DataDesigner/packages/data-designer-config
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#	packages/data-designer-config/tests/config/test_run_config.py
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pyproject.toml chore: bump pillow and python-multipart for CVEs, add SECURITY.md (#564) 2026-04-20 18:36:22 -04:00
README.md feat(models): deprecate implicit default provider routing (#594) 2026-05-05 13:39:12 -06:00

data-designer-config

Configuration layer for NeMo Data Designer synthetic data generation framework.

This package provides the configuration API for defining synthetic data generation pipelines. It's a lightweight dependency that can be used standalone for configuration management.

Installation

pip install data-designer-config

Usage

import data_designer.config as dd

# Initialize config builder with model config(s)
config_builder = dd.DataDesignerConfigBuilder(
    model_configs=[
        dd.ModelConfig(
            alias="my-model",
            model="nvidia/nemotron-3-nano-30b-a3b",
            provider="nvidia",
            inference_parameters=dd.ChatCompletionInferenceParams(temperature=0.7),
        ),
    ]
)

# Add columns
config_builder.add_column(
    dd.SamplerColumnConfig(
        name="user_id",
        sampler_type=dd.SamplerType.UUID,
        params=dd.UUIDSamplerParams(prefix="user-"),
    )
)
config_builder.add_column(
    dd.LLMTextColumnConfig(
        name="description",
        prompt="Write a product description",
        model_alias="my-model",
    )
)

# Build configuration
config = config_builder.build()

See main README.md for more information.