TensorFlow Lite in Google Play services

TensorFlow Lite is available in Google Play services runtime for all Android devices running the current version of Play services. This runtime allows you to run machine learning (ML) models without statically bundling TensorFlow Lite libraries into your app.

With the Google Play services API, you can reduce the size of your apps and gain improved performance from the latest stable version of the libraries. TensorFlow Lite in Google Play services is the recommended way to use TensorFlow Lite on Android.

You can get started with the Play services runtime with the Quickstart, which provides a step-by-step guide to implement a sample application. If you are already using stand-alone TensorFlow Lite in your app, refer to the Migrating from stand-alone TensorFlow Lite section to update an existing app to use the Play services runtime. For more information about Google Play services, see the Google Play services website.

Terms: By accessing or using TensorFlow Lite in Google Play services APIs, you agree to the Terms of Service. Please read and understand all applicable terms and policies before accessing the APIs.

Using the Play services runtime

TensorFlow Lite in Google Play services is available through the TensorFlow Lite Task API and TensorFlow Lite Interpreter API. The Task Library provides optimized out-of-box model interfaces for common machine learning tasks using visual, audio, and text data. The TensorFlow Lite Interpreter API, provided by the TensorFlow runtime and support libraries, provides a more general-purpose interface for building and running ML models.

The following sections provide instructions on how to implement the Interpreter and Task Library APIs in Google Play services. While it is possible for an app to use both the Interpreter APIs and Task Library APIs, most apps should only use one set of APIs.

Using the Task Library APIs

The TensorFlow Lite Task API wraps the Interpreter API and provides a high-level programming interface for common machine learning tasks that use visual, audio, and text data. You should use the Task API if your application requires one of the supported tasks.

1. Add project dependencies

Your project dependency depends on your machine learning use case. The Task APIs contain the following libraries:

• Vision library: org.tensorflow:tensorflow-lite-task-vision-play-services
• Audio library: org.tensorflow:tensorflow-lite-task-audio-play-services
• Text library: org.tensorflow:tensorflow-lite-task-text-play-services

Add one of the dependencies to your app project code to access the Play services API for TensorFlow Lite. For example, use the following to implement a vision task:

dependencies {
...
    implementation 'org.tensorflow:tensorflow-lite-task-vision-play-services:0.4.2'
...
}

Caution: The TensorFlow Lite Tasks Audio library version 0.4.2 maven repository is incomplete. Use version 0.4.2.1 for this library instead: org.tensorflow:tensorflow-lite-task-audio-play-services:0.4.2.1.

2. Add initialization of TensorFlow Lite

Initialize the TensorFlow Lite component of the Google Play services API before using the TensorFlow Lite APIs. The following example initializes the vision library:

Kotlin

init {
  TfLiteVision.initialize(context)
    }
  }

Important: Make sure the TfLite.initialize task completes before executing code that accesses TensorFlow Lite APIs.

Tip: The TensorFlow Lite modules are installed at the same time your application is installed or updated from the Play Store. You can check the availability of the modules by using ModuleInstallClient from the Google Play services APIs. For more information on checking module availability, see Ensuring API availability with ModuleInstallClient.

3. Run inferences

After initializing the TensorFlow Lite component, call the detect() method to generate inferences. The exact code within the detect() method varies depending on the library and use case. The following is for a simple object detection use case with the TfLiteVision library:

Kotlin

fun detect(...) {
  if (!TfLiteVision.isInitialized()) {
    Log.e(TAG, "detect: TfLiteVision is not initialized yet")
    return
  }

  if (objectDetector == null) {
    setupObjectDetector()
  }

  ...

}

Depending on the data format, you may also need to preprocess and convert your data within the detect() method before generating inferences. For example, image data for an object detector requires the following:

val imageProcessor = ImageProcessor.Builder().add(Rot90Op(-imageRotation / 90)).build()
val tensorImage = imageProcessor.process(TensorImage.fromBitmap(image))
val results = objectDetector?.detect(tensorImage)

Using the Interpreter APIs

The Interpreter APIs offer more control and flexibility than the Task Library APIs. You should use the Interpreter APIs if your machine learning task is not supported by the Task library, or if you require a more general-purpose interface for building and running ML models.

1. Add project dependencies

Add the following dependencies to your app project code to access the Play services API for TensorFlow Lite:

dependencies {
...
    // Tensorflow Lite dependencies for Google Play services
    implementation 'com.google.android.gms:play-services-tflite-java:16.0.1'
    // Optional: include Tensorflow Lite Support Library
    implementation 'com.google.android.gms:play-services-tflite-support:16.0.1'
...
}

2. Add initialization of TensorFlow Lite

Initialize the TensorFlow Lite component of the Google Play services API before using the TensorFlow Lite APIs:

Kotlin

val initializeTask: Task<Void> by lazy { TfLite.initialize(this) }

Java

Task<Void> initializeTask = TfLite.initialize(context);

Note: Make sure the TfLite.initialize task completes before executing code that accesses TensorFlow Lite APIs. Use the addOnSuccessListener() method, as shown in the next section.

3. Create an Interpreter and set runtime option

Create an interpreter using InterpreterApi.create() and configure it to use Google Play services runtime, by calling InterpreterApi.Options.setRuntime(), as shown in the following example code:

Kotlin

import org.tensorflow.lite.InterpreterApi
import org.tensorflow.lite.InterpreterApi.Options.TfLiteRuntime
...
private lateinit var interpreter: InterpreterApi
...
initializeTask.addOnSuccessListener {
  val interpreterOption =
    InterpreterApi.Options().setRuntime(TfLiteRuntime.FROM_SYSTEM_ONLY)
  interpreter = InterpreterApi.create(
    modelBuffer,
    interpreterOption
  )}
  .addOnFailureListener { e ->
    Log.e("Interpreter", "Cannot initialize interpreter", e)
  }

Java

import org.tensorflow.lite.InterpreterApi
import org.tensorflow.lite.InterpreterApi.Options.TfLiteRuntime
...
private InterpreterApi interpreter;
...
initializeTask.addOnSuccessListener(a -> {
    interpreter = InterpreterApi.create(modelBuffer,
      new InterpreterApi.Options().setRuntime(TfLiteRuntime.FROM_SYSTEM_ONLY));
  })
  .addOnFailureListener(e -> {
    Log.e("Interpreter", String.format("Cannot initialize interpreter: %s",
          e.getMessage()));
  });

You should use the implementation above because it avoids blocking the Android user interface thread. If you need to manage thread execution more closely, you can add a Tasks.await() call to interpreter creation:

Kotlin

import androidx.lifecycle.lifecycleScope
...
lifecycleScope.launchWhenStarted { // uses coroutine
  initializeTask.await()
}

Java

@BackgroundThread
InterpreterApi initializeInterpreter() {
    Tasks.await(initializeTask);
    return InterpreterApi.create(...);
}

Warning: Do not call .await() on the foreground user interface thread because it interrupts display of user interface elements and creates a poor user experience.

4. Run inferences

Using the interpreter object you created, call the run() method to generate an inference.

Kotlin

interpreter.run(inputBuffer, outputBuffer)

Java

interpreter.run(inputBuffer, outputBuffer);

Hardware acceleration

TensorFlow Lite allows you to accelerate the performance of your model using specialized hardware processors, such as graphics processing units (GPUs). You can take advantage of these specialized processors using hardware drivers called delegates. You can use the following hardware acceleration delegates with TensorFlow Lite in Google Play services:

• GPU delegate (recommended) - This delegate is provided through Google Play services and is dynamically loaded, just like the Play services versions of the Task API and Interpreter API.

• NNAPI delegate - This delegate is available as an included library dependency in your Android development project, and is bundled into your app.

For more information about hardware acceleration with TensorFlow Lite, see the TensorFlow Lite Delegates page.

Checking device compatibility

Not all devices support GPU hardware acceleration with TFLite. In order to mitigate errors and potential crashes, use the TfLiteGpu.isGpuDelegateAvailable method to check whether a device is compatible with the GPU delegate.

Use this method to confirm whether a device is compatible with GPU, and use CPU or the NNAPI delegate as a fallback for when GPU is not supported.

useGpuTask = TfLiteGpu.isGpuDelegateAvailable(context)

Once you have a variable like useGpuTask, you can use it to determine whether devices use the GPU delegate. The following examples show how this can be done with both the Task Library and Interpreter APIs.

With the Task Api

Kotlin

lateinit val optionsTask = useGpuTask.continueWith { task ->
  val baseOptionsBuilder = BaseOptions.builder()
  if (task.result) {
    baseOptionsBuilder.useGpu()
  }
 ObjectDetectorOptions.builder()
          .setBaseOptions(baseOptionsBuilder.build())
          .setMaxResults(1)
          .build()
}
    

Java

Task<ObjectDetectorOptions> optionsTask = useGpuTask.continueWith({ task ->
  BaseOptions baseOptionsBuilder = BaseOptions.builder();
  if (task.getResult()) {
    baseOptionsBuilder.useGpu();
  }
  return ObjectDetectorOptions.builder()
          .setBaseOptions(baseOptionsBuilder.build())
          .setMaxResults(1)
          .build()
});
    

With the Interpreter Api

Kotlin

val interpreterTask = useGpuTask.continueWith { task ->
  val interpreterOptions = InterpreterApi.Options()
      .setRuntime(TfLiteRuntime.FROM_SYSTEM_ONLY)
  if (task.result) {
      interpreterOptions.addDelegateFactory(GpuDelegateFactory())
  }
  InterpreterApi.create(FileUtil.loadMappedFile(context, MODEL_PATH), interpreterOptions)
}
    

Java

Task<InterpreterApi.Options> interpreterOptionsTask = useGpuTask.continueWith({ task ->
  InterpreterApi.Options options =
      new InterpreterApi.Options().setRuntime(TfLiteRuntime.FROM_SYSTEM_ONLY);
  if (task.getResult()) {
     options.addDelegateFactory(new GpuDelegateFactory());
  }
  return options;
});
    

GPU with Task Library APIs

To use the GPU delegate with the Task APIs:

1. Update the project dependencies to use the GPU delegate from Play services:

   implementation 'com.google.android.gms:play-services-tflite-gpu:16.1.0'
   

2. Initialize the GPU delegate with setEnableGpuDelegateSupport. For example, you can initialize the GPU delegate for TfLiteVision with the following:

   Kotlin

       TfLiteVision.initialize(context, TfLiteInitializationOptions.builder().setEnableGpuDelegateSupport(true).build())
       

   Java

       TfLiteVision.initialize(context, TfLiteInitializationOptions.builder().setEnableGpuDelegateSupport(true).build());
       

3. Enable the GPU delegate option with BaseOptions:

   Kotlin

       val baseOptions = BaseOptions.builder().useGpu().build()
       

   Java

       BaseOptions baseOptions = BaseOptions.builder().useGpu().build();
       

4. Configure the options using .setBaseOptions. For example, you can set up GPU in ObjectDetector with the following:

   Kotlin

       val options =
           ObjectDetectorOptions.builder()
               .setBaseOptions(baseOptions)
               .setMaxResults(1)
               .build()
       

   Java

       ObjectDetectorOptions options =
           ObjectDetectorOptions.builder()
               .setBaseOptions(baseOptions)
               .setMaxResults(1)
               .build();
       

GPU with Interpreter APIs

To use the GPU delegate with the Interpreter APIs:

1. Update the project dependencies to use the GPU delegate from Play services:

   implementation 'com.google.android.gms:play-services-tflite-gpu:16.1.0'
   

2. Enable the GPU delegate option in the TFlite initialization:

   Kotlin

       TfLite.initialize(context,
         TfLiteInitializationOptions.builder()
          .setEnableGpuDelegateSupport(true)
          .build())
       

   Java

       TfLite.initialize(context,
         TfLiteInitializationOptions.builder()
          .setEnableGpuDelegateSupport(true)
          .build());
       

3. Set GPU delegate in interpreter options to use DelegateFactory by calling addDelegateFactory() within InterpreterApi.Options():

   Kotlin

       val interpreterOption = InterpreterApi.Options()
        .setRuntime(TfLiteRuntime.FROM_SYSTEM_ONLY)
        .addDelegateFactory(GpuDelegateFactory())
       

   Java

       Options interpreterOption = InterpreterApi.Options()
         .setRuntime(TfLiteRuntime.FROM_SYSTEM_ONLY)
         .addDelegateFactory(new GpuDelegateFactory());
       

Migrating from stand-alone TensorFlow Lite

If you are planning to migrate your app from stand-alone TensorFlow Lite to the Play services API, review the following additional guidance for updating your app project code:

1. Review the Limitations section of this page to ensure your use case is supported.
2. Prior to updating your code, do performance and accuracy checks for your models, particularly if you are using versions of TensorFlow Lite earlier than version 2.1, so you have a baseline to compare against the new implementation.
3. If you have migrated all of your code to use the Play services API for TensorFlow Lite, you should remove the existing TensorFlow Lite runtime library dependencies (entries with org.tensorflow:tensorflow-lite:*) from your build.gradle file so that you can reduce your app size.
4. Identify all occurrences of new Interpreter object creation in your code, and modify it so that it uses the InterpreterApi.create() call. This new API is asynchronous, which means in most cases it's not a drop-in replacement, and you must register a listener for when the call completes. Refer to the code snippet in Step 3 code.
5. Add import org.tensorflow.lite.InterpreterApi; and import org.tensorflow.lite.InterpreterApi.Options.TfLiteRuntime; to any source files using the org.tensorflow.lite.Interpreter or org.tensorflow.lite.InterpreterApi classes.
6. If any of the resulting calls to InterpreterApi.create() have only a single argument, append new InterpreterApi.Options() to the argument list.
7. Append .setRuntime(TfLiteRuntime.FROM_SYSTEM_ONLY) to the last argument of any calls to InterpreterApi.create().
8. Replace all other occurrences of the org.tensorflow.lite.Interpreter class with org.tensorflow.lite.InterpreterApi.

If you want to use stand-alone TensorFlow Lite and the Play services API side-by-side, you must use TensorFlow Lite 2.9 (or later). TensorFlow Lite 2.8 and earlier versions are not compatible with the Play services API version.

Limitations

TensorFlow Lite in Google Play services has the following limitations:

• Support for hardware acceleration delegates is limited to the delegates listed in the Hardware acceleration section. No other acceleration delegates are supported.
• Access to TensorFlow Lite via native APIs is not supported. Only the TensorFlow Lite Java APIs are available through Google Play services.
• Experimental or deprecated TensorFlow Lite APIs, including custom ops, are not supported.

Support and feedback

You can provide feedback and get support through the TensorFlow Issue Tracker. Please report issues and support requests using the Issue template for TensorFlow Lite in Google Play services.

Terms of service

Use of TensorFlow Lite in Google Play services APIs is subject to the Google APIs Terms of Service.

Privacy and data collection

When you use TensorFlow Lite in Google Play services APIs, processing of the input data, such as images, video, text, fully happens on-device, and TensorFlow Lite in Google Play services APIs does not send that data to Google servers. As a result, you can use our APIs for processing data that should not leave the device.

The TensorFlow Lite in Google Play services APIs may contact Google servers from time to time in order to receive things like bug fixes, updated models and hardware accelerator compatibility information. The TensorFlow Lite in Google Play services APIs also sends metrics about the performance and utilization of the APIs in your app to Google. Google uses this metrics data to measure performance, debug, maintain and improve the APIs, and detect misuse or abuse, as further described in our Privacy Policy.

You are responsible for informing users of your app about Google's processing of TensorFlow Lite in Google Play services APIs metrics data as required by applicable law.

Data we collect includes the following:

• Device information (such as manufacturer, model, OS version and build) and available ML hardware accelerators (GPU and DSP). Used for diagnostics and usage analytics.
• Device identifier used for diagnostics and usage analytics.
• App information (package name, app version). Used for diagnostics and usage analytics.
• API configuration (such as which delegates are being used). Used for diagnostics and usage analytics.
• Event type (such as interpreter creation, inference). Used for diagnostics and usage analytics.
• Error codes. Used for diagnostics.
• Performance metrics. Used for diagnostics.

Next steps

For more information about implementing machine learning in your mobile application with TensorFlow Lite, see the TensorFlow Lite Developer Guide. You can find additional TensorFlow Lite models for image classification, object detection, and other applications on the TensorFlow Hub.