arXiv AI
Sep 17

NeMo Data Designer: An Extensible Framework for Multimodal Synthetic Data Generation

NeMo Data Designer (NDD) is an open‑source framework for generating multimodal synthetic data. It uses a declarative configuration format that lets users define dataset columns—text, code, structured outputs, images, embeddings, and statistical samplers—to steer diversity. The system supports a preview‑and‑revision workflow, dependency resolution, and retry logic, and can be extended via plugins. Case studies demonstrate its use for structured, agentic, multimodal, and domain‑specialized tasks, including datasets for Nemotron model development and enterprise deployments.

By Johnny Greco, Nabin Mulepati, Andre Manoel, Eric Tramel, Kirit Thadaka, Mike Knepper, Dhruv Nathawani, Dane Corneil, Yev Meyer, Alex Watson, Maarten Van Segbroeck