🎨 NeMo Data Designer

Generate high-quality synthetic datasets from scratch or using your own seed data.

📄 Read the technical report — Architecture, programming model, and case studies for multimodal synthetic data generation.

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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 text, structured data, and images using samplers, LLMs, image models, or existing seed datasets
• Build multimodal workflows with image, audio, and video context
• Connect generation to external tools through local or remote MCP servers and capture interaction traces
• Control relationships between fields with dependency-aware generation
• Validate and score outputs with Python, SQL, custom validators, and LLM judges
• Extend Data Designer with plugins for specialized data sources, generation capabilities, and processing steps
• Preview, resume, and monitor generation from small experiments to large runs

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Quick Start

1. Install

pip install data-designer

[!NOTE] To apply maintained minimum dependency versions for known vulnerabilities, use the optional security constraints file:

pip install data-designer -c "https://raw.githubusercontent.com/NVIDIA-NeMo/DataDesigner/main/constraints.txt"

With uv, use uv pip install or uv add with the same -c URL. See dependency security for details.

[!NOTE] This project will download and install additional third-party open source software projects. Review the license terms of these open source projects before use.

Or install from source:

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
• OpenAI
• OpenRouter

Grab your API key(s) using the above links and set one or more of the following environment variables:

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!

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()

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Do more with Data Designer plugins

Plugins extend the data sources, generation capabilities, and processing steps available in your Data Designer workflows. The DataDesignerPlugins repository contains optional NVIDIA-maintained packages that add these capabilities.

What you want to do	Plugin package
Use files from GitHub or local Git repositories as seed data	GitHub seed reader
Generate question-answer data for retriever training, with document chunking, embedding-based deduplication, and export to Automodel-compatible training formats	Retrieval SDG toolkit

Data Designer provides the generation framework. Plugin packages add specialized capabilities to your existing workflow and can also provide ready-made workflows with their own CLI.

After installing Data Designer, browse and install compatible packages from the default NVIDIA catalog:

data-designer plugin list
data-designer plugin info github
data-designer plugin install github
data-designer plugin installed

The NVIDIA catalog is configured by default. Install plugin packages in the same Python environment as Data Designer. Their extensions are discovered automatically after installation; follow each package’s usage guide to configure them in your workflow.

This is the beginning of the README. Read the rest at the original. Read the full README ↗