Package: dvc
Website•Docs•Blog•Tutorial•Related Technologies•How DVC works•VS Code Extension•Installation•Contributing•Community and SupportData Version ControlorDVCis a command line tool andVS Code Extensionto help you develop reproducible machine learning projects:Versionyour data and models.
Store them in your cloud storage but keep their version info in your Git repo.Iteratefast with lightweight pipelines.
When you make changes, only run the steps impacted by those changes.Trackexperiments in your local Git repo (no servers needed).Compareany data, code, parameters, model, or performance plots.Shareexperiments and automatically reproduce anyone’s experiment.Quick startPlease read ourCommand Referencefor a complete list.A common CLI workflow includes:TaskTerminalTrack data$ git add train.py params.yaml$ dvc add images/Connect code and data$ dvc stage add-nfeaturize-dimages/-ofeatures/ python featurize.py$ dvc stage add-ntrain-dfeatures/-dtrain.py-omodel.p-Mmetrics.json python train.pyMake changes and experiment$ dvc exp run-nexp-baseline$ vi train.py$ dvc exp run-nexp-code-changeCompare and select experiments$ dvc exp show$ dvc exp applyexp-baselineShare code$ git add .$ git commit-m'The baseline model'$ git pushShare data and ML models$ dvc remote add myremote-ds3://mybucket/image_cnn$ dvc pushHow DVC worksWe encourage you to read ourGet Starteddocs to better understand what DVC
does and how it can fit your scenarios.The closestanalogiesto describe the main DVC features are these:Git for data: Store and share data artifacts (like Git-LFS but without a server) and models, connecting them with a Git repository. Data management meets GitOps!Makefilesfor ML: Describes how data or model artifacts are built from other data and code in a standard format. Now you can version your data pipelines with Git.Localexperiment tracking: Turn your machine into an ML experiment management platform, and collaborate with others using existing Git hosting (Github, Gitlab, etc.).Git is employed as usual to store and version code (including DVC meta-files as placeholders for data).
DVCstores data and model filesseamlessly in a cache outside of Git, while preserving almost the same user experience as if they were in the repo.
To share and back up thedata cache, DVC supports multiple remote storage platforms - any cloud (S3, Azure, Google Cloud, etc.) or on-premise network storage (via SSH, for example).DVC pipelines(computational graphs) connect code and data together.
They specify all steps required to produce a model: input dependencies including code, data, commands to run; and output information to be saved.Last but not least,DVC Experiment Versioninglets you prepare and run a large number of experiments.
Their results can be filtered and compared based on hyperparameters and metrics, and visualized with multiple plots.VS Code ExtensionTo use DVC as a GUI right from your VS Code IDE, install theDVC Extensionfrom the Marketplace.
It currently features experiment tracking and data management, and more features (data pipeline support, etc.) are coming soon!Note: You’ll have to install core DVC on your system separately (as detailed
below). The Extension will guide you if needed.InstallationThere are several ways to install DVC: in VS Code; usingsnap,choco,brew,conda,pip; or with an OS-specific package.
Full instructions areavailable here.Snapcraft (Linux)snapinstalldvc--classicThis corresponds to the latest tagged release.
Add--betafor the latest tagged release candidate, or--edgefor the latestmainversion.Chocolatey (Windows)chocoinstalldvcBrew (mac OS)brewinstalldvcAnaconda (Any platform)condainstall-cconda-forgemamba# installs much faster than condamambainstall-cconda-forgedvcDepending 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.PyPI (Python)pipinstalldvcDepending 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. Orallto include them all.
The command should look like this:pip install 'dvc[s3]'(in this case AWS S3 dependencies such asboto3will be installed automatically).To install the development version, run:pipinstallgit+git://github.com/iterative/dvcPackage (Platform-specific)Self-contained packages for Linux, Windows, and Mac are available.
The latest version of the packages can be found on the GitHubreleases page.Ubuntu / Debian (deb)sudowgethttps://dvc.org/deb/dvc.list-O/etc/apt/sources.list.d/dvc.listwget-qO-https://dvc.org/deb/iterative.asc|sudoapt-keyadd-sudoaptupdatesudoaptinstalldvcFedora / CentOS (rpm)sudowgethttps://dvc.org/rpm/dvc.repo-O/etc/yum.repos.d/dvc.reposudorpm--importhttps://dvc.org/rpm/iterative.ascsudoyumupdatesudoyuminstalldvcContributingContributions are welcome!
Please see ourContributing Guidefor more details.
Thanks to all our contributors!Community and SupportTwitterForumDiscord ChatEmailMailing ListCopyrightThis 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.CitationIterative,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.