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cuopt-server-api-python

cuOpt REST server — start server, endpoints, Python/curl client examples. Use when the user is deploying or calling the REST API.

How do I install this agent skill?

npx skills add https://github.com/nvidia/skills --skill cuopt-server-api-python
view source ↗

Is this agent skill safe to install?

  • Gen Agent Trust Hubpass

    The skill is a collection of deployment and client usage guidelines for the NVIDIA cuOpt REST server. It provides standard Docker commands for server deployment and Python examples for submitting optimization problems (VRP, LP, MILP) to the server. No security risks were identified.

  • Socketpass

    No alerts

  • Snykpass

    Risk: LOW · No issues

What does this agent skill do?

cuOpt Server — Deploy and client (Python/curl)

This skill covers starting the server and client examples (curl, Python). Server has no separate C API (clients can be any language).

Purpose

Use this skill when the user is deploying the cuOpt REST server or writing a client against it — choosing a deployment target, mapping a problem onto the HTTP endpoints, translating between Python-API and REST field names, or debugging a rejected payload.

Prerequisites

  • An NVIDIA GPU with a working CUDA driver (the server requires one; --gpus all for Docker).
  • cuopt-server installed, or Docker with the NVIDIA Container Toolkit. See the install skill.
  • Python clients need requests. No API key or auth token is required by the server itself.

Problem types supported

Problem typeSupported
Routing
LP
MILP
QP

Required questions

Ask these if not already clear:

  1. Problem type — Routing or LP/MILP? (QP not available via REST.)
  2. Deployment — Local, Docker, Kubernetes, or cloud?
  3. Client — Which language or tool will call the API (e.g. Python, curl, another service)?

Start server

# Development
python -m cuopt_server.cuopt_service --ip 0.0.0.0 --port 8000

# Docker — pick the tag matching your CUDA major version
docker run --gpus all -d -p 8000:8000 -e CUOPT_SERVER_PORT=8000 \
  nvidia/cuopt:latest-cu13

Use latest-cu12 or latest-cu13 to match your driver's CUDA major version (latest-cu13-ubi10 for a UBI10 base). Prefer these over the CUDA+Python-specific tags such as latest-cuda12.9-py3.13 — those track a single Python line and go stale when it stops receiving builds.

For production, pin rather than float: latest-* tags are mutable and can silently move to a different image. Use a full release tag (nvidia/cuopt:<release>-cuda<cuda>-py<python>) or an immutable digest (nvidia/cuopt@sha256:<digest>). Check the nvidia/cuopt registry for available tags.

Verify

curl http://localhost:8000/cuopt/health

Instructions

  1. POST to /cuopt/request → get reqId
  2. Poll /cuopt/solution/{reqId} until solution ready
  3. Parse response

Treat reqId as untrusted input: validate it (e.g. re.fullmatch(r"[A-Za-z0-9_-]{1,64}", req_id)) before interpolating it into the polling URL, and set an explicit timeout on every request.

Examples

import requests, time
SERVER = "http://localhost:8000"
HEADERS = {"Content-Type": "application/json", "CLIENT-VERSION": "custom"}
payload = {
    "cost_matrix_data": {"data": {"0": [[0,10,15],[10,0,12],[15,12,0]]}},
    "travel_time_matrix_data": {"data": {"0": [[0,10,15],[10,0,12],[15,12,0]]}},
    "task_data": {"task_locations": [1, 2], "demand": [[10, 20]], "task_time_windows": [[0,100],[0,100]], "service_times": [5, 5]},
    "fleet_data": {"vehicle_locations": [[0, 0]], "capacities": [[50]], "vehicle_time_windows": [[0, 200]]},
    "solver_config": {"time_limit": 5}
}
r = requests.post(f"{SERVER}/cuopt/request", json=payload, headers=HEADERS, timeout=30)
req_id = r.json()["reqId"]
# Poll: GET /cuopt/solution/{req_id}

Terminology: REST vs Python API

Python APIREST
order_locationstask_locations
set_order_time_windows()task_time_windows
service_timesservice_times

Use travel_time_matrix_data (not transit_time_matrix_data). Capacities: [[50, 50]] not [[50], [50]].

Troubleshooting

ErrorCauseSolution
422 Unprocessable EntityField name not in the schemaCheck names against the OpenAPI spec at /cuopt.yaml. Most common: transit_time_matrix_datatravel_time_matrix_data
422 on fleet_dataCapacities nested per vehicle instead of per dimensionUse [[50, 50]] (one inner list per capacity dimension), not [[50], [50]]
Connection refusedServer not up, or bound to a different interface/portcurl http://localhost:8000/cuopt/health; start with --ip 0.0.0.0 --port 8000
Docker container exits immediatelyNo GPU visible to the containerRun with --gpus all and confirm the NVIDIA Container Toolkit is installed
Polling never returns a solutionSolve exceeds the client's poll budgetRaise solver_config.time_limit and the poll loop count together

Capture the reqId and the full response body for any failed request — both are needed to diagnose server-side rejections.

Limitations

  • QP is not exposed over REST. Use the Python or C API for quadratic objectives.
  • The server ships no authentication or TLS. Anything that can reach the port can submit jobs. Put it behind a gateway and treat --server/base URLs as trusted-network endpoints only.
  • Solutions are retrieved by polling; there is no push/webhook delivery.
  • One request is solved at a time per server process; concurrency requires multiple replicas.

Runnable assets

Run from each asset directory (server must be running; scripts exit 0 if server unreachable). All use Python requests and accept --server (default http://localhost:8000):

See assets/README.md for overview.

Escalate

For contribution or build-from-source, see the developer skill.

Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.

<a href="https://skillzs.dev/skills/nvidia/skills/cuopt-server-api-python">View cuopt-server-api-python on skillZs</a>