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NVIDIA® cuOpt™ is an open-source GPU-accelerated engine for solving complex decision optimization problems, such as Mixed Integer Programming, Linear Programming, and Vehicle Routing problems. This repository contains a collection of examples demonstrating the use of NVIDIA cuOpt via service APIs, SDK, and Integration with other OSS optimization packages.

This repository is underApache 2.0 License

cuOpt Docs

The easiest way to get started with these examples is using cuOpt docker image.

• Docker
• NVIDIA Container Toolkit
• NVIDIA GPU with appropriate drivers

For detailed system requirements, please refer to theNVIDIA cuOpt System Requirements documentation.

Specific requirements are listed in each workflow's README.md and in the root directory's requirements.txt files.

1. Clone this repository:

git clone https://github.com/NVIDIA/cuopt-examples.gitcd cuopt-examples

2. Pull the cuOpt docker image:

For cuda-13:

docker pull nvidia/cuopt:25.12.0a-cuda13.0-py3.13

For cuda-12

docker pull nvidia/cuopt:25.12.0a-cuda12.9-py3.13

3. Run the examples:

For cuda-13:

docker run -it --rm --gpus all --network=host -v$(pwd):/workspace -w /workspace nvidia/cuopt:25.12.0a-cuda13.0-py3.13 /bin/bash -c"pip install -r requirements.txt; jupyter-notebook"

For cuda-12:

docker run -it --rm --gpus all --network=host -v$(pwd):/workspace -w /workspace nvidia/cuopt:25.12.0a-cuda12.9-py3.13 /bin/bash -c"pip install -r requirements.txt; jupyter-notebook"

4. Open your browser with the link provided in the terminal, and you can see the notebooks.

These notebooks have been tested onNVIDIA Launchable,Google Colab, and local Jupyter environments. They may work on other platforms as well.

The repository is organized by use cases, with each directory containing examples and implementations specific to that use case. Each use case directory includes:

• Example notebooks
• README.md with specific instructions

Theintra-factory_transport directory contains an example of using the cuOpt SDK API to solve a Capacitated Pickup and Delivery Problem with Time Windows (CPDPTW) for optimizing routes of Autonomous Mobile Robots (AMRs) within a factory environment.

We welcome contributions! Please see ourCONTRIBUTING.md file for guidelines on how to contribute new examples or improve existing ones.

Example videos can be found listed in the documentation