This document describes how to build LiteRT Android library on your
own. Normally, you do not need to locally build LiteRT Android library.
If you just want to use it, see the
[Android quickstart](https://developers.google.com/edge/litert/android/quickstart) for more details on how to use
them in your Android projects.

## Use Nightly Snapshots

To use nightly snapshots, add the following repo to your root Gradle build
config.

    allprojects {
        repositories {      // should be already there
            mavenCentral()  // should be already there
            maven {         // add this repo to use snapshots
              name 'ossrh-snapshot'
              url 'https://oss.sonatype.org/content/repositories/snapshots'
            }
        }
    }

add nightly snapshots to dependencies (or edit as needed) to your build.gradle

    ...
    dependencies {
        ...
        implementation 'org.tensorflow:tensorflow-lite:0.0.0-nightly-SNAPSHOT'
        implementation 'com.google.ai.edge.litert:litert-gpu:0.0.0-nightly-SNAPSHOT'
        implementation 'com.google.ai.edge.litert:litert-support:0.0.0-nightly-SNAPSHOT'
        ...
    }
    ...

## Build LiteRT locally

In some cases, you might wish to use a local build of LiteRT. For
example, you may be building a custom binary that includes
[operations selected from TensorFlow](https://www.tensorflow.org/lite/guide/ops_select),
or you may wish to make local changes to LiteRT.

### Set up build environment using Docker

- Download the Docker file. By downloading the Docker file, you agree that the following terms of service govern your use thereof:

*By clicking to accept, you hereby agree that all use of the Android Studio and
Android Native Development Kit will be governed by the Android Software
Development Kit License Agreement available at
<https://developer.android.com/studio/terms> (such URL may be updated or changed
by Google from time to time).*
You must acknowledge the terms of service to download the file. <button class="button-blue devsite-acknowledgement-link" data-globally-unique-wall-id="tflite-android-tos">Acknowledge</button>

<br />

- You can optionally change the Android SDK or NDK version. Put the downloaded Docker file in an empty folder and build your docker image by running:

    docker build . -t tflite-builder -f tflite-android.Dockerfile

- Start the docker container interactively by mounting your current folder to /host_dir inside the container (note that /tensorflow_src is the TensorFlow repository inside the container):

    docker run -it -v $PWD:/host_dir tflite-builder bash

If you use PowerShell on Windows, replace "$PWD" with "pwd".

If you would like to use a TensorFlow repository on the host, mount that host
directory instead (-v hostDir:/host_dir).

- Once you are inside the container, you can run the following to download additional Android tools and libraries (note that you may need to accept the license):

    sdkmanager \
      "build-tools;${ANDROID_BUILD_TOOLS_VERSION}" \
      "platform-tools" \
      "platforms;android-${ANDROID_API_LEVEL}"

Now you should proceed to the [Configure WORKSPACE and .bazelrc](https://developers.google.com/edge/litert/android/lite_build#configure_workspace_and_bazelrc) section to configure the build settings.

After you finish building the libraries, you can copy them to /host_dir
inside the container so that you can access them on the host.

### Set up build environment without Docker

#### Install Bazel and Android Prerequisites

Bazel is the primary build system for TensorFlow. To build with it, you must
have it and the Android NDK and SDK installed on your system.

1. Install the latest version of the [Bazel build system](https://bazel.build/versions/master/docs/install.html).
2. The Android NDK is required to build the native (C/C++) LiteRT code. The current recommended version is 25b, which may be found [here](https://developer.android.com/ndk/downloads/older_releases.html#ndk-25b-downloads).
3. The Android SDK and build tools may be obtained [here](https://developer.android.com/tools/revisions/build-tools.html), or alternatively as part of [Android Studio](https://developer.android.com/studio/index.html). Build tools API \>= 23 is the recommended version for building LiteRT.

### Configure WORKSPACE and .bazelrc

This is a one-time configuration step that is required to build the LiteRT
libraries. Run the `./configure` script in the root TensorFlow checkout
directory, and answer "Yes" when the script asks to interactively configure the `./WORKSPACE`
for Android builds. The script will attempt to configure settings using the
following environment variables:

- `ANDROID_SDK_HOME`
- `ANDROID_SDK_API_LEVEL`
- `ANDROID_NDK_HOME`
- `ANDROID_NDK_API_LEVEL`

If these variables aren't set, they must be provided interactively in the script
prompt. Successful configuration should yield entries similar to the following
in the `.tf_configure.bazelrc` file in the root folder:

    build --action_env ANDROID_NDK_HOME="/usr/local/android/android-ndk-r25b"
    build --action_env ANDROID_NDK_API_LEVEL="21"
    build --action_env ANDROID_BUILD_TOOLS_VERSION="30.0.3"
    build --action_env ANDROID_SDK_API_LEVEL="30"
    build --action_env ANDROID_SDK_HOME="/usr/local/android/android-sdk-linux"

### Build and install

Once Bazel is properly configured, you can build the LiteRT AAR from
the root checkout directory as follows:

    bazel build -c opt --cxxopt=--std=c++17 --config=android_arm64 \
      --fat_apk_cpu=x86,x86_64,arm64-v8a,armeabi-v7a \
      --define=android_dexmerger_tool=d8_dexmerger \
      --define=android_incremental_dexing_tool=d8_dexbuilder \
      //tensorflow/lite/java:tensorflow-lite

This will generate an AAR file in `bazel-bin/tensorflow/lite/java/`. Note
that this builds a "fat" AAR with several different architectures; if you don't
need all of them, use the subset appropriate for your deployment environment.

You can build smaller AAR files targeting only a set of models as follows:

    bash tensorflow/lite/tools/build_aar.sh \
      --input_models=model1,model2 \
      --target_archs=x86,x86_64,arm64-v8a,armeabi-v7a

Above script will generate the `tensorflow-lite.aar` file and optionally the
`tensorflow-lite-select-tf-ops.aar` file if one of the models is using
Tensorflow ops. For more details, please see the
[Reduce LiteRT binary size](https://developers.google.com/edge/litert/guide/reduce_binary_size) section.

#### Add AAR directly to project

Move the `tensorflow-lite.aar` file into a directory called `libs` in your
project. Modify your app's `build.gradle` file to reference the new directory
and replace the existing LiteRT dependency with the new local library,
e.g.:

    allprojects {
        repositories {
            mavenCentral()
            maven {  // Only for snapshot artifacts
                name 'ossrh-snapshot'
                url 'https://oss.sonatype.org/content/repositories/snapshots'
            }
            flatDir {
                dirs 'libs'
            }
        }
    }

    dependencies {
        compile(name:'tensorflow-lite', ext:'aar')
    }

#### Install AAR to local Maven repository

Execute the following command from your root checkout directory:

    mvn install:install-file \
      -Dfile=bazel-bin/tensorflow/lite/java/tensorflow-lite.aar \
      -DgroupId=org.tensorflow \
      -DartifactId=tensorflow-lite -Dversion=0.1.100 -Dpackaging=aar

In your app's `build.gradle`, ensure you have the `mavenLocal()` dependency and
replace the standard LiteRT dependency with the one that has support
for select TensorFlow ops:

    allprojects {
        repositories {
            mavenCentral()
            maven {  // Only for snapshot artifacts
                name 'ossrh-snapshot'
                url 'https://oss.sonatype.org/content/repositories/snapshots'
            }
            mavenLocal()
        }
    }

    dependencies {
        implementation 'org.tensorflow:tensorflow-lite:0.1.100'
    }

Note that the `0.1.100` version here is purely for the sake of
testing/development. With the local AAR installed, you can use the standard
[LiteRT Java inference APIs](https://developers.google.com/edge/litert/guide/inference) in your app code.