---
id: data-designer-config
version: "0.9.1"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# data-designer-config — Configuration layer for DataDesigner synthetic data generation

License: permissive · Maintenance: active · Downloads: 312.4K/mo

## What it is and what it does

data-designer-config is a configuration layer for NVIDIA's NeMo Data Designer synthetic data generation framework. It provides a builder-pattern API for constructing data generation pipelines declaratively, letting you define model configurations, inference parameters, and column generation rules (both sampled and LLM-based). The package is lightweight and can be used standalone for configuration management, though it is primarily intended as a dependency of the larger Data Designer framework.

The package depends on common data and ML libraries (pandas, numpy, pydantic, requests, httpx, jinja2, pillow, pyarrow, yaml, and others) to support configuration serialization, validation, and rendering. It targets Python 3.10 and later, is actively maintained, and carries no known security vulnerabilities.

Use it for:

- Define synthetic data generation pipelines with multiple LLM models and custom inference parameters for experimentation.
- Configure column samplers (UUID, categorical, numeric) and LLM-based text generation for structured dataset creation.
- Build and serialize data generation configurations as reusable pipeline definitions.
- Integrate configuration management into a larger NeMo Data Designer workflow for reproducible synthetic data generation.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Provides a configuration API for building synthetic data generation pipelines in the NeMo Data Designer framework, allowing you to define data sources, LLM models, and column generation rules.

Yes, if you are building or using the NeMo Data Designer framework. The low install friction, active maintenance, permissive license, and zero known vulnerabilities make it a safe dependency. If you need standalone configuration management for synthetic data pipelines, verify first that the package's feature set meets your needs independently of the full framework.

## Install

pip install data-designer-config
uv add data-designer-config
poetry add data-designer-config

## Installing data-designer-config

Before you install: Low install friction with a pure-Python wheel distribution. Actively maintained with a recent release. Requires Python 3.10 or later and pulls in 14 runtime dependencies including common data/ML libraries (pandas, numpy, pydantic, requests).

License in practice: Apache-2.0 permissive license allows commercial and private use with minimal restrictions; you must include a copy of the license and state significant changes.

Quickstart:

pip install data-designer-config

import data_designer.config as dd

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),
        ),
    ]
)
config = config_builder.build()

Requires Python 3.10 or later; designed as a configuration layer for the NeMo Data Designer framework.

Verify before relying:

- Whether this package can be used fully standalone or requires the full NeMo Data Designer framework to be installed.
- Whether the 14 runtime dependencies are all strictly required or if some are optional for specific use cases.

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 312.4K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

synthetic data generation config, NeMo data designer configuration, LLM pipeline configuration, data generation framework, column sampler configuration, model inference parameters, synthetic-data, configuration-framework, nvidia-nemo

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