mmdeploy/tools/package_tools

Precompiled package

This document is going to describe the way to build MMDeploy package.

Prerequisites

• Download and install Miniconda from the official website.

• Create conda environments for python 3.6, 3.7, 3.8 and 3.9, respectively.

  for PYTHON_VERSION in 3.6 3.7 3.8 3.9
  do
    conda create --name mmdeploy-$PYTHON_VERSION python=$PYTHON_VERSION -y
  done
  

• Prepare MMDeploy dependencies

  Please follow the build-on-Linux guide or build-on-Windows guide to install dependencies of MMDeploy, including PyTorch, MMCV, OpenCV, ppl.cv, ONNX Runtime and TensorRT.

  Make sure the environment variables pplcv_DIR, ONNXRUNTIME_DIR, TENSORRT_DIR, CUDNN_DIR and CUDA_TOOLKIT_ROOT_DIR are exported.

Run precompiled command

• On Linux platform,

  conda activate mmdeploy-3.6
  pip install pyyaml
  cd the/root/path/of/mmdeploy
  python tools/package_tools/mmdeploy_builder.py tools/package_tools/configs/linux_x64.yaml .
  

  You will get the precompiled packages mmdeploy-{version}-linux-x86_64-cuda11.1-tensorrt8.2.3.0 and mmdeploy-{version}-linux-x86_64-onnxruntime1.8.1 in the current directory if everything's going well.

• On Windows platform, open Anaconda Powershell Prompt from the start menu and execute:

  conda activate mmdeploy-3.6
  pip install pyyaml
  cd the/root/path/of/MMDeploy
  python tools/package_tools/mmdeploy_builder.py tools/package_tools/configs/windows_x64.yaml .
  

  When the build procedure finishes successfully, you will find mmdeploy-{version}-windows-amd64-cuda11.1-tensorrt8.2.3.0 and mmdeploy-{version}-windows-amd64-onnxruntime1.8.1 precompiled packages in the current directory.