skills.rest
NVIDIA
NVIDIAOFFICIAL
Explore all skills by this author →
WebsiteGitHub

cuopt-installation-api-python

Install cuOpt Python API via pip, conda, or Docker and verify CUDA 12/13 compatibility.

2.8k|332|Updated Feb 25, 2026
Search Tags:#pip#verification#docker#conda#install#python-api#cuopt
One-click install
npx skills add https://github.com/NVIDIA/skills --skill cuopt-installation-api-python
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: cuopt-installation-api-python
Source: https://github.com/NVIDIA/skills/tree/main/skills/cuopt/cuopt-installation-api-python
Command: npx skills add https://github.com/NVIDIA/skills --skill cuopt-installation-api-python

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Install cuOpt Python API and verify a working environment so developers can build and test GPU-accelerated routing solutions directly from Python. This skill covers setting up the Python client with pip or conda, optional Docker deployment, and validation steps for both client and server components.

Core Features & Use Cases

  • Install via pip or conda for Python environments, matching CUDA version (cu12/cu13) and cuOpt server compatibility.
  • Verify Python client functionality and server availability with provided commands.
  • Support Docker-based deployment for containerized workflows and quick experimentation.

Quick Start

Install cuOpt Python API using pip, conda, or Docker and verify the installation with the steps described above.

Frequently Asked Questions about cuopt-installation-api-python

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I install the cuOpt Python API using pip or conda?▼

Install the cuOpt Python API using pip or conda by selecting the package matching your CUDA version (cu12 or cu13) to ensure compatibility with your GPU-accelerated routing environment. Verify the installation with provided commands to confirm client functionality.

Can I deploy the cuOpt Python API using Docker?▼

Yes, Docker deployment is supported for containerized workflows and quick experimentation. Deploying the cuOpt Python API via Docker allows developers to test GPU-accelerated routing solutions without manual environment configuration.

Does the cuOpt Python API work with CUDA 12 and CUDA 13?▼

The cuOpt Python API supports both CUDA 12 and CUDA 13. Compatibility checks for CUDA versions, Python environments, and cuOpt server components are required during installation to ensure proper client and server validation.

What is the best way to verify a cuOpt Python API installation?▼

Verify a cuOpt Python API installation by running the provided example commands to check both Python client functionality and server availability. This validation confirms environment readiness for GPU-accelerated routing and server integration.

Why does my cuOpt Python API installation fail during environment setup?▼

cuOpt Python API installation issues often stem from mismatched CUDA versions, incompatible Python environments, or missing cuOpt server components. Handle these common issues by verifying compatibility across CUDA 12/13, Python, and server dependencies.

Related Skills

Explore More Software Engineering Skills→
NVIDIANVIDIA
2.8k

cuopt-installation-common

Identify cuOpt system requirements for GPU, CUDA, and deployment environments.

Official
Intermediate
NVIDIANVIDIA
2.8k

cuopt-server-api-python

Deploy cuOpt REST server and access routing endpoints with Python clients.

Official
Intermediate
sayalinvidiasayalinvidia

cuopt-install

Guide cuOpt installation via pip, conda, or Docker with CUDA checks.

Community
Intermediate
uneezaismailuneezaismail
2

isaac-patterns

Define Isaac Sim and Isaac ROS integration patterns for Physical AI workflows.

Community
Advanced