data-designer-config
Configuration layer for DataDesigner synthetic data generation
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; designed as a configuration layer for the NeMo Data Designer framework.
- Low install friction with a pure-Python wheel distribution.
- Actively maintained with a recent release.
License · maintenance · safety
Apache-2.0 (permissive) — 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.
last release 2026-08-11 (3 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 312,393 downloads/mo, #7,724 on PyPI
Alternatives
Verify before relying
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()- 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.
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 on this page. Verify before relying on it.
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
data-designer-config on PyPI
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).
Requires Python 3.10 or later; designed as a configuration layer for the NeMo Data Designer framework.
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()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 the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 14 packageshttpxidnajinja2numpypandaspillowpyarrowpydanticpygmentspython-json-loggerpyyamlrequestsrichurllib3 |
| Maintenance | Actively maintained 3 days since the last release |
| First released | |
| Downloads | 312,393 / month, #7,724 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: data_designer_config-0.9.1-py3-none-any.whl
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See also data-designer · data-designer-engine · nemo-toolkit · nemo-evaluator · nemo-gym · nvidia-nat-atif · nvidia-nat-core · nemoguardrails · sdv · dlt-meta