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Data Version Control or DVC is a command line tool and VS Code Extension to help you develop reproducible machine learning projects:

  1. Version your data and models. Store them in your cloud storage but keep their version info in your Git repo.
  2. Iterate fast with lightweight pipelines. When you make changes, only run the steps impacted by those changes.
  3. Track experiments in your local Git repo (no servers needed).
  4. Compare any data, code, parameters, modelhttps://www.youtube.com/watch?v=IYhVmD-_wRometrics, or performance plots
  5. Share experiments and automatically reproduce anyone's experiment.

How DVC works

We encourage you to read our Get Started guides to better understand what DVC does and how it can fit your scenarios.

The easiest (but not perfect!) analogy to describe it: DVC is Git (or Git-LFS to be precise) & Makefiles made right and tailored specifically for ML and Data Science scenarios.

  1. Git/Git-LFS part - DVC helps store and share data artifacts and models, connecting them with a Git repository.
  2. Makefiles part - DVC describes how one data or model artifact was built from other data and code.

DVC usually runs along with Git. Git is used as usual to store and version code (including DVC meta-files). DVC helps to store data and model files seamlessly out of Git, while preserving almost the same user experience as if they were stored in Git itself. To store and share the data cache, DVC supports multiple remotes - any cloud (S3, Azure, Google Cloud, etc) or any on-premise network storage (via SSH, for example).

how_dvc_works

The DVC pipelines (computational graph) feature connects code and data together. It is possible to explicitly specify all steps required to produce a model: input dependencies including data, commands to run, and output information to be saved. See the quick start section below or the Get Started tutorial to learn more.

Quick start

Please read our `Command Reference https://dvc.org/doc/command-reference>`_ for a full reference.

Common workflow commands include:

Task Terminal
Track data
$ git add train.py
$ dvc add images.zip
Connect code and data
$ dvc run -n prepare -d images.zip -o images/ unzip -q images.zip
$ dvc run -n train -d images/ -d train.py -o model.p python train.py
Make changes and reproduce
$ vi train.py
$ dvc repro model.p.dvc
Share code
$ git add .
$ git commit -m 'The baseline model'
$ git push
Share data and ML models
$ dvc remote add myremote -d s3://mybucket/image_cnn
$ dvc push

Installation

There are four options to install DVC: pip, Homebrew, Conda (Anaconda) or an OS-specific package. Full instructions are available here.

To use with VS Code, install DVC and then install the DVC Extension.

Snap (Snapcraft/Linux)

Snapcraft

snap install dvc --classic

This corresponds to the latest tagged release. Add --beta for the latest tagged release candidate, or --edge for the latest main version.

Choco (Chocolatey/Windows)

Chocolatey

choco install dvc

Brew (Homebrew/Mac OS)

Homebrew

brew install dvc

Conda (Anaconda)

Conda-forge

conda install -c conda-forge mamba # installs much faster than conda
mamba install -c conda-forge dvc

Depending on the remote storage type you plan to use to keep and share your data, you might need to install optional dependencies: dvc-s3, dvc-azure, dvc-gdrive, dvc-gs, dvc-oss, dvc-ssh.

pip (PyPI)

PyPI

pip install dvc

Depending on the remote storage type you plan to use to keep and share your data, you might need to specify one of the optional dependencies: s3, gs, azure, oss, ssh. Or all to include them all. The command should look like this: pip install dvc[s3] (in this case AWS S3 dependencies such as boto3 will be installed automatically).

To install the development version, run:

pip install git+git://github.com/iterative/dvc

Package

deb|pkg|rpm|exe

Self-contained packages for Linux, Windows, and Mac are available. The latest version of the packages can be found on the GitHub releases page.

Ubuntu / Debian (deb)

sudo wget https://dvc.org/deb/dvc.list -O /etc/apt/sources.list.d/dvc.list
wget -qO - https://dvc.org/deb/iterative.asc | sudo apt-key add -
sudo apt update
sudo apt install dvc

Fedora / CentOS (rpm)

sudo wget https://dvc.org/rpm/dvc.repo -O /etc/yum.repos.d/dvc.repo
sudo rpm --import https://dvc.org/rpm/iterative.asc
sudo yum update
sudo yum install dvc

Comparison to related technologies

  1. Data Engineering tools such as AirFlow, Luigi, and others - in DVC data, model and ML pipelines represent a single ML project focused on data scientists' experience. Data engineering tools orchestrate multiple data projects and focus on efficient execution. A DVC project can be used from existing data pipelines as a single execution step.
  2. Git-annex - DVC uses the idea of storing the content of large files (which should not be in a Git repository) in a local key-value store, and uses file hardlinks/symlinks instead of copying/duplicating files.
  3. Git-LFS - DVC is compatible with many remote storage services (S3, Google Cloud, Azure, SSH, etc). DVC also uses reflinks or hardlinks to avoid copy operations on checkouts; thus handling large data files much more efficiently.
  4. Makefile (and analogues including ad-hoc scripts) - DVC tracks dependencies (in a directed acyclic graph).
  5. Workflow Management Systems - DVC is a workflow management system designed specifically to manage machine learning experiments. DVC is built on top of Git.
  6. DAGsHub - online service to host DVC projects. It provides a useful UI around DVC repositories and integrates other tools.
  7. DVC Studio - official online platform for DVC projects. It can be used to manage data and models, run and track experiments, and visualize and share results. Also, it integrates with CML (CI/CD for ML) for training models in the cloud or Kubernetes.

Contributing

Code Climate

Contributions are welcome! Please see our Contributing Guide for more details. Thanks to all our contributors!

Contributors

Mailing List

Want to stay up to date? Want to help improve DVC by participating in our occasional polls? Subscribe to our mailing list. No spam, really low traffic.

Copyright

This project is distributed under the Apache license version 2.0 (see the LICENSE file in the project root).

By submitting a pull request to this project, you agree to license your contribution under the Apache license version 2.0 to this project.

Citation

DOI

Iterative, DVC: Data Version Control - Git for Data & Models (2020) DOI:10.5281/zenodo.012345.

Barrak, A., Eghan, E.E. and Adams, B. On the Co-evolution of ML Pipelines and Source Code - Empirical Study of DVC Projects , in Proceedings of the 28th IEEE International Conference on Software Analysis, Evolution, and Reengineering, SANER 2021. Hawaii, USA.

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