---

name: runtime-deployment description: "Choose and prepare WeNet runtime deployment paths for libtorch, ONNX Runtime, OpenVINO, mobile, web, GPU/Triton/TensorRT, IPEX, BPU, and XPU targets." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

WeNet Runtime Deployment

Use this sub-skill when the user wants to deploy a trained/exported WeNet model outside ordinary Python package transcription: C++ libtorch, ONNX Runtime, OpenVINO, Android, iOS, web demos, Raspberry Pi, GPU Triton/TensorRT, Intel IPEX, Horizon BPU, or Kunlun XPU.

Start here

1. Identify the target platform and inference engine.

2. If the model is not exported yet, route to ../model-export/SKILL.md.

3. Use the runtime chooser to list expected artifacts and prerequisites:

   python sub-skills/runtime-deployment/scripts/choose_runtime.py \
     --platform linux --backend libtorch
   

4. Read references/runtime-platforms.md for the platform matrix, U2 streaming concepts, artifact mapping, and deployment templates.

5. Read references/troubleshooting.md for build/toolchain, artifact mismatch, streaming, service endpoint, GPU, mobile, and LM/context graph failures.

Route by task

• Export JIT/ONNX/vendor artifacts with ../model-export/SKILL.md.
• Train or decode experiments with ../training-and-decoding/SKILL.md.
• Use simple installed-package transcription with ../package-transcription/SKILL.md.

Safety boundary

Runtime builds can download SDKs, compile C++ code, start services, use GPUs, or require mobile/vendor toolchains. Do not run builds or servers unless the user authorizes platform-specific dependencies, hardware, network, ports, storage, and runtime duration. The bundled chooser is safe and only reports requirements.