huawei-cloud-cloudrobo-infer

Windows / PowerShell: Examples use bash syntax. To run on Windows PowerShell:

• Flatten \ line continuations to a single line, or end lines with a backtick.
• Set env vars with $env:NAME="value" instead of export NAME="value".
• Single-quoted JSON '{"a":"b"}' works as-is.

Overview

The cloudrobo-infer skill manages the full lifecycle of CloudRobo inference services (also called model-serving / inference-service). It lets the agent deploy a model (identified by model_id + model_version_id passed as a --model-json JSON object) into a long-running inference service, monitor and operate that service (start / stop / update / delete / logs), and use wait-deploy to poll until the service status is no longer DEPLOYING (status != "DEPLOYING"), at which point it returns any other state (e.g., RUNNING, FAILED, STOPPED).

Note: wait-deploy only waits on the DEPLOYING phase — it does NOT wait through CREATING. Read the Wait-Deploy reference for exact semantics.

Applicable scenarios:

• Model deployment — Deploy a model as an inference service (create → wait-deploy)
• Wait-Deploy — Poll every 5s until service status is no longer DEPLOYING (5s interval, 600s default)
• Service O&M — List deployed services, show detail, update config, start/stop
• Log diagnosis — list-logs with millisecond time range and keyword filter
• Cross-skill orchestration — Consume models exported by cloudrobo-train; expose the service to robo-dispatcher for embodied tasks (via infer_service_id).

Architecture:

Agent / LLM
    │
    ├── CLI  →  cloudrobo infer <command>
    └── SDK  →  InferClient (Python)
                    │
                    ▼
              cloudrobo-service (REST API)
              /v1/infer-services/*

All operations target the cloudrobo-service backend and require a valid workspace_id (default workspace is used unless explicitly provided). Deploying a service consumes compute (pool) resources — always confirm before starting a long-running service.