gRPC Remote Execution

NVIDIA cuOpt can run LP, MIP, and QP solves on a remote GPU host through cuopt_grpc_server. There are two ways to reach that server:

Remote execution (zero code change)

Set CUOPT_REMOTE_HOST and CUOPT_REMOTE_PORT on the client machine. The Python solver APIs, the C API cuOptSolve, and cuopt_cli forward the solve automatically. No client code changes are required. See Quick Start.

gRPC clients (explicit client)

Your program opens a gRPC connection and manages jobs itself. Use the Python async gRPC client (cuopt.grpc.linear_programming.Client) for job management, or speak CuOptRemoteService directly from a custom client (gRPC API (Reference)).

In this section, remote execution always means the zero-code-change path above. When talking about programs that construct a client and call gRPC themselves, we say gRPC client.

Note

Problem types: LP, MIP, and QP are supported today. Routing (VRP, TSP, PDP, and related APIs) over gRPC is not available yet; support is planned for an upcoming release. For remote routing today, use the HTTP/JSON REST self-hosted server.

This is not the HTTP/JSON REST self-hosted server (FastAPI). REST is for arbitrary HTTP clients; gRPC serves remote execution (client integrated into the solver APIs) and explicit gRPC clients.

When to choose which path

• Remote execution — drop-in remote solves with no code changes; same scripts and APIs as a local solve.

• Python async gRPC client — explicit job control: submit now, wait or poll later, cancel, stream solver logs, stream MIP incumbents.

• Custom ``CuOptRemoteService`` client — non-Python (or fully custom) integrations that speak the protos directly. See gRPC API (Reference).

Start with Quick Start (install, server, and a minimal LP). Use Python Async gRPC Client for the Python gRPC client; Advanced Configuration for TLS, Docker, environment variables, and troubleshooting; Examples for additional patterns.

In this section

• Quick Start
  • How Remote Execution Works
  • Install NVIDIA cuOpt
  • Run the gRPC Server (GPU Server)
  • Minimal Python Example
  • Minimal cuopt_cli Example (LP)
  • Next Steps
• Python Async gRPC Client
  • Prerequisites
  • Connect and Solve
  • Variable Names
  • TLS / mTLS
  • Next steps
• Python Async gRPC Client Examples
  • Log Streaming
  • Incumbent Streaming (MIP)
  • See Also
• Python Async gRPC Client API Reference
  • Client
  • Supporting Types
  • Exceptions
  • See also
• Advanced Configuration
  • Configuration Parameters
  • Usage
  • Limitations and Scope
  • Troubleshooting
  • Further Reading
• Examples
  • Where to Find Examples
  • More Samples
  • REST vs gRPC
• gRPC API (Reference)
  • Service: CuOptRemoteService
  • Messages and Constraints
  • Further Reading
• gRPC Server Behavior
  • Process model
  • Job lifecycle (summary)
  • Job states
  • Logs, capacity, and workers
  • Fault tolerance and cancellation
  • Further reading

See System Requirements for GPU, CUDA, and OS requirements.