Tutorials

These tutorials demonstrate how to build Data Designer configurations and execute them through the NeMo Data Designer plugin.

Configuration and Execution

Data Designer separates configuration (building dataset schemas) from execution (generating the data).

Part 1: Build Configs (Library)

Use data_designer.config to define your dataset. See the library documentation for comprehensive guides on column types, constraints, and processors.

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import data_designer.config as dd
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config_builder = dd.DataDesignerConfigBuilder(model_configs)
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config_builder.add_column(dd.SamplerColumnConfig(...))
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config_builder.add_column(dd.LLMTextColumnConfig(...))

Part 2: Execute (Plugin)

Run the configuration locally with the CLI, submit it to NeMo Services, or call the Data Designer API from the SDK:

$
nemo data-designer preview run product_reviews.py --num-records 5
$
nemo data-designer create submit product_reviews.py --workspace default --num-records 30

SDK execution uses the Data Designer API today:

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import os
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from nemo_platform import NeMoPlatform
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client = NeMoPlatform(
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    base_url=os.environ.get("NMP_BASE_URL", "http://localhost:8080"),
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    workspace="default",
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)
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data_designer = client.data_designer
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preview = data_designer.preview(config_builder)
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job = data_designer.create(config_builder, num_records=1000)

Execution-Specific Considerations

When running through the plugin, supported resources depend on the execution mode:

Prerequisites

These tutorials use an Inference Gateway provider for model calls, so a NeMo Services cluster must be running before you preview or create data — including with local CLI run (see Execution Modes for more about this distinction). Complete Setup to ensure you have the NeMo Services running locally and an inference provider available. These tutorials reference the default NVIDIA Build model provider, which is created as default/nvidia-build during setup.

Tutorials