Get Started

Assuming DVC is already installed, let's initialize it by running dvc init inside a Git project:

⚙️ Expand to prepare the project.

In expandable sections that start with the ⚙️ emoji, we'll be providing more information for those trying to run the commands. It's up to you to pick the best way to read the material - read the text (skip sections like this, and it should be enough to understand the idea of DVC), or try to run them and get the first hand experience.

We'll be building an NLP project from scratch together. The end result is published on GitHub.

Let's start with git init:

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

$ 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!

DVC's features can be grouped into functional components. We'll explore them one by one in the next few pages:

Data versioning (try this next) is the base layer of DVC for large files, datasets, and machine learning models. Use a regular Git workflow, but without storing large files in the repo (think "Git for data"). Data is stored separately, which allows for efficient sharing.
Data access shows how to use data artifacts from outside of the project and how to import data artifacts from another DVC project. This can help to download a specific version of an ML model to a deployment server or import a model to another project.
Data pipelines describe how models and other data artifacts are built, and provide an efficient way to reproduce them. Think "Makefiles for data and ML projects" done right.
Metrics, parameters, and plots can be attached to pipelines. These let you capture, navigate, and evaluate ML projects without leaving Git. Think "Git for machine learning".
Experiments enable exploration, iteration, and comparison across many ML experiments. Track your experiments with automatic versioning and checkpoint logging. Compare differences in parameters, metrics, code, and data. Apply, drop, roll back, resume, or share any experiment.