---

name: sdk description: "Use MMDeploy SDK model directories, runtime FFIs, profiler output, and packaged SDK distributions for inference." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

MMDeploy SDK Router

Use this sub-skill when the user already has, or needs to consume, an MMDeploy SDK model directory produced with --dump-info; when they ask how to run mmdeploy_runtime from Python; when they need C, C++, Java, or C# SDK inference patterns; when they have SDK profiler output; or when they need safe guidance on precompiled SDK/runtime packages.

Do not use this sub-skill for conversion internals, deployment config authoring, backend toolkit installation, custom-op builds, regression automation, or metric benchmarking. Route those tasks to the owning conversion, backend, extensibility, or validation guidance, and return here only for the SDK model directory/runtime handoff.

Read These First

• SDK workflows — --dump-info handoff, Python and C/C++ API patterns, language demo coverage, and FFI task mapping.
• Model directory — required JSON files, backend artifacts, zip-model support, and validation checks for SDK model packages.
• Profiler and packaging — SDK profiler data format, bundled analyzer usage, precompiled package concepts, and safe packaging caveats.
• Troubleshooting — symptoms, likely causes, recovery steps, and stop conditions for SDK runtime failures.
• SDK profile analyzer — read-only analyzer for SDK profiler text files.

Route The User Request

1. Clarify the artifact. SDK runtime APIs consume a model directory or a zip model built for SDK loading. A raw backend file such as end2end.engine, end2end.onnx, end2end.xml, or end2end.param is not enough for mmdeploy_runtime task classes because preprocessing and postprocessing live in the SDK metadata JSON files.

2. Check the model package before writing runtime code. At minimum, the directory should contain deploy.json, pipeline.json, detail.json, and every backend file named by deploy.json. Use Model directory for a deterministic checklist.

3. Pick the runtime FFI from the user environment.

   • Python: import mmdeploy_runtime and create the appropriate task class, usually Detector, Classifier, Segmentor, Restorer, TextDetector, TextRecognizer, PoseDetector, RotatedDetector, or VideoRecognizer.
   • C: create a model or task handle, convert input images to mmdeploy_mat_t, call *_apply, release results, and destroy handles.
   • C++: use mmdeploy::Model, mmdeploy::Device or mmdeploy::Context, then construct mmdeploy::<Task> and call Apply.
   • Java/C#: instantiate the generated task wrapper with (modelPath, deviceName, deviceId) and keep required native libraries discoverable.

4. Interpret device arguments literally. Python runtime constructors take a device_name such as "cpu" or "cuda" plus a numeric device_id. Do not pass a converter-style backend_files list to SDK task classes, and do not pass only an engine file as model_path.

5. For profiler questions, route directly to the analyzer. If the file is a text SDK profile with a graph section, a ---- separator, and event lines, run:

   python scripts/sdk_analyze.py profiler_data.txt
   

   Then use Profiler and packaging to interpret occupy, usage, n_call, t_mean, t_50%, and t_90%.

6. For precompiled package questions, distinguish usage from release builds. Installing an existing runtime wheel/package is a user workflow. Building release packages is a maintainer workflow that depends on Python ABI, platform, device, backend libraries, CMake flags, and licensing constraints.

Minimal SDK Runtime Checks

Use these only as read-only probes; they do not prove a backend engine is valid:

python - <<'PY'
import importlib.util
print('mmdeploy_runtime:', importlib.util.find_spec('mmdeploy_runtime') is not None)
PY

from mmdeploy.backend.sdk import is_available
print(is_available())  # True only when the Python SDK runtime module is visible

If either check is false, do not continue with SDK inference code until the runtime package or built SDK Python API is available.

Boundaries And Handoffs

• If --dump-info was omitted or the SDK JSON files are missing, hand the user back to conversion guidance to regenerate the model directory with SDK info.
• If mmdeploy_runtime imports but backend shared libraries or device runtimes are missing, use backend guidance for the target backend/device before trying another API layer.
• If the user asks for model accuracy, dataset metrics, latency benchmarking, or regression matrix automation, use validation guidance. This route only covers SDK inference and SDK profiler interpretation.
• If the user asks to modify converters, rewrite transforms, or add new SDK task support, use extensibility guidance; this route only consumes the emitted SDK package.