cuopt-sandbox

NemoClaw सैंडबॉक्स में cuOpt चलाएँ — प्रोब/स्मोक गेट्स, रद्द करने योग्य Python gRPC जॉब्स को प्राथमिकता दें, लीगेसी रिमोट निष्पादन का उपयोग केवल तब करें जब वह API अनुपलब्ध हो, फिर…

cuOpt in the NemoClaw sandbox

Infrastructure for solving with cuOpt inside NemoClaw: probe/smoke gates, capability-based gRPC execution, and handoff to vendored formulation/API skills.

When to use

Constructive planning from uploaded constraint data (schedule, assign, route, roster — any wording). See references/intent-and-triggers.md.
CSV upload + plan → optimization-from-data-orchestrator + references/activation.md.
ImportError / cudaErrorInsufficientDriver.

Mandatory order

Complete before any assignment output, feasibility verdict, or custom solver code:

Step	Action	Reference
0	Probe capability → gRPC smoke	`references/grpc-connectivity-and-smoke.md`
1	Formulate	vendored `*-formulation` skills
2	Solve (one job, terminal status)	`references/long-running-jobs.md`

Inspecting uploaded data for columns and constraints is fine; emit a completed plan only after smoke succeeds.

Quick reference

Imports (LP/MILP/QP):

from cuopt.linear_programming.problem import Problem, INTEGER, MINIMIZE
from cuopt.linear_programming.solver_settings import SolverSettings
# When available (preferred):
from cuopt.grpc.linear_programming import Client, GrpcError, JobStatus

Interfaces: LP/MILP/QP → prefer async Python gRPC client on :5001, otherwise use the legacy remote fallback; routing → REST :5000. See references/async-grpc-python.md, references/remote-execution-fallback.md, references/interfaces.md, and references/routing-rest-only.md.

Reference index

Topic	File
Activation / skill order	`references/activation.md`
Intent / paraphrases	`references/intent-and-triggers.md`
Gates / common mistakes	`references/gates-and-first-actions.md`
Async Python gRPC jobs	`references/async-grpc-python.md`
Legacy remote fallback	`references/remote-execution-fallback.md`
Connectivity + smoke	`references/grpc-connectivity-and-smoke.md`
Python imports	`references/python-imports.md`
gRPC vs REST	`references/interfaces.md`
Routing REST	`references/routing-rest-only.md`
Paths + probe	`references/environment-and-networking.md`
Long-running jobs	`references/long-running-jobs.md`
Troubleshooting	`references/troubleshooting.md`

Orchestration skills (local)

After gates: optimization-from-data-orchestrator → optimization-intent-router → tabular-optimization-ingestion → cuopt-model-mapper (and optimization-mode-router when replay/audit signals appear).

Vendored upstream skills

Installed under /sandbox/.openclaw/skills/ by install-skill: numerical-optimization-formulation, cuopt-numerical-optimization-api-python, routing-formulation, cuopt-routing-api-python, cuopt-server-api-python, cuopt-user-rules, etc.

For LP/MILP/QP, use upstream skills to build the model. Execute with this skill's async Client lifecycle when importable; only then fall back to the legacy remote Problem.solve() path, which cannot cancel submitted work.