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cuopt-routing-api-python

nvidia

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This skill has been hardened by Chainguard

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This version passed the hardening gate. Hardening applied 3 targeted changes and preserved all other text. Classified findings before hardening: 2. Classified findings closed: 0. Classified findings remaining: 2. Unclassified scanner findings remaining: 0. Review findings closed: 3. Review findings remaining: 6.


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BENCHMARK.md

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references/server_examples.md

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SKILL.md

cuOpt Routing — Python API

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

Required questions

Ask these if not already clear:

  1. Problem type — TSP, VRP, or PDP?
  2. Locations — How many? Depot(s)? Cost or distance between pairs (matrix or derived)?
  3. Orders / tasks — Which locations must be visited? Demand or service per stop?
  4. Fleet — Number of vehicles, capacity per vehicle (and per dimension if multiple), start/end locations?
  5. 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

ProblemFix

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()

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.

Install

Install with chainctl
chainctl skill install public/nvidia/cuopt-routing-api-python:latest
Get started with chainctl
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Provenance

Upstream sourcehttps://github.com/nvidia/skills
Upstream commit77580296021eb309cfb372787409660ae35e6b2e
OCI reference

skills.chainreg.biz/public/github.com/nvidia/skills/cuopt-routing-api-python:latest

License

Apache-2.0

Files

BENCHMARK.md

HARDENING.md

SKILL.md

assets/README.md

assets/pdp_basic/README.md

assets/pdp_basic/model.py

assets/vrp_basic/README.md

assets/vrp_basic/model.py

evals/evals.json

references/examples.md

references/server_examples.md

skill-card.md

skill.oms.sig


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