# Build support status
## Host build
* Windows build (cpu and gpu)
* Linux build   (cpu and gpu)
* MacOS build   (cpu only)

## Cross build
* Windows cross build ARM-Android (ok)
* Windows cross build ARM-Linux   (ok)
* Linux cross build ARM-Android   (ok)
* Linux cross build ARM-Linux     (ok)
* MacOS cross build ARM-Android   (ok)
* MacOS cross build ARM-Linux     (ok but experimental)
* MacOS cross build IOS           (ok)

# Build env prepare
## Prerequisites

Most of the dependencies of MegBrain(MegEngine) are located in [third_party](third_party) directory, which can be prepared by executing:

```bash
./third_party/prepare.sh
./third_party/install-mkl.sh
```
Windows shell env(bash from windows-git), infact if you can use git command on Windows, which means you always install bash.exe at the same dir of git.exe, find it, then you can prepare third-party code by

* command:
```
bash.exe ./third_party/prepare.sh
bash.exe ./third_party/install-mkl.sh
if you are use github MegEngine and build for Windows XP, please
	1: donwload mkl for xp from: http://registrationcenter-download.intel.com/akdlm/irc_nas/4617/w_mkl_11.1.4.237.exe
	2: install exe, then from install dir:
		2a: cp include file to third_party/mkl/x86_32/include/
		2b: cp lib file to third_party/mkl/x86_32/lib/
```

But some dependencies need to be installed manually:

* [CUDA](https://developer.nvidia.com/cuda-toolkit-archive)(>=10.1), [cuDNN](https://developer.nvidia.com/cudnn)(>=7.6) are required when building MegBrain with CUDA support.
* [TensorRT](https://docs.nvidia.com/deeplearning/sdk/tensorrt-archived/index.html)(>=5.1.5) is required when building with TensorRT support.
* LLVM/Clang(>=6.0) is required when building with Halide JIT support.
* Python(>=3.5) and numpy are required to build Python modules.
## Package install
### Windows host build
* commands:
```
1: install git (Windows GUI)
	* download git-install.exe from https://git-scm.com/download/win
	* only need choose git-lfs component
	* install to default dir:  /c/Program\ Files/Git
2: install visual studio 2019 Enterprise (Windows GUI)
	* download install exe from https://visualstudio.microsoft.com
	* choose "c++ develop" -> choose cmake/MSVC/cmake/windows-sdk when install
	* NOTICE: windows sdk version >=14.28.29910 do not compat with CUDA 10.1, please
		choose version < 14.28.29910
	* then install choosed components
3: install LLVM from https://releases.llvm.org/download.html (Windows GUI)
    * llvm install by Visual Studio have some issue, eg, link crash on large project, please use official version
    * download install exe from https://releases.llvm.org/download.html
	* our ci use LLVM 12.0.1, if u install other version, please modify LLVM_PATH
    * install 12.0.1 to /c/Program\ Files/LLVM_12_0_1
4: install python3 (Windows GUI)
	* download python 64-bit install exe (we support python3.5-python3.8 now)
	     https://www.python.org/ftp/python/3.5.4/python-3.5.4-amd64.exe
	     https://www.python.org/ftp/python/3.6.8/python-3.6.8-amd64.exe
	     https://www.python.org/ftp/python/3.7.7/python-3.7.7-amd64.exe
	     https://www.python.org/ftp/python/3.8.3/python-3.8.3-amd64.exe
	* install 3.5.4 to /c/Users/${USER}/mge_whl_python_env/3.5.4
	* install 3.6.8 to /c/Users/${USER}/mge_whl_python_env/3.6.8
	* install 3.7.7 to /c/Users/${USER}/mge_whl_python_env/3.7.7
	* install 3.8.3 to /c/Users/${USER}/mge_whl_python_env/3.8.3
	* cp python.exe to python3.exe
		loop cd /c/Users/${USER}/mge_whl_python_env/*
		copy python.exe to python3.exe
	* install python depends components
		loop cd /c/Users/${USER}/mge_whl_python_env/*
		python3.exe -m pip install --upgrade pip
		python3.exe -m pip install -r imperative/python/requires.txt
		python3.exe -m pip install -r imperative/python/requires-test.txt
5: install cuda components (Windows GUI)
	* now we support cuda10.1+cudnn7.6+TensorRT6.0 on Windows
	* install cuda10.1 to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1
	* install cudnn7.6 to C:\Program Files\NVIDIA GPU Computing Toolkit\cudnn-10.1-windows10-x64-v7.6.5.32
	* install TensorRT6.0 to C:\Program Files\NVIDIA GPU Computing Toolkit\TensorRT-6.0.1.5
6: edit system env variables (Windows GUI)
	* create new key: "VS_PATH", value: "C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise"
	* create new key: "LLVM_PATH", value: "C:\Program Files\LLVM_12_0_1"
	* append "Path" env value
        C:\Program Files\Git\cmd
		C:\Users\build\mge_whl_python_env\3.8.3
		C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\bin
		C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\libnvvp
		C:\Program Files\NVIDIA GPU Computing Toolkit\cudnn-10.1-windows10-x64-v7.6.5.32\cuda\bin
		C:\Program Files\LLVM_12_0_1\lib\clang\12.0.1\lib\windows
```

### Linux host build
* commands:
```
1: install Cmake, which version >= 3.15.2, ninja-build
2: install gcc/g++, which version >= 6, (gcc/g++ >= 7, if need build training mode)
3: install build-essential git git-lfs gfortran libgfortran-6-dev autoconf gnupg flex bison gperf curl zlib1g-dev gcc-multilib g++-multilib lib32ncurses5-dev libxml2-utils xsltproc unzip libtool librdmacm-dev rdmacm-utils python3-dev python3-numpy texinfo
4: CUDA env(if build with CUDA), please export CUDA/CUDNN/TRT env, for example:
export CUDA_ROOT_DIR=/path/to/cuda
export CUDNN_ROOT_DIR=/path/to/cudnn
export TRT_ROOT_DIR=/path/to/tensorrt
```

### MacOS host build
* commands:
```
1: install Cmake, which version >= 3.15.2
2: install brew: /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install.sh)"
3: brew install python python3 coreutils ninja
4: install at least xcode command line tool: https://developer.apple.com/xcode/
5: about cuda: we do not support CUDA on MacOS
6: python3 -m pip install numpy (if you want to build with training mode)
```

### Cross build for ARM-Android
Now we support Windows/Linux/MacOS cross build to ARM-Android

* commands:
```
2: download NDK from https://developer.android.google.cn/ndk/downloads/ for diff OS platform package, suggested NDK20 or NDK21
3: export NDK_ROOT=NDK_DIR at bash-like env
```

### Cross build for ARM-Linux
Now we support ARM-Linux on Linux and Windows fully, also experimental on MacOS

* commands:
```
1: download toolchains from http://releases.linaro.org/components/toolchain/binaries/ or https://developer.arm.com/tools-and-software/open-source-software/developer-tools/gnu-toolchain/gnu-a/downloads if use Windows or Linux
2: download toolchains from https://github.com/thinkski/osx-arm-linux-toolchains if use MacOS
```

### Cross build for IOS
Now we only support cross build to IOS from MACOS

* commands:
```
1: install full xcode: https://developer.apple.com/xcode/
```

# How to build
## With bash env(Linux/MacOS/Windows-git-bash)

* host build just use scripts:scripts/cmake-build/host_build.sh
  builds MegBrain(MegEngine) that runs on the same host machine (i.e., no cross compiling)
  The following command displays the usage:
  ```
  scripts/cmake-build/host_build.sh -h
    more example:
    1a: build for Windows for XP (sp3): (dbg) EXTRA_CMAKE_ARGS="-DMGE_DEPLOY_INFERENCE_ON_WINDOWS_XP=ON" ./scripts/cmake-build/host_build.sh -m -d
                                        (opt) EXTRA_CMAKE_ARGS="-DMGE_DEPLOY_INFERENCE_ON_WINDOWS_XP=ON" ./scripts/cmake-build/host_build.sh -m
    2a: build for Windows for XP (sp2): (dbg) EXTRA_CMAKE_ARGS="-DMGE_DEPLOY_INFERENCE_ON_WINDOWS_XP_SP2=ON" ./scripts/cmake-build/host_build.sh -m -d
                                        (opt) EXTRA_CMAKE_ARGS="-DMGE_DEPLOY_INFERENCE_ON_WINDOWS_XP_SP2=ON" ./scripts/cmake-build/host_build.sh -m
  ```
* cross build to ARM-Android: scripts/cmake-build/cross_build_android_arm_inference.sh
  builds MegBrain(MegEngine) for inference on Android-ARM platforms.
  The following command displays the usage:
  ```
  scripts/cmake-build/cross_build_android_arm_inference.sh -h
  ```
* cross build to ARM-Linux:   scripts/cmake-build/cross_build_linux_arm_inference.sh
  builds MegBrain(MegEngine) for inference on Linux-ARM platforms.
  The following command displays the usage:
  ```
  scripts/cmake-build/cross_build_linux_arm_inference.sh -h
  ```
* cross build to IOS:         scripts/cmake-build/cross_build_ios_arm_inference.sh
  builds MegBrain(MegEngine) for inference on iOS (iPhone/iPad) platforms.
  The following command displays the usage:
  ```
  scripts/cmake-build/cross_build_ios_arm_inference.sh -h
  ```

## Visual Studio GUI(only for Windows host)

* command:
```
1: import megengine src to Visual Studio as a project
2: right click CMakeLists.txt, choose config 'cmake config' choose clang_cl_x86 or clang_cl_x64
3: config other CMAKE config, eg, CUDA ON OR OFF
```


# Other ARM-Linux-Like board support
It`s easy to support other customized arm-linux-like board, example:

* 1: HISI 3516/3519, infact u can just use toolchains from arm developer or linaro
then call scripts/cmake-build/cross_build_linux_arm_inference.sh to build a ELF
binary, or if you get HISI official toolschain, you just need modify CMAKE_CXX_COMPILER
and CMAKE_C_COMPILER in toolchains/arm-linux-gnueabi* to a real name

* 2: about Raspberry, just use scripts/cmake-build/cross_build_linux_arm_inference.sh
