cuopt-server-api-python
$
npx mdskill add NVIDIA/skills/cuopt-server-api-pythonLaunch cuOpt server and execute routing optimization via Python or curl.
- Enables agents to deploy the REST server and run vehicle routing problems.
- Integrates with cuOpt REST endpoints for request submission and solution polling.
- Executes optimization by accepting cost, time, and fleet data in JSON payloads.
- Delivers results through HTTP responses containing solution paths and metrics.
SKILL.md
.github/skills/cuopt-server-api-pythonView on GitHub ↗
---
name: cuopt-server-api-python
version: "26.06.00"
description: cuOpt REST server — start server, endpoints, Python/curl client examples. Use when the user is deploying or calling the REST API.
---
# 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).
## Start server
```bash
# Development
python -m cuopt_server.cuopt_service --ip 0.0.0.0 --port 8000
# Docker
docker run --gpus all -d -p 8000:8000 -e CUOPT_SERVER_PORT=8000 \
nvidia/cuopt:latest-cuda12.9-py3.13
```
## Verify
```bash
curl http://localhost:8000/cuopt/health
```
## Workflow
1. POST to `/cuopt/request` → get `reqId`
2. Poll `/cuopt/solution/{reqId}` until solution ready
3. Parse response
## Python client (routing)
```python
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)
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]]`.
## Debugging (422 / payload)
**Validation errors:** Check field names against OpenAPI (`/cuopt.yaml`). Common mistakes: `transit_time_matrix_data` → `travel_time_matrix_data`; capacities per dimension `[[50, 50]]` not per vehicle `[[50], [50]]`. Capture `reqId` and response body for failed requests.
## Runnable assets
Run from each asset directory (server must be running; scripts exit 0 if server unreachable). All use Python `requests`:
- [assets/vrp_simple/](assets/vrp_simple/) — Basic VRP (no time windows)
- [assets/vrp_basic/](assets/vrp_basic/) — VRP with time windows
- [assets/pdp_basic/](assets/pdp_basic/) — Pickup and delivery
- [assets/lp_basic/](assets/lp_basic/) — LP via REST (CSR format)
- [assets/milp_basic/](assets/milp_basic/) — MILP via REST
See [assets/README.md](assets/README.md) for overview.
## Escalate
For contribution or build-from-source, see the developer skill.
More from NVIDIA/skills
- accessing-mlflowQuery and browse evaluation results stored in MLflow. Use when the user wants to look up runs by invocation ID, compare metrics across models, fetch artifacts (configs, logs, results), or set up the MLflow MCP server. ALWAYS triggers on mentions of MLflow, experiment results, run comparison, invocation IDs in the context of results, or MLflow MCP setup.
- ad-add-fusion-transformation>
- ad-conf-check>
- ad-graph-dump>
- ad-model-onboard>
- ad-pipeline-failure-pr>
- add-benchmark>
- aiq-deploy|
- aiq-research|
- byobCreate custom LLM evaluation benchmarks using the BYOB decorator framework. Use when the user wants to (1) create a new benchmark from a dataset, (2) pick or write a scorer, (3) compile and run a BYOB benchmark, (4) containerize a benchmark, or (5) use LLM-as-Judge evaluation. Triggers on mentions of BYOB, custom benchmark, bring your own benchmark, scorer, or benchmark compilation.