Development tools for Android

LiteRT provides a number of tools for integrating models into Android apps. This page describes development tools for use in building apps with Kotlin, Java, and C++, as well as support for LiteRT development in Android Studio.

Tools for building with Kotlin and Java

The following sections describe development tools for LiteRT that use the Kotlin and Java languages.

LiteRT library

Use the LiteRT library in your Android app by adding the AAR hosted at MavenCentral to your development project.

You can specify this in your build.gradle dependencies as follows:

dependencies {
    ...
    implementation 'com.google.ai.edge.litert:+'
}

repositories {
    ...
    google()
}

If you use nightly snapshots, make sure you add the Sonatype snapshot repository to your project.

This AAR includes binaries for all of the Android ABIs. You can reduce the size of your application's binary by only including the ABIs you need to support.

Unless you are targeting specific hardware, you should omit the x86, x86_64, and arm32 ABIs in most cases. You can configure this with the following Gradle configuration. It specifically includes only armeabi-v7a and arm64-v8a, and should cover most modern Android devices.

android {
    defaultConfig {
        ndk {
            abiFilters 'armeabi-v7a', 'arm64-v8a'
        }
    }
}

To learn more about abiFilters, see Android ABIs in the Android NDK documentation.

LiteRT Support Library

The LiteRT Android Support Library makes it easier to integrate models into your application. It provides high-level APIs that help transform raw input data into the form required by the model, and interpret the model's output, reducing the amount of boilerplate code required.

It supports common data formats for inputs and outputs, including images and arrays. It also provides pre- and post-processing units that perform tasks such as image resizing and cropping.

Use the Support Library in your Android app by including the LiteRT Support Library AAR hosted at MavenCentral.

You can specify this in your build.gradle dependencies as follows:

dependencies {
    implementation 'com.google.ai.edge.litert:litert-support:+'
}

If you use nightly snapshots, make sure you add the Sonatype snapshot repository to your project.

For instructions on how to get started, see the LiteRT Android Support Library.

Using Android Studio

In addition to the development libraries described above, Android Studio also provides support for integrating LiteRT models, as described below.

Android Studio ML Model Binding

The ML Model Binding feature of Android Studio 4.1 and later allows you to import .tflite model files into your existing Android app, and generate interface classes to make it easier to integrate your code with a model.

To import a LiteRT model:

  1. Right-click on the module you would like to use the LiteRT model or click on File > New > Other > LiteRT Model.

  2. Select the location of your LiteRT file. Note that the tooling configures the module's dependency with ML Model binding and automatically adds all required dependencies to your Android module's build.gradle file.

  3. Click Finish to begin the import process. When the import is finished, the tool displays a screen describing the model, including its input and output tensors.

  4. To start using the model, select Kotlin or Java, copy and paste the code in the Sample Code section.

You can return to the model information screen by double clicking the TensorFlow Lite model under the ml directory in Android Studio. For more information on using the Modle Binding feature of Android Studio, see the Android Studio release notes. For an overview of using model binding in Android Studio, see the code example instructions.

Tools for building with C and C++

The C and C++ libraries for LiteRT are primarily intended for developers using the Android Native Development Kit (NDK) to build their apps. There are two ways to use LiteRT through C++ if you build your app with the NDK:

LiteRT C API

Using this API is the recommended approach for developers using the NDK. Download the LiteRT AAR hosted at MavenCentral file, rename to tensorflow-lite-*.zip, and unzip it. You must include the four header files in the headers/tensorflow/lite/ and headers/tensorflow/lite/c/ folders and the relevant libtensorflowlite_jni.so dynamic library in the jni/ folder in your NDK project.

The c_api.h header file contains basic documentation about using the LiteRT C API.

LiteRT C++ API

If you want to use LiteRT through C++ API, you can build the C++ shared libraries:

32bit armeabi-v7a:

bazel build -c opt --config=android_arm //tensorflow/lite:libtensorflowlite.so

64bit arm64-v8a:

bazel build -c opt --config=android_arm64 //tensorflow/lite:libtensorflowlite.so

Currently, there is no straightforward way to extract all header files needed, so you must include all header files in tensorflow/lite/ from the TensorFlow repository. Additionally, you will need header files from FlatBuffers and Abseil.