For AI agents: a documentation index is available at the root level at /llms.txt and /llms-full.txt. Append /llms.txt to any URL for a page-level index, or .md for the markdown version of any page.
  • Getting Started
    • Welcome
    • Contributing
  • Concepts
    • Columns
    • Seed Datasets
    • Agent Rollout Ingestion
    • Custom Columns
    • Validators
    • Processors
    • Person Sampling
    • Traces
    • Architecture & Performance
    • Deployment Options
    • Security
  • Tutorials
    • Overview
    • The Basics
    • Structured Outputs, Jinja Expressions, and Conditional Generation
    • Seeding with an External Dataset
    • Providing Images as Context
    • Generating Images
    • Image-to-Image Editing
  • Recipes
    • Recipe Cards
  • Plugins
    • Overview
    • Example Plugin
    • FileSystemSeedReader Plugins
    • Discover
  • Code Reference
    • Overview
      • Overview
      • models
      • mcp
      • column_configs
      • config_builder
      • data_designer_config
      • run_config
      • sampler_params
      • validator_params
      • seeds
      • processors
      • analysis
      • Config API
        • Analysis
        • Base
        • Column Configs
        • Column Types
        • Config Builder
        • Custom Column
        • Data Designer Config
        • Dataset Metadata
        • Default Model Settings
        • Errors
        • Exportable Config
        • Fingerprint
        • Interface
        • Mcp
        • Models
        • Preview Results
        • Processor Types
        • Processors
        • Run Config
        • Sampler Constraints
        • Sampler Params
        • Seed
        • Seed Source
        • Seed Source Dataframe
        • Seed Source Types
        • Testing
        • Utils
        • Validator Params
        • Version
  • Dev Notes
    • Overview
    • Have It Your Way
    • VLM Long Document Understanding
    • Push Datasets to Hugging Face Hub
    • Text-to-SQL for Nemotron Super
    • Async All the Way Down
    • Owning the Model Stack
NVIDIANVIDIA
Developer-friendly docs for your API
Privacy Policy | Your Privacy Choices | Terms of Service | Accessibility | Corporate Policies | Product Security | Contact

Copyright © 2026, NVIDIA Corporation.

LogoLogoNeMo Data Designer
On this page
  • Module Contents
  • Classes
  • API
Code ReferenceConfigConfig API

data_designer.config.data_designer_config

||View as Markdown|
Previous

Custom Column

Next

Dataset Metadata

Module Contents

Classes

NameDescription
DataDesignerConfigConfiguration for NeMo Data Designer.

API

1class data_designer.config.data_designer_config.DataDesignerConfig(
2    /,
3    **data: typing.Any
4)

Bases: data_designer.config.exportable_config.ExportableConfigBase

Configuration for NeMo Data Designer.

This class defines the main configuration structure for NeMo Data Designer, which the engine consumes when generating synthetic data.

Parameters:

columns

Required list of column configurations defining how each column should be generated. Must contain at least one column.

model_configs

Optional list of model configurations for LLM-based generation. Each model config defines the model, provider, and inference parameters.

tool_configs

Optional list of tool configurations for MCP tool calling. Each tool config defines the provider, allowed tools, and execution limits.

seed_config

Optional seed dataset settings to use for generation.

constraints

Optional list of column constraints.

profilers

Optional list of column profilers for analyzing generated data characteristics.

processors

Optional list of processor configurations for post-generation transformations.

Attributes:

columns

Required list of column configurations defining how each column should be generated. Must contain at least one column.

model_configs

Optional list of model configurations for LLM-based generation. Each model config defines the model, provider, and inference parameters.

tool_configs

Optional list of tool configurations for MCP tool calling. Each tool config defines the provider, allowed tools, and execution limits.

seed_config

Optional seed dataset settings to use for generation.

constraints

Optional list of column constraints.

profilers

Optional list of column profilers for analyzing generated data characteristics.

processors

Optional list of processor configurations for post-generation transformations.

Initialization:

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

1columns: list[typing.Annotated[data_designer.config.column_types.ColumnConfigT, Field(discriminator='column_type')]] = Field(...)
1model_configs: list[data_designer.config.models.ModelConfig] | None
1tool_configs: list[data_designer.config.mcp.ToolConfig] | None
1seed_config: data_designer.config.seed.SeedConfig | None
1constraints: list[data_designer.config.sampler_constraints.ColumnConstraintInputT] | None
1profilers: list[data_designer.config.analysis.column_profilers.ColumnProfilerConfigT] | None
1processors: list[typing.Annotated[data_designer.config.processor_types.ProcessorConfigT, Field(discriminator='processor_type')]] | None
1_validate_subcategory_parents() -> typing_extensions.Self
1fingerprint() -> dict[str, str | int]

Compute a deterministic content-addressable fingerprint of this config.

See data_designer.config.fingerprint.fingerprint_config for the full list of identity-relevant and excluded fields, and how custom column generators are identified.

Returns:

dict[str, str | int]

A dict with config_hash, config_hash_algo, and config_hash_version.