Data Designer SDK Resources | NVIDIA NeMo Platform

The data_designer.config module provides a consistent, context-agnostic experience for building Data Designer configs. Once you are ready to execute that config through NeMo Services APIs, you use objects from the nemo_platform SDK. This page explains the SDK objects used for Data Designer API execution.

DataDesignerResource

The DataDesignerResource is the initial SDK object for working with Data Designer through the SDK. It provides Data Designer API preview and create operations for Data Designer configurations.

A DataDesignerResource is accessed directly from a NeMoPlatform instance:

import os
from nemo_platform import NeMoPlatform
client = NeMoPlatform(
    base_url=os.environ.get("NMP_BASE_URL", "http://localhost:8080"),
    workspace="default",
)
data_designer = client.data_designer  # this object is a DataDesignerResource

The DataDesignerResource is primarily used to make Data Designer API preview requests (preview) and create jobs (create), but exposes some additional useful methods:

Method	Description
`validate(config_builder, *, workspace=None)`	Checks a configuration and returns a `ValidationReport` (see below).
`get_default_model_providers()`	Returns a list of model providers registered with the Models API and Inference Gateway API that can be used in your Data Designer config.
`get_job_resource(job_name: str)`	Returns a `DataDesignerJobResource` for interacting with a job (see below).

Validating a config

validate() runs the same checks preview() and create() perform internally, but never short-circuits — every detectable problem appears in one report. Use it to catch configuration errors before submitting work.

import data_designer.config as dd
config_builder = dd.DataDesignerConfigBuilder(
    model_configs=[
        dd.ModelConfig(
            provider="default/nvidia-build",
            model="nvidia/nemotron-3-nano-30b-a3b",
            alias="text",
        )
    ],
)
# Add columns, constraints, seed datasets, processors, and profilers here.
report = client.data_designer.validate(config_builder)
if not report.ok:
    for error in report.errors:
        print(error.message)

The workspace keyword argument selects the workspace used to resolve provider references and seed sources. It falls back to the platform client’s workspace, then to "default".

report = client.data_designer.validate(config_builder, workspace="my-workspace")

ValidationReport

validate() returns a ValidationReport describing what the checks found.

Attribute	Description
`ok`	`True` when no errors were found.
`errors`	A list of `ValidationError` objects, each with a `message` string. Empty when `ok` is `True`.
`config_source`	The configuration source path when validation ran through the CLI. `None` for SDK calls.

DataDesignerJobResource

The DataDesignerJobResource provides several helper methods for working with a job. It is returned by the DataDesignerResource.create() method when you create a job; you can also use DataDesignerResource.get_job_resource() to get an instance of this object for an existing job.

Some of the most useful methods are described below.

Method	Description
`wait_until_done()`	Polls the job service until the job reaches a terminal state. Prints job logs along the way.
`get_logs()`	Returns logs from the job as a list of dicts. Handles pagination automatically.
`download_artifacts()`	Downloads the job results as a tar archive. Returns a `DataDesignerJobResults` object (see below).

DataDesignerJobResults

The DataDesignerJobResults object simplifies loading downloaded job results into memory.

Method	Description
`load_analysis()`	Returns a `DatasetProfilerResults` object (from the library) with an analysis of the dataset.
`load_dataset()`	Returns the output dataset as a Pandas DataFrame.
`load_processor_dataset(processor_name: str)`	Returns the named processor dataset as a Pandas DataFrame.