Using 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.yaml
pipelines files define stages that form the pipeline(s) of a
project. All stage-based features such as dvc params
, dvc metrics
, and
dvc plots
are specified here..dvc
files ("dot DVC files") are placeholders to track data files and
directories..dvcignore
files (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).