GPU acceleration delegate with Task library

Using graphics processing units (GPUs) to run your machine learning (ML) models can dramatically improve the performance and the user experience of your ML-enabled applications. On Android devices, you can enable GPU-accelerated execution of your models using a delegate and one of the following APIs:

• Interpreter API - guide
• Task library API - this guide
• Native (C/C++) API - this guide

This page describes how to enable GPU acceleration for TensorFlow Lite models in Android apps using the Task library. For more information about the GPU delegate for TensorFlow Lite, including best practices and advanced techniques, see the GPU delegates page.

Use GPU with TensorFlow Lite with Google Play services

The TensorFlow Lite Task Libraries provide a set of task-specific APIs for building machine learning applications. This section describes how to use the GPU accelerator delegate with these APIs using TensorFlow Lite with Google Play services.

TensorFlow Lite with Google Play services is the recommended path to use TensorFlow Lite on Android. If your application is targeting devices not running Google Play, see the GPU with Task Library and standalone TensorFlow Lite section.

Add project dependencies

To enable access to the GPU delegate with the TensorFlow Lite Task Libraries using Google Play services, add com.google.android.gms:play-services-tflite-gpu to the dependencies of your app's build.gradle file:

dependencies {
  ...
  implementation 'com.google.android.gms:play-services-tflite-gpu:16.4.0'
}

Enable GPU acceleration

Then, verify asynchronously that GPU delegate is available for the device using the TfLiteGpu class and enable the GPU delegate option for your Task API model class with the BaseOptions class. For example, you can set up GPU in ObjectDetector as shown in the following code examples:

    lateinit val optionsTask = useGpuTask.continueWith { task ->
      val baseOptionsBuilder = BaseOptions.builder()
      if (task.result) {
        baseOptionsBuilder.useGpu()
      }
    ObjectDetectorOptions.builder()
              .setBaseOptions(baseOptionsBuilder.build())
              .setMaxResults(1)
              .build()
    }
  </pre></p>
</section>
<section>
  <h3>Java</h3>
  <p><pre class="prettyprint lang-java">
  Task<Boolean> useGpuTask = TfLiteGpu.isGpuDelegateAvailable(context);

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

Use GPU with standalone TensorFlow Lite {:#standalone}

If your application is targets devices which are not running Google Play, it is possible to bundle the GPU delegate to your application and use it with the standalone version of TensorFlow Lite.

Add project dependencies

To enable access to the GPU delegate with the TensorFlow Lite Task Libraries using the standalone version of TensorFlow Lite, add org.tensorflow:tensorflow-lite-gpu-delegate-plugin to the dependencies of your app's build.gradle file:

dependencies {
  ...
  implementation 'org.tensorflow:tensorflow-lite'
  implementation 'org.tensorflow:tensorflow-lite-gpu-delegate-plugin'
}

Enable GPU acceleration

Then enable the GPU delegate option for your Task API model class with the BaseOptions class. For example, you can set up GPU in ObjectDetector as shown in the following code examples:

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

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

val objectDetector = ObjectDetector.createFromFileAndOptions(
  context, model, options)
  </pre></p>
</section>
<section>
  <h3>Java</h3>
  <p><pre class="prettyprint lang-java">
import org.tensorflow.lite.task.core.BaseOptions
import org.tensorflow.lite.task.gms.vision.detector.ObjectDetector

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

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

val objectDetector = ObjectDetector.createFromFileAndOptions(
  context, model, options);
  </pre></p>
</section>