Turning OpenMMLab's PyTorch models into edge-ready binaries

What it does

MMDeploy is the official deployment toolkit for the OpenMMLab ecosystem. It takes models trained in PyTorch-based libraries like mmdetection, mmsegmentation, and mmpose and exports them to a wide range of inference backends—ONNX, TensorRT, NCNN, CoreML, and others—so they can run on x86, ARM, NVIDIA GPUs, Apple Silicon, Rockchip, Huawei Ascend, and even RISC-V. A C/C++ SDK handles the runtime plumbing, including image transforms and postprocessing.

The interesting bit

The real work isn’t the conversion itself; it’s the maintenance of a massive compatibility matrix spanning nine OpenMMLab codebases and a dozen hardware targets. The project treats Android Hexagon DSP and Linux RISC-V as first-class citizens alongside standard CUDA servers, which is unusual for a research-centric framework.

Key highlights

• Supports nine OpenMMLab codebases, including detection, segmentation, OCR, pose estimation, and 3D detection.
• Targets a broad hardware spectrum: NVIDIA GPUs and Jetson, Apple M1 via CoreML, Qualcomm via SNPE, Huawei Ascend via CANN, Rockchip via RKNN, and RISC-V.
• Provides a modular C/C++ SDK with pluggable components for pre-processing, inference, and post-processing.
• Version 1.x aligns with OpenMMLab 2.0; the older 0.x branch is deprecated.

Caveats

• Several backend-platform combinations show “no status” in the project’s own CI matrix—notably OpenVINO and TVM on Linux x86, LibTorch on NVIDIA Linux, and TensorRT on Jetson—so your mileage may vary.
• Windows support for CPU inference appears largely untested in CI relative to Linux.

Verdict

If you are already invested in OpenMMLab’s training stack and need to ship to edge or embedded devices without rewriting your pipeline, this is the sanctioned off-ramp. If you use a different training framework, the value drops sharply.