Provenance in HPE Machine Learning Data Management refers to the tracking of the dependencies and relationships between datasets, as well as the ability to go back in time and see the state of a dataset or repository at a particular moment. HPE Machine Learning Data Management models both commit provenance and branch provenance to represent the dependencies between data in the pipeline.

Commit Provenance

Commit provenance refers to the relationship between commits in different repositories. If a commit in a repository is derived from a commit in another repository, the derived commit is provenant on the source commit. Capturing this relationship supports queries regarding how data in a commit was derived.

Branch Provenance

Branch provenance represents a more general relationship between data. It asserts that future commits in the downstream branch will be derived from the head commit of the upstream branch.

Traversing Provenance

HPE Machine Learning Data Management automatically maintains a complete audit trail, allowing all results to be fully reproducible. To track the direct provenance of commits and learn where the data in the repository originates, you can use the pachctl inspect command to view provenance information, including the origin kind, direct provenance, and size of the data.

HPE Machine Learning Data Management’s DAG structure makes it easy to traverse the provenance and subvenance in any commit. All related steps in a DAG share the same global identifier, making it possible to run pachctl list commit <commitID> to get the full list of all the branches with commits created due to provenance relationships.