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开源软件名称:iterative/dvc开源软件地址:https://github.com/iterative/dvc开源编程语言:Python 99.4%开源软件介绍:Website • Docs • Blog • Twitter • Chat (Community & Support) • Tutorial • Mailing List Data Version Control or DVC helps you develop reproducible machine learning projects:
Why DVC?
Contents How DVC worksWe 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.
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 startPlease read Get Started guide for a full version. Common workflow commands include:
InstallationThere are four options to install DVC: Snap (Snapcraft/Linux)snap install dvc --classic This corresponds to the latest tagged release.
Add Choco (Chocolatey/Windows)choco install dvc Brew (Homebrew/Mac OS)brew install dvc Conda (Anaconda)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)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: To install the development version, run: pip install git+git://github.com/iterative/dvc PackageSelf-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
ContributingContributions are welcome! Please see our Contributing Guide for more details. Thanks to all our contributors! Mailing ListWant 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. CopyrightThis 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. |
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