cuopt-developer
FreeModify, build, test, debug, and contribute to NVIDIA cuOpt (C++/CUDA, Python, server, CI). Use for solver internals, PRs, DCO, and code conventions.
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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.
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See how to installReview the instructions the agent will receive before you install.
This skill is Python only. Routing has no C API in cuOpt.
Ask these if not already clear:
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()
# 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]))
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())
cost_matrix = cost_matrix.astype("float32")
order_locations = cudf.Series([...], dtype="int32")
demand = cudf.Series([...], dtype="int32")
ss = routing.SolverSettings()
ss.set_time_limit(30)
ss.set_verbose_mode(True)
ss.set_error_logging_mode(True)
| 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) |
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.
assets/ — vrp_basic, pdp_basic. See assets/README.md.For contribution or build-from-source, see the developer skill.
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Modify, build, test, debug, and contribute to NVIDIA cuOpt (C++/CUDA, Python, server, CI). Use for solver internals, PRs, DCO, and code conventions.
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Install cuOpt for Python, C, or server via pip, conda, or Docker; verify the install. For building cuOpt from source, see cuopt-developer.
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Trace, complete, and interpret the Pareto frontier across competing objectives using repeated single-objective cuOpt solves (weighted-sum and ε-constraint).
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LP, MILP, and QP (beta) with cuOpt — Python, C, and CLI. Use when the user is solving LP, MILP, or QP with any cuOpt interface.
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LP, MILP, QP — concepts, problem-text parsing, and formulation patterns (parameters, constraints, decisions, objective). Concepts only; no API.
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cuOpt REST server — start server, endpoints, Python/curl client examples. Use when the user is deploying or calling the REST API.
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