Content
# 🎨 NeMo Data Designer
[](https://github.com/NVIDIA-NeMo/DataDesigner/actions/workflows/ci.yml)
[](https://opensource.org/licenses/Apache-2.0)
[](https://www.python.org/downloads/) [](https://docs.nvidia.com/nemo/microservices/latest/index.html) [](https://nvidia-nemo.github.io/DataDesigner/) 
**Generate high-quality synthetic datasets from scratch or using your own seed data.**
---
## Welcome!
Data Designer helps you create synthetic datasets that go beyond simple LLM prompting. Whether you need diverse statistical distributions, meaningful correlations between fields, or validated high-quality outputs, Data Designer provides a flexible framework for building production-grade synthetic data.
## What can you do with Data Designer?
- **Generate diverse data** using statistical samplers, LLMs, or existing seed datasets
- **Control relationships** between fields with dependency-aware generation
- **Validate quality** with built-in Python, SQL, and custom local and remote validators
- **Score outputs** using LLM-as-a-judge for quality assessment
- **Iterate quickly** with preview mode before full-scale generation
---
## Quick Start
### 1. Install
```bash
pip install data-designer
```
Or install from source:
```bash
git clone https://github.com/NVIDIA-NeMo/DataDesigner.git
cd DataDesigner
make install
```
### 2. Set your API key
Start with one of our default model providers:
- [NVIDIA Build API](https://build.nvidia.com)
- [OpenAI](https://platform.openai.com/api-keys)
- [OpenRouter](https://openrouter.ai)
Grab your API key(s) using the above links and set one or more of the following environment variables:
```bash
export NVIDIA_API_KEY="your-api-key-here"
export OPENAI_API_KEY="your-openai-api-key-here"
export OPENROUTER_API_KEY="your-openrouter-api-key-here"
```
### 3. Start generating data!
```python
import data_designer.config as dd
from data_designer.interface import DataDesigner
# Initialize with default settings
data_designer = DataDesigner()
config_builder = dd.DataDesignerConfigBuilder()
# Add a product category
config_builder.add_column(
dd.SamplerColumnConfig(
name="product_category",
sampler_type=dd.SamplerType.CATEGORY,
params=dd.CategorySamplerParams(
values=["Electronics", "Clothing", "Home & Kitchen", "Books"],
),
)
)
# Generate personalized customer reviews
config_builder.add_column(
dd.LLMTextColumnConfig(
name="review",
model_alias="nvidia-text",
prompt="Write a brief product review for a {{ product_category }} item you recently purchased.",
)
)
# Preview your dataset
preview = data_designer.preview(config_builder=config_builder)
preview.display_sample_record()
```
---
## What's next?
### 📚 Learn more
- **[Getting Started](https://nvidia-nemo.github.io/DataDesigner/latest/)** – Install, configure, and generate your first dataset
- **[Tutorial Notebooks](https://nvidia-nemo.github.io/DataDesigner/latest/notebooks/)** – Step-by-step interactive tutorials
- **[Column Types](https://nvidia-nemo.github.io/DataDesigner/latest/concepts/columns/)** – Explore samplers, LLM columns, validators, and more
- **[Validators](https://nvidia-nemo.github.io/DataDesigner/latest/concepts/validators/)** – Learn how to validate generated data with Python, SQL, and remote validators
- **[Model Configuration](https://nvidia-nemo.github.io/DataDesigner/latest/concepts/models/model-configs/)** – Configure custom models and providers
- **[Person Sampling](https://nvidia-nemo.github.io/DataDesigner/latest/concepts/person_sampling/)** – Learn how to sample realistic person data with demographic attributes
### 🔧 Configure models via CLI
```bash
data-designer config providers # Configure model providers
data-designer config models # Set up your model configurations
data-designer config list # View current settings
```
### 🤝 Get involved
- **[Contributing Guide](https://nvidia-nemo.github.io/DataDesigner/latest/CONTRIBUTING)** – Help improve Data Designer
- **[GitHub Issues](https://github.com/NVIDIA-NeMo/DataDesigner/issues)** – Report bugs or make a feature request
---
## Telemetry
Data Designer collects telemetry to help us improve the library for developers. We collect:
* The names of models used
* The count of input tokens
* The count of output tokens
**No user or device information is collected.** This data is not used to track any individual user behavior. It is used to see an aggregation of which models are the most popular for SDG. We will share this usage data with the community.
Specifically, a model name that is defined a `ModelConfig` object, is what will be collected. In the below example config:
```python
ModelConfig(
alias="nv-reasoning",
model="openai/gpt-oss-20b",
provider="nvidia",
inference_parameters=ChatCompletionInferenceParams(
temperature=0.3,
top_p=0.9,
max_tokens=4096,
),
)
```
The value `openai/gpt-oss-20b` would be collected.
To disable telemetry capture, set `NEMO_TELEMETRY_ENABLED=false`.
### Top Models
This chart represents the breakdown of models used for Data Designer across all synthetic data generation jobs from 1/24/2026 to 2/24/2026.
_Last updated on 2/24/2026_
---
## License
Apache License 2.0 – see [LICENSE](LICENSE) for details.
---
## Citation
If you use NeMo Data Designer in your research, please cite it using the following BibTeX entry:
```bibtex
@misc{nemo-data-designer,
author = {The NeMo Data Designer Team, NVIDIA},
title = {NeMo Data Designer: A framework for generating synthetic data from scratch or based on your own seed data},
howpublished = {\url{https://github.com/NVIDIA-NeMo/DataDesigner}},
year = {2025},
note = {GitHub Repository},
}
```
