    Prerequisites
        Linux Host System Compilation Tools
        Windows Host System Compilation Tools
        MacOS Host System Compilation Tools
        Android Target System Cross-Compilation Tools
        Linux-AArch64 Target System Cross-Compilation Tools
        iOS Target System Cross-Compilation Tools
        Tools
    Download and Build Bolt
    Common install problem

Prerequisites

Linux Host System Compilation Tools

CMake

Download and install Cmake from https://cmake.org/download/. Set shell environment variable PATH.
GNU make

Download and install make from https://ftp.gnu.org/gnu/make/. Set shell environment variable PATH.
Wget

Download and install Wget from https://www.gnu.org/software/wget/. Set shell environment variable PATH.

Windows Host System Compilation Tools

Git Shell

Download and install Git Shell from https://gitforwindows.org/. Set system environment variable PATH.
CMake

Download and install Cmake from https://cmake.org/download/. Set system environment variable PATH.
Wget

Download and install Wget from https://eternallybored.org/misc/wget/. Set system environment variable PATH.
MinGW toolchains(mingw32-make, gcc, g++)

Download and install Mingw32-w64 from https://udomain.dl.sourceforge.net/project/mingw-w64/Toolchains%20targetting%20Win64/Personal%20Builds/mingw-builds/6.4.0/threads-posix/seh/x86_64-6.4.0-release-posix-seh-rt_v5-rev0.7z. Set system environment variable PATH.

MacOS Host System Compilation Tools

Xcode

Download and install Xcode.

Android Target System Cross-Compilation Tools

Android NDK

Refer to the NDK installation example to install android-ndk-r20 and set shell environment variable ANDROID_NDK_ROOT.
```
  export ANDROID_NDK_ROOT=/data/opt/android-ndk-r20
```

Linux-AArch64 Target System Cross-Compilation Tools

Cross compiler

Install gcc-arm-8.3-2019.03-x86_64-aarch64-linux-gnu and set shell environment PATH.

iOS Target System Cross-Compilation Tools

Cross compiler for Linux

Here is an tutorial for building toolchains.

Cross compiler for MacOS

You can use MacOS's clang to build. Only need to set shell environment IOS_SDK_ROOT to iPhoneOS.sdk.

  export IOS_SDK_ROOT=/Applications/Xcode.app/Contents/Developer/Platforms/iPhoneOS.platform/Developer/SDKs/iPhoneOS.sdk

Tools

Android adb

Refer to the ADB installation example to install ADB tool helping you transfer the executables to android mobile phones.
```
export PATH=/data/opt/platform-tools:$PATH
```
Android dx

If you want to directly run jar file on Android device, you can use Android dx tool. Install Android v28.0.3 build tools and set shell environment PATH.

JDK

If you want to use Java API without Android NDK, you need to install JDK.

Download and install OpenJDK and set shell environment PATH and JNI_ROOT.
```
export JNI_ROOT=/data/opt/openjdk-16_windows-x64_bin
export PATH=${JNI_ROOT}/bin:$PATH
```

Download and Build Bolt

A simple shell script install.sh is provided to build and install the Bolt library, and you should modify it according to your scenario and environment. --target option is requireed to set target deployment platform. Use help message to find more useful information. There are some commn used build command.

We will install Bolt to install_[target] directory. These subdirectories will be found in it:

include
- C API header file
- Java API class file
lib
- libBoltModel.so: build for Java application
- libbolt.so: build for C/C++ application
- libflow.so: flow sub project library, when using --flow option
- libengine.so: inference sub project library
- libtensor.so: tensor computing sub project library
- libimage.so: image sub project library
- libblas_enhance.so: blas_enhance sub project library
- libmodel_tools*.so: model_tools sub project library
- libmodel_spec.so: model_spec sub project library
- libuni.so: uni sub project library
tools
- X2bolt for generally converting deep learning(caffe/onnx/tflite/tensorflow) model to bolt model
- post_training_quantization for generating bolt int8 inference model
- tensorflow2caffe for converting tensorflow model to caffe model
- pytorch2caffe for converting pytorch model to caffe model
tests
- operator unit test
examples
- benchmark for measuring inference performance of bolt model
  
  These examples will be build when using --example option
- classification for imagenet classification task，c_image_classifification is a simplified C API version
- u2net for object detection
- ultra_face for face detection
- tinybert and tinybert_onnx for tinybert intention identification
- nmt for machine translation
- asr_rnnt for automatic speech recognition task (RNNT model)
- asr_convolution_transformer for automatic speech recognition task (Convolution+Transformer model)
- vad for ASR pinyin to chinese
- tts for text to speech
- c_input_method for input method

Common install problem

wget error

Use wget to download file. If you use proxy to access the network, you may be reminded to add --no-check-certificate flag when using wget.
- Download is limited by network proxy or time consuming.
You can download these files and save to a specified directory, Bolt will automatically use it.
1. save Linux protoc https://github.com/protocolbuffers/protobuf/releases/download/v3.14.0/protoc-3.14.0-linux-x86_64.zip or Windows protoc https://github.com/protocolbuffers/protobuf/releases/download/v3.14.0/protoc-3.14.0-win64.zip or MacOS protoc https://github.com/protocolbuffers/protobuf/releases/download/v3.14.0/protoc-3.14.0-osx-x86_64.zip to third_party/sources/ directory.
2. save https://github.com/protocolbuffers/protobuf/archive/v3.14.0.tar.gz to third_party/sources/protobuf-3.14.0.tar.gz.
3. save https://github.com/google/flatbuffers/tree/master/include to third_party/sources/flatbuffers/include.
4. save https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/schema/schema_generated.h third_party/sources/tflite/include/tensorflow/lite/schema/schema_generated.h.
5. save https://github.com/open-source-parsers/jsoncpp/archive/refs/tags/1.9.4.zip to third_party/sources/jsoncpp-1.9.4.zip.
6. optional. save https://github.com/KhronosGroup/OpenCL-Headers/tree/master/CL to third_party/sources/opencl/include/CL* when using ARM MALI/Qualcomm GPU.
7. optional. save http://www.ijg.org/files/jpegsrc.v9c.tar.gz to third_party/sources/jpegsrc.v9c.tar.gz when using example.
8. optional. save https://codeload.github.com/anthonix/ffts/zip/master to third_party/sources/ffts-master.zip when using Flow.
9. optional. save https://github.com/opencv/opencv/archive/refs/tags/4.5.2.zip to third_party/sources/opencv-4.5.2.zip when using face detection example.

MinGW version error

Third party library protobuf use some POSIX standard system library. If you don't use POSIX version mingw, you may encounter these errors. You can download this link to download POSIX version mingw. https://udomain.dl.sourceforge.net/project/mingw-w64/Toolchains%20targetting%20Win64/Personal%20Builds/mingw-builds/6.4.0/threads-posix/seh/x86_64-6.4.0-release-posix-seh-rt_v5-rev0.7z

error: 'mutex' in namespace 'std' does not name a type
 std::mutex mu_;
error: 'once_flag' in namespace 'std' does not name a type
 using once_flag = std::once_flag;
error: 'call_once' is not a member of 'std'
 std::call_once(std::forward<Args>(args)...);
error: 'strtoll' was not declared in this scope
 return strtoll(nptr, endptr, base);
error: 'thread' is not a member of 'std'
 static std::atomic<std::thread::id> runner;

Don't want to use third party library or model conversion tools.

Third party library are used in model conversion tools. If you don't want to use it, you can close it by using --converter=OFF option. This will not build third party library.
Only want to use partial model conversion tools.

You can implement it by modifying install.sh. for example, there are some cmake options, such as -DUSE_CAFFE=ON for Caffe.
Only want to use partial inference precision.

You can implement it by change --fp32, --fp16 and --int8 option setting.
Can not build success with special compiler(such as MinGW)

You may encounter various compilation problem, this maybe caused by compiler or others. Here is an example.
```
mingw64\bin\ar.exe: unable to rename 'CMakeFiles\test_softmax.dir/objects.a'; reason: File exists
```
You can enter build directory build_[target] and continuously run make install. This may complete all compilation step by step.
Parallel build error

Bolt use -t option to set use parallel build, and default value is 8. If you encounter unknown error, you can try to set -t 1 to use serial build.

Install

Contents

Prerequisites

Linux Host System Compilation Tools

CMake

GNU make

Wget

Windows Host System Compilation Tools

Git Shell

CMake

Wget

MinGW toolchains(mingw32-make, gcc, g++)

MacOS Host System Compilation Tools

Xcode

Android Target System Cross-Compilation Tools

Android NDK

Linux-AArch64 Target System Cross-Compilation Tools

Cross compiler

iOS Target System Cross-Compilation Tools

Cross compiler for Linux

Cross compiler for MacOS

Tools

Android adb

Android dx

JDK

Download and Build Bolt

Common install problem

wget error

Download is limited by network proxy or time consuming.

MinGW version error

Don't want to use third party library or model conversion tools.

Only want to use partial model conversion tools.

Only want to use partial inference precision.

Can not build success with special compiler(such as MinGW)

Parallel build error

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