dvc init in your workspace will start a DVC
project, including the internal
.dvc/ directory. From there on, you
will create and manage different DVC files and populate the cache
as you use DVC and work on your data science experiments.
dvc.yamlpipelines files define stages that form the pipeline(s) of a project. All stage-based features such as
dvc metrics, and
dvc plotsare specified here.
.dvcfiles ("dot DVC files") are placeholders to track data files and directories.
.dvcignorefiles (optional) contain a list of paths for DVC to ignore, which can dramatically increase its operational performance.
.dvc/contains the local configuration file(s), default local cache location, and other utilities that DVC needs to operate.
These metafiles should be versioned with Git (in Git-enabled repositories).