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Cuopt Install

NVIDIAOfficialCreated by NVIDIA cuOpt Team

  • Skill
  • Workflow
  • Software development
  • Apache-2.0
  • Medium risk

From the author

Install cuOpt for Python, C, or server via pip, conda, or Docker; verify the install. For building cuOpt from source, see cuopt-developer.
— the description by NVIDIA cuOpt Team

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides standardized procedures for installing the NVIDIA cuOpt optimization library. It covers multiple installation methods including pip, conda, and Docker, ensuring compatibility across Python, C, and REST server interfaces. The guide includes essential system requirement checks, such as GPU compute capability and CUDA version matching, along with verification steps to confirm successful deployment.

What it does

  • Provides installation commands for Python, C, and REST server interfaces.
  • Supports installation via pip, conda, and Docker containers.
  • Includes verification scripts for Python, C, and server endpoints.
  • Outlines system requirements including GPU compute capability and CUDA versioning.
  • Offers troubleshooting guidance for common installation and environment issues.

When to use it

  • When setting up a new environment to use NVIDIA cuOpt for optimization tasks.
  • When you need to verify that an existing cuOpt installation is correctly configured.
  • When transitioning between different deployment methods like local pip installs or Docker containers.
  • When troubleshooting missing modules or CUDA compatibility issues.

Advantages

  • Centralized installation instructions for multiple interfaces.
  • Includes specific verification tests to ensure GPU access and library functionality.
  • Clearly distinguishes between Python and C library dependencies.
  • Provides environment-specific guidance for cloud and local setups.

Usage examples

“How do I install cuOpt for Python with CUDA 12?”
The agent provides the specific pip or conda command using the correct index and package suffix for CUDA 12.
“Verify my cuOpt installation is working.”
The agent guides the user through running a DataModel creation test or a server health check to confirm the library is functional.
“I need to run cuOpt as a REST server using Docker.”
The agent provides the docker pull and run commands to deploy the cuOpt server container.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/nvidia/cuopt-install/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip cuopt-install.zip -d .agents/skills

Documentation by NVIDIA cuOpt Team

cuOpt Install (user)

Install cuOpt to use it from Python, C, or as a REST server. For building cuOpt from source to contribute or modify it, see cuopt-developer.

System requirements

  • GPU: NVIDIA Compute Capability ≥ 7.0 (Volta or newer). Examples: V100, A100, H100, RTX 20xx/30xx/40xx. Not supported: GTX 10xx (Pascal).

  • CUDA: 12.x or 13.x. The package CUDA suffix must match the runtime CUDA (e.g. cuopt-cu12 / libcuopt-cu12 with CUDA 12).

  • Driver: NVIDIA driver compatible with the CUDA version.

  • cuopt-cuXX (Python) depends on libcuopt-cuXX (C), so installing the Python package also installs the C library and headers. Installing libcuopt-cuXX on its own does not install the Python API.

Required questions

Ask these if not already clear:

  1. Interface — Python, C, or REST server? Server can be called from any language via HTTP.

  2. CUDA version — What is installed? Check with nvcc --version or nvidia-smi.

  3. Package manager — pip, conda, or Docker preferred?

  4. Environment — Local machine with GPU, cloud instance, Docker/Kubernetes, or remote/server (no local GPU)?

Python API

Choose one — do not run both. The second install would override the first and can cause CUDA / package mismatch.

pip
  • CUDA 13.x:

    pip install --extra-index-url=https://pypi.nvidia.com cuopt-cu13
  • CUDA 12.x:

    pip install --extra-index-url=https://pypi.nvidia.com 'cuopt-cu12==26.2.*'
conda
conda install -c rapidsai -c conda-forge -c nvidia cuopt
Verify
import cuopt
print(cuopt.__version__)
from cuopt import routing
dm = routing.DataModel(n_locations=3, n_fleet=1, n_orders=2)

C API

The C API ships in libcuopt-cuXX, which is also pulled in as a dependency of cuopt-cuXX — so if you already installed the Python package, the C library and headers are already present. Install libcuopt standalone only when you want the C API without Python. Choose one of pip or conda — do not run both.

pip
  • CUDA 13.x:

    pip install --extra-index-url=https://pypi.nvidia.com libcuopt-cu13
  • CUDA 12.x:

    pip install --extra-index-url=https://pypi.nvidia.com 'libcuopt-cu12==26.2.*'
conda
conda install -c rapidsai -c conda-forge -c nvidia libcuopt
Verify

See references/verification_examples.md for the canonical C-API header/library find commands (conda and pip/venv variants).

Server (REST)

pip
pip install --extra-index-url=https://pypi.nvidia.com cuopt-server-cu12 cuopt-sh-client
conda
conda install -c rapidsai -c conda-forge -c nvidia cuopt-server cuopt-sh-client
Docker
docker pull nvidia/cuopt:latest-cuda12.9-py3.13
docker run --gpus all -it --rm -p 8000:8000 nvidia/cuopt:latest-cuda12.9-py3.13
Verify
python -m cuopt_server.cuopt_service --ip 0.0.0.0 --port 8000 &
sleep 5
curl -s http://localhost:8000/cuopt/health | jq .

Common Issues

  • No module named 'cuopt' → check pip list | grep cuopt, which python, reinstall with the correct extra-index-url.

  • CUDA not available → run nvidia-smi and nvcc --version; ensure the package CUDA suffix (cu12 vs cu13) matches the installed CUDA.

  • Python vs C → cuopt-cuXX pulls in libcuopt-cuXX as a transitive dependency, so the C library (libcuopt.so) and headers (cuopt_c.h) are already available after installing the Python package. The reverse is not true: libcuopt-cuXX alone does not install the Python bindings.

See also

  • verification_examples.md — full verification recipes for Python, C, server, and Docker.

  • cuopt-developer — build cuOpt from source and contribute to the codebase.

Files

LICENSE

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs network access, Needs a command-line tool, Needs Python

Security

What our scanner noticed· Medium risk

  • Large base64-encoded blobs
  • Instructions that use the network

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Attribution and licence

Created by
NVIDIA cuOpt Team
Publisher
NVIDIA (Official)
Source
NVIDIA/skills · skills/cuopt-install · commit fce3cca
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE-APACHE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by NVIDIA cuOpt Team or NVIDIA.

Download ZIP

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