Building TensorFlow on Android

To get you started working with TensorFlow on Android, we'll walk through two ways to build our TensorFlow mobile demos and deploying them on an Android device. The first is Android Studio, which lets you build and deploy in an IDE. The second is building with Bazel and deploying with ADB on the command line.

Why choose one or the other of these methods?

The simplest way to use TensorFlow on Android is to use Android Studio. If you aren't planning to customize your TensorFlow build at all, or if you want to use Android Studio's editor and other features to build an app and just want to add TensorFlow to it, we recommend using Android Studio.

If you are using custom ops, or have some other reason to build TensorFlow from scratch, scroll down and see our instructions for building the demo with Bazel.

Build the demo using Android Studio


If you haven't already, do the following two things:

  • Install Android Studio, following the instructions on their website.

  • Clone the TensorFlow repository from Github:

    git clone


  1. Open Android Studio, and from the Welcome screen, select Open an existing Android Studio project.

  2. From the Open File or Project window that appears, navigate to and select the tensorflow/examples/android directory from wherever you cloned the TensorFlow Github repo. Click OK.

    If it asks you to do a Gradle Sync, click OK.

    You may also need to install various platforms and tools, if you get errors like "Failed to find target with hash string 'android-23' and similar.

  3. Open the build.gradle file (you can go to 1:Project in the side panel and find it under the Gradle Scripts zippy under Android). Look for the nativeBuildSystem variable and set it to none if it isn't already:

    // set to 'bazel', 'cmake', 'makefile', 'none'
    def nativeBuildSystem = 'none'
  4. Click the Run button (the green arrow) or use Run -> Run 'android' from the top menu.

    If it asks you to use Instant Run, click Proceed Without Instant Run.

    Also, you need to have an Android device plugged in with developer options enabled at this point. See here for more details on setting up developer devices.

This installs three apps on your phone that are all part of the TensorFlow Demo. See Android Sample Apps for more information about them.

Adding TensorFlow to your apps using Android Studio

To add TensorFlow to your own apps on Android, the simplest way is to add the following lines to your Gradle build file:

allprojects {
    repositories {

dependencies {
    compile 'org.tensorflow:tensorflow-android:+'

This automatically downloads the latest stable version of TensorFlow as an AAR and installs it in your project.

Build the demo using Bazel

Another way to use TensorFlow on Android is to build an APK using Bazel and load it onto your device using ADB. This requires some knowledge of build systems and Android developer tools, but we'll guide you through the basics here.

  • First, follow our instructions for installing from sources. This will also guide you through installing Bazel and cloning the TensorFlow code.

  • Download the Android SDK and NDK if you do not already have them. You need at least version 12b of the NDK, and 23 of the SDK.

  • In your copy of the TensorFlow source, update the WORKSPACE file with the location of your SDK and NDK, where it says <PATH_TO_NDK> and <PATH_TO_SDK>.

  • Run Bazel to build the demo APK:

    bazel build -c opt //tensorflow/examples/android:tensorflow_demo
  • Use ADB to install the APK onto your device:

    adb install -r bazel-bin/tensorflow/examples/android/tensorflow_demo.apk

This installs three apps on your phone that are all part of the TensorFlow Demo. See Android Sample Apps for more information about them.

Android Sample Apps

The Android example code is a single project that builds and installs three sample apps which all use the same underlying code. The sample apps all take video input from a phone's camera:

  • TF Classify uses the Inception v3 model to label the objects it’s pointed at with classes from Imagenet. There are only 1,000 categories in Imagenet, which misses most everyday objects and includes many things you’re unlikely to encounter often in real life, so the results can often be quite amusing. For example there’s no ‘person’ category, so instead it will often guess things it does know that are often associated with pictures of people, like a seat belt or an oxygen mask. If you do want to customize this example to recognize objects you care about, you can use the TensorFlow for Poets codelab as an example for how to train a model based on your own data.

  • TF Detect uses a multibox model to try to draw bounding boxes around the locations of people in the camera. These boxes are annotated with the confidence for each detection result. Results will not be perfect, as this kind of object detection is still an active research topic. The demo also includes optical tracking for when objects move between frames, which runs more frequently than the TensorFlow inference. This improves the user experience since the apparent frame rate is faster, but it also gives the ability to estimate which boxes refer to the same object between frames, which is important for counting objects over time.

  • TF Stylize implements a real-time style transfer algorithm on the camera feed. You can select which styles to use and mix between them using the palette at the bottom of the screen, and also switch out the resolution of the processing to go higher or lower rez.

When you build and install the demo, you'll see three app icons on your phone, one for each of the demos. Tapping on them should open up the app and let you explore what they do. You can enable profiling statistics on-screen by tapping the volume up button while they’re running.

Android Inference Library

Because Android apps need to be written in Java, and core TensorFlow is in C++, TensorFlow has a JNI library to interface between the two. Its interface is aimed only at inference, so it provides the ability to load a graph, set up inputs, and run the model to calculate particular outputs. You can see the full documentation for the minimal set of methods in

The demos applications use this interface, so they’re a good place to look for example usage. You can download prebuilt binary jars at