Skip to content

Data Designer

Data Designer on NeMo Platform enables high-quality synthetic data generation through the NeMo Data Designer plugin. You can execute workloads locally from the CLI, submit them to a running NeMo Services cluster, or call the Data Designer API from the SDK.

Overview

Data Designer is a framework for orchestrating complex synthetic data generation workflows. It coordinates LLM calls, manages dependencies between data fields, handles batching and parallelization, and validates generated data against specifications.

The plugin is built on the open-source NVIDIA NeMo Data Designer library (GitHub). The library provides the configuration and generation engine; the plugin provides CLI, SDK, Data Designer API, Jobs, Files API, Secrets API, and Inference Gateway API integration.

How It Works

Data Designer separates configuration from execution.

Note

The code snippets below are for conceptual demonstration purposes only. For runnable examples, see the tutorials.

1. Build Configurations

Use data_designer.config to define the dataset you want to generate:

import data_designer.config as dd

# Define models
model_configs = [
    dd.ModelConfig(
        provider="default/nvidia-build",
        model="nvidia/nemotron-3-nano-30b-a3b",
        alias="text",
    )
]

# Build configuration
config_builder = dd.DataDesignerConfigBuilder(model_configs)
config_builder.add_column(dd.SamplerColumnConfig(...))
config_builder.add_column(dd.LLMTextColumnConfig(...))

Configuration code describes the dataset schema, columns, dependencies, constraints, seed data, processors, profilers, and inference settings.

Learn more: See the library documentation for comprehensive guides on column types, samplers, constraints, and advanced features.

2. Choose Where to Execute

The same configuration can run through different plugin surfaces:

Interface Execution location NeMo Services required? Best for
nemo data-designer ... run Local CLI process Optional Fast local iteration, local files, library-equivalent workload behavior.
nemo data-designer ... submit Data Designer API or Jobs worker Yes Service-managed execution, logs, artifacts, and shared resources.
client.data_designer.preview/create Data Designer API or Jobs worker Yes Application code that calls Data Designer programmatically.

run versus submit primarily controls where the plugin workload execution happens. A local run can be fully local, but it is not an offline-only mode: it can still use the Files API, Secrets API, and Inference Gateway API from a running NeMo Services cluster when the configuration references the corresponding resources.

See Execution Modes for the full model.

NeMo Services Integration

When you use CLI submit, SDK execution, or NeMo resources from a local run, the plugin integrates with these NeMo Services APIs:

Integration What it provides
Inference Gateway API Centralized model providers and OpenAI-compatible inference routes.
Files API Filesets for seed data and persona datasets.
Secrets API API keys and tokens referenced from Data Designer configurations.
Jobs API Service-managed create workloads, logs, status, and artifacts.

These integrations are required for submit and SDK execution. They are optional for CLI run execution, depending on the resources your configuration references.

Next Steps

  • Execution Modes

    Understand local execution, NeMo Services execution, and NeMo resources.

  • CLI

    Run previews and create datasets with nemo data-designer.

  • Tutorials

    Learn through examples: basics, seeding, and more.

  • Migration Guide

    Move configurations between local CLI and NeMo Services execution.

  • Library Documentation

    Comprehensive guides on column types, constraints, and advanced features.