Kumo Relational Client

The NVIDIA Kumo Relational Client (kumo-relational-client) prepares relational prediction requests and submits them to a Kumo Relational NIM endpoint. Its RelationalClient API binds a relational graph to the endpoint and returns predictions as pandas DataFrames.

The client supports the kumo-relational model. Refer to Supported Models for supported execution paths.

How the Client Works with NIM

Data preparation runs in the client process; model inference runs in the NIM:

Component	Role
Kumo Relational Client	Manages the endpoint connection, prepares prediction requests, and returns results to your Python application.
Relational engine, installed with the client’s `relational` extra	Builds the relational graph, processes Predictive Query Language (PQL) queries, and samples related records for prediction.
Kumo Relational NIM	Hosts the model and runs inference on the supplied prediction context.

Configure a reachable NIM URL before calling predict(). The NIM deployment provides the GPU and model weights; the client process needs neither. Deploy the NIM separately or connect to an existing compatible endpoint.

Explore available predictive AI NIMs, model details, and deployment options in the NVIDIA API Catalog.

For endpoint setup and authentication requirements, follow Deploy, Install, and Connect.

Get Started

Install and Connect

Set up the endpoint connection and install the client and optional connectors.

Run the Quickstart

Build a graph from sample tables and make your first prediction through NIM.

Kumo Relational NIM

Review supported prediction tasks and the model execution flow.

Python API Reference

Look up client connections, graph handles, predictions, and errors.

Run Models Directly in Python

To load pretrained weights and run inference in the same Python process, use the Structured Data Models library.