NIM REST API

Kumo Relational exposes the Universal Structured Data Foundation Models API, a shared REST request and response format for structured-data models. Use the Python SDK for standard prediction workflows. Call the REST API directly when integrating Kumo Relational into a service or generating a client from its OpenAPI schema.

Choose an endpoint

• Self-hosted NVIDIA NIM: NVIDIA NIM is the deployed inference service that hosts Kumo Relational and exposes its REST endpoint. Use the base URL of your deployment. The NIM API contract does not require authentication, but a gateway in front of the deployment might require an API key or another credential.
• NVIDIA API Catalog: Use the server URL and bearer-token authentication shown in the published API reference.

Run a one-shot prediction

The POST /v1/predictions endpoint accepts one complete request in the shared API format. The request specifies the model, prediction task, relational schema, labeled context, rows to predict, requested outputs, and optional inference settings and metadata. The endpoint returns the prediction synchronously and does not create a durable prediction resource that you can retrieve later.

To send a request:

1. Copy the complete multitable_binary_classification request example from the published Kumo Relational NIM API reference and save it as request.json.
2. Set KUMO_RELATIONAL_API_ENDPOINT to the base URL of your deployment, such as http://localhost:8000.
3. Run the following command:

curl --fail-with-body \
  --request POST \
  --url "${KUMO_RELATIONAL_API_ENDPOINT}/v1/predictions" \
  --header "Content-Type: application/json" \
  --data @request.json

If your gateway requires authentication, add its required authentication header to the command. For the NVIDIA API Catalog endpoint, add --header "Authorization: Bearer ${NVIDIA_API_KEY}", as shown in the generated API examples.

Response and errors

A successful response contains a response-level correlation ID, the model name, prediction items, and metadata. Each prediction item includes a zero-based row_index that identifies its position among the requested rows. It can also include the input instance_id in the id field and requested output fields such as prediction and probabilities.

Errors use the application/problem+json media type with fields such as type, status, title, detail, and code. See the generated API reference for the complete request schema, response schema, error model, and runnable examples.