create_derived_dataset

nominal.experimental.compute_as_code.create_derived_dataset(client, name, spec, *, message='Initial derived definition', description=None, labels=(), properties=None, markings=None)

Create a derived dataset defined by a nominal.compute graph.

A derived dataset is a regular dataset whose contents are computed from a nominal.compute graph (spec) instead of ingested files. It is returned as a DerivedDataset, which is not a Dataset: look it up with NominalClient.get_dataset to read its data or attach it to an asset.

For the common case of a union of tag-filtered input datasets, nominal.experimental.derived_datasets.create_derived_dataset expresses the same thing without the compute extra, and its inputs can be edited afterwards.

Parameters:

• client (NominalClient)

  The NominalClient to use for creating the derived dataset.

• name (str)

  Name of the derived dataset to create.

• spec (Dataset)

  nominal.compute graph defining how the dataset’s contents are computed.

• message (str)

  Commit message for the initial derived definition.

• description (str | None)

  Human readable description of the dataset.

• labels (Sequence[str])

  Text labels to apply to the created dataset.

• properties (Mapping[str, str | float] | None)

  String or numeric properties for the created derived dataset. Integers are stored as floats.

• markings (Sequence[Marking | str] | None)

  If present, markings (or marking RIDs) applied to the dataset. Sent as part of the creation request rather than applied in a follow-up call.

Returns:

Reference to the created derived dataset in Nominal.

Raises:

TypeError – If a property value is a boolean or is not a string, integer, or float.

Return type:

DerivedDataset