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Android TVM Demo

This folder contains Android Demo app that allows us to show how to deploy model using TVM runtime api on a Android phone.

You will need JDK, Android SDK, Android NDK and an Android device to use this. Make sure the ANDROID_HOME variable already points to your Android SDK folder or set it using export ANDROID_HOME=[Path to your Android SDK, e.g., ~/Android/sdk]. We use Gradle to build. Please follow the installation instruction for your operating system.

Alternatively, you may execute Docker image we provide wich contains the required packages. Use the command below to build the image and enter interactive session. Note, that building with OpenCL was not tested from Docker.

./docker/ demo_android -it bash
(docker) $ echo $ANDROID_HOME
(docker) /opt/android-sdk-linux

Build and Installation

Build APK

Before you build the Android application, please refer to TVM4J Installation Guide and install tvm4j-core to your local maven repository. You can find tvm4j dependency declare in app/build.gradle. Modify it if it is necessary.

dependencies {
    compile fileTree(dir: 'libs', include: ['*.jar'])
    androidTestCompile('', {
        exclude group: '', module: 'support-annotations'
    compile ''
    compile ''
    compile ''
    compile 'ml.dmlc.tvm:tvm4j-core:0.0.1-SNAPSHOT'
    testCompile 'junit:junit:4.12'

Application default has CPU version TVM runtime flavor and follow below instruction to setup. In app/src/main/jni/make you will find JNI Makefile config and copy it to app/src/main/jni and modify it.

cd apps/android_deploy/app/src/main/jni
cp make/ .

Here's a piece of example for

APP_ABI = arm64-v8a

APP_PLATFORM = android-17

# whether enable OpenCL during compile

Now use Gradle to compile JNI, resolve Java dependencies and build the Android application together with tvm4j. Run following script to generate the apk file.

cd apps/android_deploy
gradle clean build

In app/build/outputs/apk you'll find app-release-unsigned.apk, use dev_tools/ to generate a signature and use dev_tools/ to get the signed apk file app/build/outputs/apk/tvmdemo-release.apk.

Upload tvmdemo-release.apk to your Android device and install it.

Build with OpenCL

Application does not link with OpenCL library unless you configure it to. Modify JNI Makefile config app/src/main/jni with proper target OpenCL configuration.

Here's a piece of example for

APP_ABI = arm64-v8a

APP_PLATFORM = android-17

# whether enable OpenCL during compile

# the additional include headers you want to add, e.g., SDK_PATH/adrenosdk/Development/Inc
ADD_C_INCLUDES = /opt/adrenosdk-osx/Development/Inc

# the additional link libs you want to add, e.g., ANDROID_LIB_PATH/

Note that you should specify the correct GPU development headers for your android device. Run adb shell dumpsys | grep GLES to find out what GPU your android device uses. It is very likely the library ( is already present on the mobile device. For instance, I found it under /system/vendor/lib64. You can do adb pull /system/vendor/lib64/ ./ to get the file to your desktop.

After you setup the, follow the instructions in Build APK to build the Android package with OpenCL flavor.

Cross Compile and Run on Android Devices

Architecture and Android Standalone Toolchain

In order to cross compile a shared library (.so) for your android device, you have to know the target triple for the device. (Refer to Cross-compilation using Clang for more information). Run adb shell cat /proc/cpuinfo to list the device's CPU information.

Now use NDK to generate standalone toolchain for your device. For my test device, I use following command.

cd /opt/android-ndk/build/tools/
./ --platform=android-24 --use-llvm --arch=arm64 --install-dir=/opt/android-toolchain-arm64

If everything goes well, you will find compile tools in /opt/android-toolchain-arm64/bin. For example, bin/aarch64-linux-android-g++ can be used to compile C++ source codes and create shared libraries for arm64 Android devices.

Place compiled model on Android application assets folder

Follow instruction to get compiled version model for android target here.

Copied these compiled model, deploy_graph.json and deploy_param.params to apps/android_deploy/app/src/main/assets/ and modify TVM flavor changes on java

CPU Verison flavor

    private static final boolean EXE_GPU            = false;

OpenCL Verison flavor

    private static final boolean EXE_GPU            = true;

Install compiled android application on phone and enjoy the image classifier demo using extraction model

You can define your own TVM operators and deploy via this demo application on your Android device to find the most optimized TVM schedule.

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