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  :alt: Travis

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  :alt: Codecov

`Website <https://dvc.org>`_ • `Docs <https://dvc.org/doc>`_ • `Discuss <https://discuss.dvc.org/>`_ •
`Blog <https://blog.dataversioncontrol.com/>`_ • `Twitter <https://twitter.com/iterativeai>`_ •
`Tutorial <https://dvc.org/tutorial>`_

.. raw:: html

  <p align="center"><img src="https://dvc.org/static/img/logo-github-readme.png" height="161px" alt="DVC"></p>

Data Science Version Control or DVC is an open source tool for data science projects.
It helps data scientists manage their code and data together in a simple form of Git-like commands.

.. contents:: **Contents**
  :backlinks: none

Get started
===========

+-----------------------------------+-------------------------------------------------------------------+
| Step                              | Command                                                           |
+===================================+===================================================================+
| Track code and data together      | | ``$ git add train.py``                                          |
|                                   | | ``$ dvc add images.zip``                                        |
+-----------------------------------+-------------------------------------------------------------------+
| Connect code and data by commands | | ``$ dvc run -d images.zip -o images/ unzip -q images.zip``      |
|                                   | | ``$ dvc run -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 s3://mybucket/image_cnn``           |
|                                   | | ``$ dvc core.remote myremote``                                  |
|                                   | | ``$ dvc push``                                                  |
+-----------------------------------+-------------------------------------------------------------------+

Follow this link to learn more and get more details: `get started <https://dvc.org/doc/get-started>`_.

Installation
============

It could be installed using ``pip``, Homebrew or an OS-specific package.

.. code-block:: bash

   pip install dvc

Package
-------

Self-contained packages for Windows, Linux, Mac are available. The latest version of the packages can be found at
GitHub `releases page <https://github.com/iterative/dvc/releases>`_.

Homebrew
--------

.. code-block:: bash

   brew install iterative/homebrew-dvc/dvc

or:

.. code-block:: bash

   brew cask install iterative/homebrew-dvc/dvc

Related technologies
====================

#. `Git-annex <https://git-annex.branchable.com/>`_ - DVC uses the idea of storing the content of large files (that you
   don't want to see in your Git repository) in a local key-value store and uses file hardlinks/symlinks instead of the
   copying actual files.

#. `Git-LFS <https://git-lfs.github.com/>`_. - DVC is compatible with any remote storage (S3, Google Cloud, Azure, SSH,
   etc). DVC utilizes reflinks or hardlinks to avoid copy operation on checkouts which makes much more efficient for
   large data files.

#. Makefile (and it's analogues). DVC tracks dependencies (DAG).

#. `Workflow Management Systems <https://en.wikipedia.org/wiki/Workflow_management_system>`_. DVC is a workflow
   management system designed specifically to manage machine learning experiments. DVC is built on top of Git.

DVC is compatible with Git for storing code and the dependency graph (DAG), but not data files cache.
To store and share data files cache DVC supports remotes - any cloud (S3, Azure, Google Cloud, etc) or any on-premise
network storage (via SSH, for example).

How DVC works
=============


.. image:: https://dvc.org/static/img/flow.png
   :target: https://dvc.org/static/img/flow.png
   :alt: how_dvc_works


Contributing
============
Contributions are welcome! Please see our `Contributing Guide <https://dvc.org/doc/user-guide/contributing/>`_ for more
details.

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 for this project, you agree to license your contribution under the Apache license version
2.0 to this project.