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Data Version Control or DVC is an open-source tool for data
science and machine learning projects. Key features:

1. Simple command line Git-like experience. Does not require
   installing and maintaining any databases. Does not depend on any
   proprietary online services.
2. Management and versioning of datasets and machine learning
   models. Data is saved in S3, Google cloud, Azure, Alibaba cloud,
   SSH server, HDFS, or even local HDD RAID.
3. Makes projects reproducible and shareable; helping to answer
   questions about how a model was built.
4. Helps manage experiments with Git tags/branches and metrics
   tracking.

DVC aims to replace spreadsheet and document sharing tools (such as
Excel or Google Docs) which are being used frequently as both knowledge
repositories and team ledgers. DVC also replaces both ad-hoc scripts to
track, move, and deploy different model versions; as well as ad-hoc data
file suffixes and prefixes.

::: {.contents}
Contents
:::

How DVC works

We encourage you to read our Get
Started guide to better understand
what DVC is 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).

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 Get Started guide for a
full version. Common workflow commands include:

+-----------------------+----------------------------------------------+
| Step | Command |
+=======================+==============================================+
| Track data | | $ git add train.py |
| | | $ dvc add images.zip |
+-----------------------+----------------------------------------------+
| Connect code and data | | $ dvc run -d images.zip -o images/ unzip | | by commands | -q images.zip |
| | | $ dvc run -d images/ -d train.py -o model | | | .p python train.py |
+-----------------------+----------------------------------------------+
| Make changes and | | $ vi train.py |
| reproduce | | $ dvc repro model.p.dvc |
+-----------------------+----------------------------------------------+
| Share code | | $ git add . |
| | | $ git commit -m 'The baseline model' |
| | | $ git push |
+-----------------------+----------------------------------------------+
| Share data and ML | | $ dvc remote add myremote -d s3://mybucke | | models | t/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.

Snap (Snapcraft/Linux)

snap install dvc --classic

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

Choco (Chocolatey/Windows)

choco install dvc

Brew (Homebrew/Mac OS)

brew install dvc

Conda (Anaconda)

conda install -c conda-forge dvc

Currently, this includes support for Python versions 2.7, 3.6 and 3.7.

pip (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

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
sudo apt-get update
sudo apt-get install dvc

Fedora / CentOS (rpm)

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

Comparison to related technologies

1. 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.
2. Git-LFS - DVC is compatible with any
   remote storage (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.
3. Makefile (and analogues including ad-hoc scripts) - DVC tracks
   dependencies (in a directed acyclic graph).
4. Workflow Management
   Systems -
   DVC is a workflow management system designed specifically to manage
   machine learning experiments. DVC is built on top of Git.
5. DAGsHub - This is a Github equivalent for
   DVC. Pushing Git+DVC based repositories to DAGsHub will produce in a
   high level project dashboard; including DVC pipelines and metrics
   visualizations, as well as links to any DVC-managed files present in
   cloud storage.

Contributing

Contributions are welcome! Please see our Contributing
Guide for more
details.

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

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