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:

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).

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>-cu<cuda-major>) 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

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 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):

• assets/vrp_simple/ — Basic VRP (no time windows)
• assets/vrp_basic/ — VRP with time windows
• assets/pdp_basic/ — Pickup and delivery
• assets/lp_basic/ — LP via REST (CSR format)
• assets/milp_basic/ — MILP via REST

See assets/README.md for overview.

Escalate

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