvc init

A few internal files are created that should be added to Git:

$ git status
Changes to be committed:
        new file:   .dvc/.gitignore
        new file:   .dvc/config
        ...
$ git commit -m "Initialize DVC"

Now you're ready to DVC!

Following This Guide

To help you understand and use DVC better, consider the following two use-cases: data management and experiment tracking. You may pick either one to start learning about how DVC helps you "solve" that scenario!

Choose a trail to jump into its first chapter:

• Data Management - Track and version large amounts of data along with your code, and use DVC as a build system for reproducible, data driven pipelines.

• Experiment Management - Easily track your experiments and their progress by only instrumenting your code, and collaborate on ML experiments like software engineers do for code.