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.

The SDK currently executes Data Designer workloads through the Data Designer API. Local SDK execution is planned, but not available yet. Use nemo data-designer ... run for local in-process execution today.

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:

1 import os
2 from nemo_platform import NeMoPlatform
3 
4 
5 client = NeMoPlatform(
6     base_url=os.environ.get("NMP_BASE_URL", "http://localhost:8080"),
7     workspace="default",
8 )
9 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
`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).

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.