usage: dvc dag [-h] [-q | -v] [--dot] [--full] [target] positional arguments: target Stage or output to show pipeline for (optional) Uses all stages in the workspace by default.
A data pipeline, in general, is a series of data processing stages (for example, console commands that take an input and produce an output). A pipeline may produce intermediate data, and has a final result.
Data science and machine learning pipelines typically start with large raw datasets, include intermediate featurization and training stages, and produce a final model, as well as accuracy metrics.
In DVC, pipeline stages and commands, their data I/O, interdependencies, and
results (intermediate or final) are specified in
dvc.yaml, which can be
written manually or built using the helper command
dvc run. This allows DVC to
restore one or more pipelines later (see
DVC builds a dependency graph (DAG) to do this.
dvc dag command displays the stages of a pipeline up to the target stage. If
target is omitted, it will show the full project DAG.
--full- show full DAG that the
targetstage belongs to, instead of showing only its ancestors.
--dot- show DAG in DOT format. It can be passed to third party visualization utilities.
--help- prints the usage/help message, and exit.
--quiet- do not write anything to standard output. Exit with 0 if no problems arise, otherwise 1.
--verbose- displays detailed tracing information.
This command's output is automatically piped to
Less, if available in the
terminal. (The exact command used is
less --chop-long-lines --clear-screen.)
less is not available (e.g. on Windows), the output is simply printed out.
It's also possible to enable Less paging on Windows.
It's possible to override the default pager via the
variable. For example, the following command will replace the default pager with
more, for a single run:
$ DVC_PAGER=more dvc dag
For a persistent change, define
DVC_PAGER in the shell configuration. For
example in Bash, we could add the following line to
Visualize the prepare, featurize, train, and evaluate stages of a pipeline as
$ dvc dag +---------+ | prepare | +---------+ * * * +-----------+ | featurize | +-----------+ ** ** ** * * ** +-------+ * | train | ** +-------+ * ** ** ** ** * * +----------+ | evaluate | +----------+