Data Version Control is a new type of data versioning, workflow, and experiment management software, that builds upon Git (although it can work stand-alone). DVC reduces the gap between established engineering tool sets and data science needs, allowing users to take advantage of new features while reusing existing skills and intuition.
DVC codifies data and ML experiments
Data science experiment sharing and collaboration can be done through a regular Git flow (commits, branching, pull requests, etc.), the same way it works for software engineers. Using Git and DVC, data science and machine learning teams can version experiments, manage large datasets, and make projects reproducible.
Easy to use: DVC is quick to install and doesn't require special infrastructure, nor does it depend on APIs or external services. It's a stand-alone CLI tool.
Git servers, as well as SSH and cloud storage providers are supported, however.
DVC files such as
.dvc files serve as placeholders to track
large data files and directories for versioning (among other
purposes). These metafiles change along
with your data, and you can use Git to place them under
as a proxy to the actual data versions, which are stored in the DVC
cache (outside of Git). This does not replace features of Git.
DVC does, however, provide several commands similar to Git such as
dvc checkout, or
dvc push, which interact with the underlying Git
repo (if one is being used, which is not required).