cuopt-server-api-python

par nvidia

Serveur REST cuOpt — démarrage du serveur, points d'accès, exemples de clients Python/curl. À utiliser lorsque l'utilisateur déploie ou appelle l'API REST.

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

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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 type	Supported
Routing	✓
LP	✓
MILP	✓
QP	✗

Required questions

Ask these if not already clear:

Problem type — Routing or LP/MILP? (QP not available via REST.)
Deployment — Local, Docker, Kubernetes, or cloud?
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

Confirm the server is up by requesting GET /cuopt/health on the local port (e.g. http://localhost:8000/cuopt/health) — a healthy server returns HTTP 200.

Instructions

POST to /cuopt/request → get reqId
Poll /cuopt/solution/{reqId} until solution ready
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 API	REST
order_locations	task_locations
set_order_time_windows()	task_time_windows
service_times	service_times

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

Troubleshooting

Error	Cause	Solution
`422 Unprocessable Content`	Field name not in the schema	Check names against the OpenAPI spec at `/cuopt.yaml`. Most common: `transit_time_matrix_data` → `travel_time_matrix_data`
`422` on `fleet_data`	Capacities nested per vehicle instead of per dimension	Use `[[50, 50]]` (one inner list per capacity dimension), not `[[50], [50]]`
Connection refused	Server not up, or bound to a different interface/port	`curl http://localhost:8000/cuopt/health`; start with `--ip 0.0.0.0 --port 8000`
Docker container exits immediately	No GPU visible to the container	Run with `--gpus all` and confirm the NVIDIA Container Toolkit is installed
Polling never returns a solution	Solve exceeds the client's poll budget	Raise `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):