Data Designer Configuration

DataDesignerConfig is the main configuration object for builder datasets with Data Designer. It is a declarative configuration for defining the dataset you want to generate column-by-column, including options for dataset post-processing, validation, and profiling.

Generally, you should use the DataDesignerConfigBuilder to build your configuration, but you can also build it manually by instantiating the DataDesignerConfig class directly.

Classes:

Name	Description
DataDesignerConfig	Configuration for NeMo Data Designer.

DataDesignerConfig

Bases: ExportableConfigBase

Configuration for NeMo Data Designer.

This class defines the main configuration structure for NeMo Data Designer, which orchestrates the generation of synthetic data.

Attributes:

Name	Type	Description
columns	list[Annotated[ColumnConfigT, Field(discriminator='column_type')]]	Required list of column configurations defining how each column should be generated. Must contain at least one column.
model_configs	Optional[list[ModelConfig]]	Optional list of model configurations for LLM-based generation. Each model config defines the model, provider, and inference parameters.
seed_config	Optional[SeedConfig]	Optional seed dataset settings to use for generation.
constraints	Optional[list[ColumnConstraintT]]	Optional list of column constraints.
profilers	Optional[list[ColumnProfilerConfigT]]	Optional list of column profilers for analyzing generated data characteristics.