data-designer-engine
Generation engine for DataDesigner synthetic data generation
Decision gist · record as of 2026-08-14
Yes, if you are building synthetic data generation workflows within the NeMo Data Designer ecosystem. The package is actively maintained (3-day-old release), has low install friction, permissive licensing, and no known vulnerabilities. The 31 dependencies are a commitment; evaluate whether you need the full generation engine or a lighter alternative if you only need configuration management.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; 31 runtime dependencies including pandas, numpy, duckdb, and LLM integration libraries will be installed.
- Low friction installation with a pure-Python wheel.
- Active maintenance with a release 3 days old.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows use in commercial and proprietary projects with minimal restrictions; you must include a copy of the license and notice of modifications if you distribute the package.
last release 2026-08-11 (3 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 312,231 downloads/mo, #7,727 on PyPI
Alternatives
Verify before relying
pip install data-designer-engine
from data_designer_engine import <engine_component>
# Engine provides execution capabilities for synthetic data generation workflows- Specific API entry points and primary classes available in the engine module
- Whether the engine can be used standalone or requires additional NeMo Data Designer components beyond data-designer-config
- Performance characteristics and scalability limits for synthetic data generation tasks
What it is and what it does
data-designer-engine is the execution runtime for NVIDIA's NeMo Data Designer synthetic data generation framework. It orchestrates data transformation, generation, and LLM-based synthesis workflows to create synthetic datasets. The engine depends on data-designer-config for configuration management and integrates with a large ecosystem of data science and HTTP libraries including pandas, numpy, duckdb, and huggingface-hub for model access.
The package is designed for developers and researchers building synthetic data pipelines. It handles the computational heavy lifting of data generation, including native HTTP-based LLM integration via httpx and tiktoken for tokenization. With 31 runtime dependencies covering data processing, networking, cryptography, and schema validation, it represents a complete generation stack rather than a lightweight utility.
Use it for
- Generate synthetic datasets for machine learning model training when real data is scarce or sensitive
- Create diverse test data for data pipeline validation and quality assurance workflows
- Build data augmentation pipelines using LLM-based generation for NLP and structured data tasks
- Transform and synthesize data across multiple formats using the integrated data processing stack
- Prototype data workflows with configurable generation parameters via data-designer-config
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building synthetic data generation workflows within the NeMo Data Designer ecosystem.
The package is actively maintained (3-day-old release), has low install friction, permissive licensing, and no known vulnerabilities. The 31 dependencies are a commitment; evaluate whether you need the full generation engine or a lighter alternative if you only need configuration management.
Install
data-designer-engine on PyPI
Before you install
Low friction installation with a pure-Python wheel. Active maintenance with a release 3 days old. The 31 runtime dependencies are substantial but include standard data science and HTTP libraries, suggesting a mature ecosystem integration rather than experimental tooling.
Requires Python 3.10 or later; 31 runtime dependencies including pandas, numpy, duckdb, and LLM integration libraries will be installed.
License in practice
Apache-2.0 permissive license allows use in commercial and proprietary projects with minimal restrictions; you must include a copy of the license and notice of modifications if you distribute the package.
Quickstart
pip install data-designer-engine
from data_designer_engine import <engine_component>
# Engine provides execution capabilities for synthetic data generation workflowsVerify before relying
- Specific API entry points and primary classes available in the engine module
- Whether the engine can be used standalone or requires additional NeMo Data Designer components beyond data-designer-config
- Performance characteristics and scalability limits for synthetic data generation tasks
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 31 packagesanyasciiasciichartpychardetcryptographydata-designer-configduckdbfakerfsspechttpx-retrieshttpxhuggingface-hubjinja2json-repairjsonpath-rust-bindingsjsonschemalxmlmarkomcpnetworkxnumpypandaspyarrowpydanticpyjwtpython-multipartruffscipysqlfluffstarlettetiktoken |
| Maintenance | Actively maintained 3 days since the last release |
| First released | |
| Downloads | 312,231 / month, #7,727 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_engine-0.9.1-py3-none-any.whl
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See also data-designer · data-designer-config · nemoguardrails · nemo-evaluator · nemo-gym · nemo-toolkit · deepecho · llama-index-question-gen-openai · nvdlfw-inspect · nvidia-lm-eval