# Build LiteRT

## In this learning path

- [Introduction](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/)
- [Set up your development environment](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/1-prerequisites/)
- [Download and test the model](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/2-testing-model/)
- [Convert Stable Audio Open Small model to LiteRT](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/3-converting-model/)
- [Build LiteRT](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/4-building-litert/)
- [Create a simple program for Android target](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/5-creating-simple-program-for-android/)
- [Create a simple program for macOS target](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/6-creating-simple-program-for-macos/)
- [Next Steps](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/_next-steps/)

## LiteRT
LiteRT (short for Lite Runtime), formerly known as TensorFlow Lite, is Google’s high-performance runtime for on-device AI. Designed for low-latency, resource-efficient execution, LiteRT is optimized for mobile and embedded environments — making it a natural fit for Arm CPUs running models like Stable Audio Open Small. You’ll build the runtime using the Bazel build tool.

## Build LiteRT libraries
Clone the repository and get the latest modules
```bash
cd $WORKSPACE
git clone https://github.com/tensorflow/tensorflow.git tensorflow_src
cd tensorflow_src
```
Check out the specified commit of TensorFlow, and set the `TF_SRC_PATH`:
```bash
git checkout 84dd28bbc29d75e6a6d917eb2998e4e8ea90ec56
export TF_SRC_PATH=$(pwd)
```
A script is available to configure the `bazel` build environment. Run it to create a custom TFLite build for Android:

> Ensure the `NDK_PATH` variable is set to your previously installed Android NDK:
```bash
export NDK_PATH=$WORKSPACE/android-ndk-r25b/
export ANDROID_NDK_HOME=$NDK_PATH
export PATH=$NDK_PATH/toolchains/llvm/prebuilt/linux-x86_64/bin/:$PATH
```
```bash
export NDK_PATH=$WORKSPACE/android-ndk-r25b/
export ANDROID_NDK_HOME=$NDK_PATH
export PATH=$NDK_PATH/toolchains/llvm/prebuilt/darwin-x86_64/bin/:$PATH
```
The configuration script is interactive. Run it using the command below, and use the table to set the parameters for this Learning Path use-case.
```bash
python3 ./configure.py
```
| Question | Input |
|----------|-------|
| Please specify the location of python. [Default is $WORKSPACE/bin/python3]: | Enter (default) |
| Please input the desired Python library path to use[$WORKSPACE/lib/python3.10/site-packages] | Enter |
| Do you wish to build TensorFlow with ROCm support? [y/N] | N (No) |
| Do you wish to build TensorFlow with CUDA support? | N |
| Please specify optimization flags to use during compilation when bazel option “–config=opt” is specified [Default is -Wno-sign-compare]: | Enter |
| Do you want to use Clang to build TensorFlow? [Y/n] | N |
| Would you like to interactively configure ./WORKSPACE for Android builds? [y/N] | y (Yes) |
| Please specify the home path of the Android NDK to use. [Default is /home/user/Android/Sdk/ndk-bundle] | Enter |
| Please specify the (min) Android NDK API level to use. [Default is 21] | 27 |
| Please specify the home path of the Android SDK to use. [Default is /home/user/Android/Sdk] | Enter |
| Please specify the Android SDK API level to use. [Default is 35] | Enter |
| Please specify an Android build tools version to use. [Default is 35.0.0] | Enter |
| Do you wish to build TensorFlow with iOS support? [y/N]: | n |

Once the Bazel configuration is complete, you can build LiteRT for your target platform as follows:
```bash
bazel build -c opt --config android_arm64 //tensorflow/lite:libtensorflowlite.so \
    --define tflite_with_xnnpack=true \
    --define=xnn_enable_arm_i8mm=true \
    --define tflite_with_xnnpack_qs8=true \
    --define tflite_with_xnnpack_qu8=true
```
```bash
bazel build -c opt --config macos //tensorflow/lite:libtensorflowlite.so \
    --define tflite_with_xnnpack=true \
    --define xnn_enable_arm_i8mm=true \
    --define tflite_with_xnnpack_qs8=true \
    --define tflite_with_xnnpack_qu8=true
```
The final step is to build flatbuffers used by the application:
```bash
cd $WORKSPACE/tensorflow_src
mkdir flatc-native-build && cd flatc-native-build
cmake ../tensorflow/lite/tools/cmake/native_tools/flatbuffers
cmake --build .
```
Now that LiteRT and FlatBuffers are built, you’re ready to compile and deploy the Stable Audio Open Small inference application on your Android or macOS device.