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name: mmdeploy description: "Route MMDeploy model deployment, backend setup, SDK runtime, extensibility, and validation workflows for OpenMMLab/PyTorch models." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

MMDeploy repo skill

Use this repo skill when a task is about deploying PyTorch/OpenMMLab computer-vision models with MMDeploy: converting checkpoints to backend artifacts, preparing or diagnosing inference backends, using SDK model directories, extending export behavior, or validating/profiling exported models.

Read repo provenance before relying on version-sensitive behavior or refreshing this skill. Read root troubleshooting for cross-cutting install/import/version problems before entering a workflow-specific troubleshooting page.

Minimal public setup

Install MMDeploy in an environment that matches the target OpenMMLab codebase and selected backend. Do not install every optional backend by default.

python -m pip install -r requirements/runtime.txt -r requirements/optional.txt
python -m pip install -r requirements/build.txt
python -m pip install . --no-build-isolation
python - <<'PY'
import mmdeploy
from mmdeploy.backend.base import get_backend_manager
print('mmdeploy', mmdeploy.__version__)
print('torchscript backend available:', get_backend_manager('torchscript').is_available())
PY

Backend-specific packages, vendor toolkits, custom ops, SDK runtime libraries, and hardware drivers are selected per route; a CPU import is not proof that TensorRT, RKNN, Ascend, SNPE, VACC, or SDK runtime execution works.

Route map

User intent	Load this route	First files to read
Convert a model/checkpoint to ONNX, TorchScript, TensorRT, NCNN, OpenVINO, SDK metadata, or another backend artifact	conversion	conversion workflows, configuration, bundled deploy.py
Choose, install, check, or troubleshoot backend packages, converter tools, custom-op libraries, or hardware/toolkit readiness	backends	backends backend matrix, custom ops, bundled check_env.py
Run an SDK model directory with mmdeploy_runtime, inspect SDK JSON metadata, use C/C++/Java/C# runtime patterns, or analyze SDK profiler output	sdk	sdk SDK workflows, model directory, bundled sdk_analyze.py
Modify MMDeploy internals: rewriters, symbolic overrides, backend/codebase/task support, custom ops, partition marks, or developer tests	extensibility	extensibility rewriters, backend/codebase support, testing
Evaluate an exported model, profile latency, run regression matrices, or generate supported backend/model tables	validation	validation evaluation, profiling, regression

Operating sequence

1. Identify the user's concrete artifact and goal: source checkpoint/config, deployment config, backend target, SDK model directory, backend file set, profiler report, or extension point.
2. Route to exactly one owning sub-skill first. Use sibling sub-skills only at explicit handoff boundaries, such as conversion failing due to missing TensorRT, or SDK runtime failing because a backend library is absent.
3. Prefer bundled helpers in the selected sub-skill over source-repo tools: future agents should run sub-skills/<id>/scripts/..., not tools from an original checkout.
4. Keep backend claims scoped to the installed runtime. Optional backend guidance is evidence-backed, but actual runtime success requires the matching backend package/toolkit/hardware.
5. For validation or regression, avoid full matrices, downloads, or accelerator runs unless the user explicitly asks and the environment is ready.

Common handoffs

• Conversion produced deploy.json, pipeline.json, and detail.json: continue with sdk for runtime use.
• Conversion exported IR but backend conversion failed: continue with backends using the failing backend_config.type and error message.
• A backend model exists and the user asks for accuracy or speed: continue with validation, not SDK runtime guidance.
• A model cannot export because a PyTorch/MMCV op is unsupported or partition marks are missing: continue with extensibility.
• A user asks for general OpenMMLab model training or dataset preparation before deployment: use the relevant upstream codebase skill/documentation; this skill starts at MMDeploy deployment and validation workflows.

Verification status

The generated skill was built for MMDeploy 1.3.1 source evidence. The production environment verified core package importability, API inspection, and CPU TorchScript backend-manager availability. ONNXRuntime, TensorRT, NCNN, OpenVINO, PPLNN, RKNN, Ascend, CoreML, TVM, VACC, SNPE, and mmdeploy_runtime SDK execution are optional/document-backed routes unless the user's current environment verifies them.