cuopt-routing-api-python

cuOpt - GPU-accelerated Optimization

NVIDIA® cuOpt™ is a GPU-accelerated optimization engine that excels in mixed integer linear programming (MILP), linear programming (LP), quadratic programming (QP), and vehicle routing problems (VRP). It enables near real-time solutions for large-scale LPs with millions of variables and constraints, and MIPs with hundreds of thousands of variables. cuOpt offers easy integration into existing modeling languages and seamless deployment across hybrid and multi-cloud environments.

The core engine is written in C++ and wrapped with a C API, Python API and Server API.

For the latest version, ensure you are on the main branch.

Latest Documentation

cuOpt Documentation

Supported APIs

cuOpt supports the following APIs:

• C API support
  • Linear Programming (LP)
  • Mixed Integer Linear Programming (MILP)
  • Quadratic Programming (QP)
• C++ API support
  • cuOpt is written in C++ and includes a native C++ API. However, we do not provide documentation for the C++ API at this time. We anticipate that the C++ API will change significantly in the future. Use it at your own risk.
• Python support
  • Routing (TSP, VRP, and PDP)
  • Linear Programming (LP), Mixed Integer Linear Programming (MILP) and Quadratic Programming (QP)
    • Algebraic modeling Python API allows users to easily build constraints and objectives
• Server support
  • Linear Programming (LP)
  • Mixed Integer Linear Programming (MILP)
  • Routing (TSP, VRP, and PDP)

This repo is also hosted as a COIN-OR project.

Latest Release Notes:

RELEASE-NOTES.md

Installation

CUDA/GPU requirements

• CUDA 12.0+ or CUDA 13.0+
• NVIDIA driver >= 525.60.13 (Linux) and >= 527.41 (Windows)
• Volta architecture or better (Compute Capability >=7.0)

Python requirements

• Python >=3.11, <=3.14

OS requirements

• Only Linux is supported and Windows via WSL2
  • x86_64 (64-bit)
  • aarch64 (64-bit)

Note: WSL2 is tested to run cuOpt, but not for building.

More details on system requirements can be found here

Pip

Pip wheels are easy to install and easy to configure. Users with existing workflows who uses pip as base to build their workflows can use pip to install cuOpt.

cuOpt can be installed via pip from the NVIDIA Python Package Index. Be sure to select the appropriate cuOpt package depending on the major version of CUDA available in your environment:

For CUDA 12.x:

pip install \
  --extra-index-url=https://pypi.nvidia.com \
  nvidia-cuda-runtime-cu12==12.9.* \
  cuopt-server-cu12==26.06.* cuopt-sh-client==26.06.*

Development wheels are available as nightlies, please update --extra-index-url to https://pypi.anaconda.org/rapidsai-wheels-nightly/simple/ to install latest nightly packages.

pip install --pre \
  --extra-index-url=https://pypi.nvidia.com \
  --extra-index-url=https://pypi.anaconda.org/rapidsai-wheels-nightly/simple/ \
  cuopt-server-cu12==26.06.* cuopt-sh-client==26.06.*

For CUDA 13.x:

pip install \
  --extra-index-url=https://pypi.nvidia.com \
  cuopt-server-cu13==26.06.* cuopt-sh-client==26.06.*