Build for macOS-arm64¶

Build for macOS-arm64

Install Toolchains¶

cmake
```
brew install cmake
```
clang

install Xcode or Command Line Tools
```
xcode-select --install
```

Install Dependencies¶

Install Dependencies for Model Converter¶

Please refer to get_started to install conda.

# install pytorch & mmcv
conda install pytorch==1.9.0 torchvision==0.10.0 -c pytorch
pip install -U openmim
mim install mmengine
mim install "mmcv>=2.0.0rc2"

Install Dependencies for SDK¶

You can skip this chapter if you are only interested in the model converter.

NAME	INSTALLATION
OpenCV (>=3.0)	`brew install opencv`

Install Inference Engines for MMDeploy¶

Both MMDeploy’s model converter and SDK share the same inference engines.

You can select you interested inference engines and do the installation by following the given commands.

This document focus on Core ML. The installation of ONNX Runtime, ncnn and TorchScript is similar to the linux platform, please refer to the document linux-x86_64 for installation.

The TorchScript model is used as the IR in the conversion process of the Core ML model. In order to support the custom operator in some models like detection models in mmdet, libtorch needs to be installed.

NAME PACKAGE INSTALLATION

Core ML

coremltools


pip install coremltools==6.3

TorchScript

libtorch

1. Libtorch doesn't provide prebuilt arm library for macOS, so you need to compile it yourself. Please note that the version of libtorch must be consistent with the version of pytorch.
2. Take LibTorch 1.9.0 as an example. You can install it like this:


git clone -b v1.9.0 --recursive https://github.com/pytorch/pytorch.git
cd pytorch
mkdir build && cd build
cmake .. \
    -DCMAKE_BUILD_TYPE=Release \
    -DPYTHON_EXECUTABLE=`which python` \
    -DCMAKE_INSTALL_PREFIX=install \
    -DDISABLE_SVE=ON # low version like 1.9.0 of pytorch need DISABLE_SVE option
make -j4 && make install
export Torch_DIR=$(pwd)/install/share/cmake/Torch

Build MMDeploy¶

cd /the/root/path/of/MMDeploy
export MMDEPLOY_DIR=$(pwd)

Build Model Converter¶

Core ML

Core ML uses torchscript as IR, to convert models in some codebases like mmdet, you need to compile torchscript custom operators

torchscript custom operators

cd ${MMDEPLOY_DIR}
mkdir -p build && cd build
cmake -DMMDEPLOY_TARGET_BACKENDS=coreml -DTorch_DIR=${Torch_DIR} ..
make -j4 && make install

Please check cmake build option.

Install Model Converter¶

# You should use `conda install` to install the grpcio in requirements/runtime.txt
conda install grpcio

cd ${MMDEPLOY_DIR}
mim install -v -e .

Note

Some dependencies are optional. Simply running pip install -e . will only install the minimum runtime requirements. To use optional dependencies, install them manually with pip install -r requirements/optional.txt or specify desired extras when calling pip (e.g. pip install -e .[optional]). Valid keys for the extras field are: all, tests, build, optional.

Build SDK and Demo¶

The following shows an example of building an SDK using Core ML as the inference engine.

cpu + Core ML

cd ${MMDEPLOY_DIR}
mkdir -p build && cd build
cmake .. \
    -DMMDEPLOY_BUILD_SDK=ON \
    -DMMDEPLOY_BUILD_EXAMPLES=ON \
    -DMMDEPLOY_BUILD_SDK_PYTHON_API=ON \
    -DMMDEPLOY_TARGET_DEVICES=cpu \
    -DMMDEPLOY_TARGET_BACKENDS=coreml \
    -DTorch_DIR=${Torch_DIR}

make -j4 && make install