Overview | NVIDIA Kumo

What Is NVIDIA Kumo?

NVIDIA Kumo is a family of open models, libraries, and tools for making predictions from tabular, relational, and time-series data. Use Kumo to forecast outcomes, score risk, detect anomalies, and generate recommendations.

Use a pretrained model to make predictions directly, connect to a deployed model through a client, or train your own task-specific model with Kumo Engine.

Choose a Model

These pretrained foundation models let you make predictions for supported tasks without training a model on your own data. Choose by the structure of your data and the prediction you need.

Model	Data and task	Required inputs	Outputs	Run with
Kumo Tabular	Single-table classification or regression	Labeled context and query rows	Class probabilities (classification) or numeric predictions (regression)	Structured Data Models
Kumo Relational	Prediction across related tables	Context, query rows, and related tables; graph and query for NIM	Class probabilities (classification) or numeric predictions (regression)	Structured Data Models; Kumo Relational Client with NIM
Kumo Forecast	Time-series forecasting	History, timestamps, target, and horizon	Future target values	Kumo-TS
Kumo Anomaly	Time-series anomaly detection	Numeric signal sequences	Anomaly flags and scores	Kumo-TS

Choose a Library or Tool

Use Kumo Engine to train your own task-specific models, Structured Data Models to run pretrained foundation models directly in Python, or Structured Data Models Client to request predictions from a compatible Kumo Relational NIM endpoint. For the Kumo Forecast and Kumo Anomaly workflows documented below, use Kumo-TS.

Library or tool	Use it to…	Execution and requirements
Kumo Engine	Train and evaluate a task-specific model on your relational data.	Runs in a supported GPU-enabled Linux or notebook environment. Requires local working storage.
Structured Data Models	Run pretrained Kumo Tabular or Kumo Relational models directly in Python.	Runs in your Python process. Device and memory requirements depend on the selected model.
Structured Data Models Client	Request predictions from a deployed Kumo Relational NIM using Kumo Relational Client.	The client runs in your application; inference runs on the NIM server. Requires a reachable, compatible endpoint.
Kumo-TS	Run the Kumo Forecast or Kumo Anomaly workflows documented below.	Runs in Python. Forecast and Anomaly require separate environments.

Build Your First Workflow

Structured Data Models

Kumo-TS

Structured Data Models Client

Kumo Engine

Run Kumo Tabular or Kumo Relational in Python

Install the library

Check Python, PyTorch, and device requirements in the SDM setup guide and your model card. Then install Structured Data Models (SDM).

Prepare the rows you want to predict

Use rows with known outcomes as labeled context, and keep the rows you want to predict separate. For Relational, include the related tables and their keys. The table semantics guide explains how to represent your columns.

Run the model and read the predictions

Pass the context and query rows to the model. Classification returns class probabilities; regression returns numeric predictions.

Install SDM and run inference

Examples You Can Run

Explore a use case to find the model, tools, and example to start with.

🗂️ Classify records from labeled examples

Classify vehicle silhouettes from 18 numerical features using Kumo Tabular and the OpenML Vehicle dataset. The notebook runs the small model on CPU, uses labeled examples as context, and returns class probabilities for held-out rows. It compares prediction accuracy and ROC AUC with a majority-class baseline.

A GPU is not required. The notebook installs its dependencies and downloads the dataset and pretrained weights, so you need internet access. Use a separate notebook environment for its pinned dependencies.

Model: Kumo Tabular
Library: Structured Data Models
Example: Open the Kumo Tabular CPU notebook guide
Benchmarks: Tabular evaluation results and limitations

🔄 Predict customer churn

Estimate which customers are likely to stop buying so you can prioritize follow-up. The AdventureWorks notebook uses customer and order history to predict whether an active customer will place no orders in the next 90 days. You will build a graph from four related tables, describe churn in a predictive query, and inspect the predicted outcome and its probabilities without training a task-specific model. Before running, install the client and prepare a compatible NIM endpoint and the AdventureWorks data files.

Model: Kumo Relational
Setup: Connect Kumo Relational Client to NIM
Example: Open the AdventureWorks notebook and follow “Predict customer churn.”

📈 Forecast future readings from a time series

Predict future values from a history of timestamped measurements. The Kumo Forecast example reads an ETTh sample CSV and predicts the next 100 values of its LULL signal. Install the Forecast environment and provide the CSV files expected by the script, or update the paths and columns for your own data. Context-enhanced forecasting requires a separate history CSV. Kumo Forecast is intended for research and development only.

Model: Kumo Forecast
Library: Kumo-TS Forecast setup and guide
Example: Open the forecasting code

🔎 Spot unusual sensor readings

Find readings that depart from the usual patterns across a set of sensors. The Kumo Anomaly example generates synthetic sensor data with spikes, dips, shifts, and noise, then returns anomaly flags and error scores. Install the Anomaly environment and allow model-weight downloads or supply local weights. Confirm that inference produced the Anomaly flags and MAE scores; a dataset preview alone does not confirm prediction. Kumo Anomaly is intended for research and development only.

Model: Kumo Anomaly
Library: Kumo-TS Anomaly setup and guide
Example: Open the anomaly-detection code

🛠️ Train a model to predict repeat purchases

Build a model for a prediction task you define on your own relational data. This Kumo Engine guide uses AdventureWorks to predict whether active customers will order again in the next 90 days. You will prepare the tables and graph, train and evaluate the model, then save it with its training metrics and predictions for later use. Before running, configure the documented GPU environment and writable local scratch, then download and prepare the AdventureWorks data.

Tool: Kumo Engine
Training: Define and train a task-specific model
Guide and code: Predict a yes or no outcome on Linux

Continue Building

Integrate with APIs

Structured Data Models model API · Relational Client Python API · Relational NIM REST API

Deploy and manage execution

Kumo Engine deployment options · Relational NIM deployment and connection

Check compatibility and resolve issues

For SDM import, model download, device, or input errors, start with SDM Troubleshooting.

For setup requirements, follow SDM environment setup, Kumo Relational Client setup, Kumo Engine prerequisites, or Kumo-TS package requirements. For prediction issues, check the input schema and expected outputs in your selected example.

Understand the concepts and architecture

Core Concepts · Workflow Overview