AI - DVC

About DVC

DVC is a versatile system for managing and versioning data, developed by Iterative Data Science team. It allows users to track data changes, connect code and data, and facilitate collaboration in data science projects. DVC supports various package managers such as Snap, Choco, Brew, Conda, PyPI, and can be installed using the provided package managers or deb/rpm packages. The system is open-source and distributed under the Apache license version 2.0.

DVC provides a number of features that make data management more efficient and effective for data scientists. It enables tracking changes to data files, including images, text files, and even directories. DVC also supports connecting code and data by allowing users to define dependencies between datasets and code commits, ensuring reproducibility and ease of collaboration. With built-in support for various version control systems such as Git and Mercurial, DVC simplifies the process of managing both code and data together.

Notable achievements include a significant contributor community, with over 120 contributors to the project, as well as an active forum and Discord chat for community support. DVC has also been adopted by various organizations, including NASA's Jet Propulsion Laboratory, and has seen extensive use in fields such as machine learning, data engineering, and scientific research.

To get started with using DVC, users can install it using their preferred package manager or operating system packages. For example, on Ubuntu, users can add the DVC repository to their sources list and install the software using apt. On CentOS/Fedora, similar steps are required to use yum instead of apt. Once installed, users can start managing data and code changes with commands like 'dvc add' and 'dvc commit'.