Cuopt Install
NVIDIAOfficialCreated by NVIDIA cuOpt Team
- Skill
- Workflow
- Software development
- Apache-2.0
- Medium risk
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
Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/cuopt-install/downloadUnzip 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-cu12with CUDA 12).Driver: NVIDIA driver compatible with the CUDA version.
cuopt-cuXX(Python) depends onlibcuopt-cuXX(C), so installing the Python package also installs the C library and headers. Installinglibcuopt-cuXXon its own does not install the Python API.
Required questions
Ask these if not already clear:
Interface — Python, C, or REST server? Server can be called from any language via HTTP.
CUDA version — What is installed? Check with
nvcc --versionornvidia-smi.Package manager — pip, conda, or Docker preferred?
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-cu13CUDA 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 cuoptVerify
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-cu13CUDA 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 libcuoptVerify
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-clientconda
conda install -c rapidsai -c conda-forge -c nvidia cuopt-server cuopt-sh-clientDocker
docker pull nvidia/cuopt:latest-cuda12.9-py3.13
docker run --gpus all -it --rm -p 8000:8000 nvidia/cuopt:latest-cuda12.9-py3.13Verify
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'→ checkpip list | grep cuopt,which python, reinstall with the correct extra-index-url.CUDA not available → run
nvidia-smiandnvcc --version; ensure the package CUDA suffix (cu12vscu13) matches the installed CUDA.Python vs C →
cuopt-cuXXpulls inlibcuopt-cuXXas 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-cuXXalone 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
- ATTRIBUTION.mdAdded by AutoGineer908 B
- BENCHMARK.md4.8 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md4.1 KiB
- benchmark/evals.json15.8 KiB
- evals/evals.json6.7 KiB
- references/verification_examples.md4.2 KiB
- skill-card.md3.9 KiB
- skill.oms.sig4.9 KiB
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
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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.
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