Imported from nvidia/skills (
skills/cuopt-server-api-python/SKILL.md) via skills.sh. Install upstream withnpx skills add nvidia/skills --skill cuopt-server-api-python. Copyright stays with the author (Apache-2.0).
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 allfor Docker). cuopt-serverinstalled, 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-cu13Use 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/healthInstructions
- POST to
/cuopt/request→ getreqId - 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 Entity |
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.