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|Banner|
`Website <https://dvc.org>`_
• `Docs <https://dvc.org/doc>`_
• `Blog <http://blog.dataversioncontrol.com>`_
• `Twitter <https://twitter.com/DVCorg>`_
• `Chat (Community & Support) <https://dvc.org/chat>`_
• `Tutorial <https://dvc.org/doc/get-started>`_
• `Mailing List <https://sweedom.us10.list-manage.com/subscribe/post?u=a08bf93caae4063c4e6a351f6&id=24c0ecc49a>`_
|CI| |Maintainability| |Coverage| |Donate| |DOI|
|PyPI| |Packages| |Brew| |Conda| |Choco| |Snap|
|
**Data Version Control** or **DVC** helps you develop reproducible machine
learning projects:
#. Version your data and models.
#. Connect them to your code and parameters to form a pipeline.
#. Modify data, code, or parameters and run your modified pipeline.
#. Iterate and track each experiment.
#. Share, compare, and reconstruct experiments.
**Why DVC?**
#. **Store** data and models (big) in your cloud storage, but keep their version
info (small) in your Git repo.
#. **Iterate** fast with straightforward YAML pipelines. When you make changes,
only run the steps impacted by those changes.
#. **Track** experiments in your local Git repo (no servers needed) and choose
which to keep and share.
#. **Compare** any data, code, parameters, metrics, or plots and analyze however
you want.
#. **Reconstruct** anyone's experiment with a single command.
.. contents:: **Contents**
:backlinks: none
How DVC works
=============
We encourage you to read our `Get Started <https://dvc.org/doc/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.
#. ``Git/Git-LFS`` part - DVC helps store and share data artifacts and models, connecting them with a Git repository.
#. ``Makefile``\ s 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).
|Flowchart|
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 <https://dvc.org/doc/get-started>`_ tutorial to learn more.
Quick start
===========
Please read `Get Started <https://dvc.org/doc/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 by commands | | ``$ 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 <https://dvc.org/doc/get-started/install>`_.
Snap (Snapcraft/Linux)
----------------------
|Snap|
.. code-block:: bash
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)
--------------------------
|Choco|
.. code-block:: bash
choco install dvc
Brew (Homebrew/Mac OS)
----------------------
|Brew|
.. code-block:: bash
brew install dvc
Conda (Anaconda)
----------------
|Conda|
.. code-block:: bash
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|
.. code-block:: bash
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:
.. code-block:: bash
pip install git+git://github.com/iterative/dvc
Package
-------
|Packages|
Self-contained packages for Linux, Windows, and Mac are available. The latest version of the packages
can be found on the GitHub `releases page <https://github.com/iterative/dvc/releases>`_.
Ubuntu / Debian (deb)
^^^^^^^^^^^^^^^^^^^^^
.. code-block:: bash
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)
^^^^^^^^^^^^^^^^^^^^^
.. code-block:: bash
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
==================================
#. Data Engineering tools such as `AirFlow <https://airflow.apache.org/>`_,
`Luigi <https://github.com/spotify/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.
#. `Git-annex <https://git-annex.branchable.com/>`_ - 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.
#. `Git-LFS <https://git-lfs.github.com/>`_ - 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.
#. Makefile (and analogues including ad-hoc scripts) - DVC tracks
dependencies (in a directed acyclic graph).
#. `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.
#. `DAGsHub <https://dagshub.com/>`_ - online service to host DVC
projects. It provides a useful UI around DVC repositories and integrates
other tools.
#. `DVC Studio <https://studio.iterative.ai/>`_ - 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) <https://cml.dev/>`__ for training
models in the cloud or Kubernetes.
Contributing
============
|Maintainability| |Donate|
Contributions are welcome! Please see our `Contributing Guide <https://dvc.org/doc/user-guide/contributing/core>`_ for more
details. Thanks to all our contributors!
|Contribs|
Mailing List
============
Want to stay up to date? Want to help improve DVC by participating in our occasional polls? Subscribe to our `mailing list <https://sweedom.us10.list-manage.com/subscribe/post?u=a08bf93caae4063c4e6a351f6&id=24c0ecc49a>`_. 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 <https://doi.org/10.5281/zenodo.3677553>`_.
Barrak, A., Eghan, E.E. and Adams, B. `On the Co-evolution of ML Pipelines and Source Code - Empirical Study of DVC Projects <https://mcis.cs.queensu.ca/publications/2021/saner.pdf>`_ , in Proceedings of the 28th IEEE International Conference on Software Analysis, Evolution, and Reengineering, SANER 2021. Hawaii, USA.
.. |Banner| image:: https://dvc.org/img/logo-github-readme.png
:target: https://dvc.org
:alt: DVC logo
.. |CI| image:: https://github.com/iterative/dvc/workflows/Tests/badge.svg?branch=main
:target: https://github.com/iterative/dvc/actions
:alt: GHA Tests
.. |Maintainability| image:: https://codeclimate.com/github/iterative/dvc/badges/gpa.svg
:target: https://codeclimate.com/github/iterative/dvc
:alt: Code Climate
.. |Coverage| image:: https://codecov.io/gh/iterative/dvc/branch/main/graph/badge.svg
:target: https://codecov.io/gh/iterative/dvc
:alt: Codecov
.. |Donate| image:: https://img.shields.io/badge/patreon-donate-green.svg?logo=patreon
:target: https://www.patreon.com/DVCorg/overview
:alt: Donate
.. |Snap| image:: https://img.shields.io/badge/snap-install-82BEA0.svg?logo=snapcraft
:target: https://snapcraft.io/dvc
:alt: Snapcraft
.. |Choco| image:: https://img.shields.io/chocolatey/v/dvc?label=choco
:target: https://chocolatey.org/packages/dvc
:alt: Chocolatey
.. |Brew| image:: https://img.shields.io/homebrew/v/dvc?label=brew
:target: https://formulae.brew.sh/formula/dvc
:alt: Homebrew
.. |Conda| image:: https://img.shields.io/conda/v/conda-forge/dvc.svg?label=conda&logo=conda-forge
:target: https://anaconda.org/conda-forge/dvc
:alt: Conda-forge
.. |PyPI| image:: https://img.shields.io/pypi/v/dvc.svg?label=pip&logo=PyPI&logoColor=white
:target: https://pypi.org/project/dvc
:alt: PyPI
.. |Packages| image:: https://img.shields.io/github/v/release/iterative/dvc?label=deb|pkg|rpm|exe&logo=GitHub
:target: https://github.com/iterative/dvc/releases/latest
:alt: deb|pkg|rpm|exe
.. |DOI| image:: https://img.shields.io/badge/DOI-10.5281/zenodo.3677553-blue.svg
:target: https://doi.org/10.5281/zenodo.3677553
:alt: DOI
.. |Flowchart| image:: https://dvc.org/img/flow.gif
:target: https://dvc.org/img/flow.gif
:alt: how_dvc_works
.. |Contribs| image:: https://contrib.rocks/image?repo=iterative/dvc
:target: https://github.com/iterative/dvc/graphs/contributors
:alt: Contributors
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