Cuopt routing api python

Skill NVIDIA/skills/skills/cuopt-routing-api-python

Agent Skills for NVIDIA products — install into Claude Code, Codex, and other coding agents to run Physical AI, robotics, simulation, CUDA, and RAG workflows end to end.

Install

npx -y skills add NVIDIA/skills --skill cuopt-routing-api-python

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What its author says it does

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Vehicle routing (VRP, TSP, PDP) with cuOpt — Python API only. Use when the user is building or solving routing in Python.

The file declares its own license as Apache-2.0. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

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cuOpt Routing — Python API

This skill is Python only. Routing has no C API in cuOpt.

Required questions

Ask these if not already clear:

Problem type — TSP, VRP, or PDP?
Locations — How many? Depot(s)? Cost or distance between pairs (matrix or derived)?
Orders / tasks — Which locations must be visited? Demand or service per stop?
Fleet — Number of vehicles, capacity per vehicle (and per dimension if multiple), start/end locations?
Constraints — Time windows (earliest/latest arrival), service times, precedence (order A before B)?

Minimal VRP Example

import cudf
from cuopt import routing

cost_matrix = cudf.DataFrame([...], dtype="float32")
dm = routing.DataModel(n_locations=4, n_fleet=2, n_orders=3)
dm.add_cost_matrix(cost_matrix)
dm.set_order_locations(cudf.Series([1, 2, 3], dtype="int32"))
solution = routing.Solve(dm, routing.SolverSettings())

if solution.get_status() == 0:
    solution.display_routes()

Adding Constraints

# Time windows
dm.add_transit_time_matrix(transit_time_matrix)
dm.set_order_time_windows(earliest_series, latest_series)

# Capacities
dm.add_capacity_dimension("weight", demand_series, capacity_series)
dm.set_order_service_times(service_times)
dm.set_vehicle_locations(start_locations, end_locations)
dm.set_vehicle_time_windows(earliest_start, latest_return)

# Pickup-delivery pairs
dm.set_pickup_delivery_pairs(pickup_indices, delivery_indices)

# Precedence
dm.add_order_precedence(node_id=2, preceding_nodes=np.array([0, 1]))

Solution Checking

status = solution.get_status()  # 0=SUCCESS, 1=FAIL, 2=TIMEOUT, 3=EMPTY
if status == 0:
    route_df = solution.get_route()
    total_cost = solution.get_total_objective()
else:
    print(solution.get_error_message())
    print(solution.get_infeasible_orders().to_list())

Data Types (use explicit dtypes)

cost_matrix = cost_matrix.astype("float32")
order_locations = cudf.Series([...], dtype="int32")
demand = cudf.Series([...], dtype="int32")

Solver Settings

ss = routing.SolverSettings()
ss.set_time_limit(30)
ss.set_verbose_mode(True)
ss.set_error_logging_mode(True)

Common Issues

Problem	Fix
Empty solution	Widen time windows or check travel times
Infeasible orders	Increase fleet or capacity
Status != 0 with time windows	Add `add_transit_time_matrix()`
Wrong cost	Check cost_matrix is symmetric
`compute_waypoint_sequence` alters route_df	It replaces the `location` column with waypoint ids in place — pass `route_df.copy()` if you still need cost-matrix indices (e.g. when iterating per truck)

Debugging

When status != 0: print(solution.get_error_message()) and print(solution.get_infeasible_orders().to_list()) to see which orders are infeasible.

Data types: Use explicit dtypes (float32, int32) for matrices and series to avoid silent errors.

Examples

examples.md — VRP, PDP, multi-depot
server_examples.md — REST client (curl, Python)
Reference models: This skill's assets/ — vrp_basic, pdp_basic. See assets/README.md.

Escalate

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