> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/nemo/datadesigner/llms.txt.
> For full documentation content, see https://docs.nvidia.com/nemo/datadesigner/llms-full.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nemo/datadesigner/_mcp/server.

# data\_designer.config.data\_designer\_config

## Module Contents

### Classes

| Name                                                                               | Description                           |
| ---------------------------------------------------------------------------------- | ------------------------------------- |
| [`DataDesignerConfig`](#data_designerconfigdata_designer_configdatadesignerconfig) | Configuration for NeMo Data Designer. |

### API

```python
class data_designer.config.data_designer_config.DataDesignerConfig(
    /,
    **data: typing.Any
)
```

**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:**

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

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

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

Optional seed dataset settings to use for generation.

Optional list of column constraints.

Optional list of column profilers for analyzing generated data characteristics.

Optional list of processor configurations for post-generation transformations.

**Attributes:**

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

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

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

Optional seed dataset settings to use for generation.

Optional list of column constraints.

Optional list of column profilers for analyzing generated data characteristics.

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.

```python
columns: list[typing.Annotated[data_designer.config.column_types.ColumnConfigT, Field(discriminator='column_type')]] = Field(...)
```

```python
model_configs: list[data_designer.config.models.ModelConfig] | None
```

```python
tool_configs: list[data_designer.config.mcp.ToolConfig] | None
```

```python
seed_config: data_designer.config.seed.SeedConfig | None
```

```python
constraints: list[data_designer.config.sampler_constraints.ColumnConstraintInputT] | None
```

```python
profilers: list[data_designer.config.analysis.column_profilers.ColumnProfilerConfigT] | None
```

```python
processors: list[typing.Annotated[data_designer.config.processor_types.ProcessorConfigT, Field(discriminator='processor_type')]] | None
```

```python
_validate_subcategory_parents() -> typing_extensions.Self
```

```python
fingerprint() -> 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`.