TensorFlow Lite GPU delegate

TensorFlow Lite supports several hardware accelerators. This document describes how to use the GPU backend using the TensorFlow Lite delegate APIs on Android and iOS.

GPUs are designed to have high throughput for massively parallelizable workloads. Thus, they are well-suited for deep neural nets, which consist of a huge number of operators, each working on some input tensor(s) that can be easily divided into smaller workloads and carried out in parallel, typically resulting in lower latency. In the best scenario, inference on the GPU may now run fast enough for previously not available real-time applications.

Unlike CPUs, GPUs compute with 16-bit or 32-bit floating point numbers and do not require quantization for optimal performance. The delegate does accept 8-bit quantized models, but the calculation will be performed in floating point numbers. Refer to the advanced documentation for details.

Another benefit with GPU inference is its power efficiency. GPUs carry out the computations in a very efficient and optimized manner, so that they consume less power and generate less heat than when the same task is run on CPUs.

Demo app tutorials

The easiest way to try out the GPU delegate is to follow the below tutorials, which go through building our classification demo applications with GPU support. The GPU code is only binary for now; it will be open-sourced soon. Once you understand how to get our demos working, you can try this out on your own custom models.

Android (with Android Studio)

For a step-by-step tutorial, watch the GPU Delegate for Android video.

Step 1. Clone the TensorFlow source code and open it in Android Studio

git clone https://github.com/tensorflow/tensorflow

Step 2. Edit app/build.gradle to use the nightly GPU AAR

Add the tensorflow-lite-gpu package alongside the existing tensorflow-lite package in the existing dependencies block.

dependencies {
    implementation 'org.tensorflow:tensorflow-lite:2.3.0'
    implementation 'org.tensorflow:tensorflow-lite-gpu:2.3.0'

Step 3. Build and run

Run → Run ‘app’. When you run the application you will see a button for enabling the GPU. Change from quantized to a float model and then click GPU to run on the GPU.

running android gpu demo and switch to gpu

iOS (with XCode)

For a step-by-step tutorial, watch the GPU Delegate for iOS video.

Step 1. Get the demo source code and make sure it compiles.

Follow our iOS Demo App tutorial. This will get you to a point where the unmodified iOS camera demo is working on your phone.

Step 2. Modify the Podfile to use the TensorFlow Lite GPU CocoaPod

From 2.3.0 release, by default GPU delegate is excluded from the pod to reduce the binary size. You can include them by specifying subspec. For TensorFlowLiteSwift pod:

pod 'TensorFlowLiteSwift/Metal', '~> 0.0.1-nightly',


pod 'TensorFlowLiteSwift', '~> 0.0.1-nightly', :subspecs => ['Metal']

You can do similarly for TensorFlowLiteObjC or TensorFlowLitC if you want to use the Objective-C (from 2.4.0 release) or C API.

Before 2.3.0 release

Until TensorFlow Lite 2.0.0

We have built a binary CocoaPod that includes the GPU delegate. To switch the project to use it, modify the `tensorflow/tensorflow/lite/examples/ios/camera/Podfile` file to use the `TensorFlowLiteGpuExperimental` pod instead of `TensorFlowLite`.

    target 'YourProjectName'
      # pod 'TensorFlowLite', '1.12.0'
      pod 'TensorFlowLiteGpuExperimental'

Until TensorFlow Lite 2.2.0

From TensorFlow Lite 2.1.0 to 2.2.0, GPU delegate is included in the `TensorFlowLiteC` pod. You can choose between `TensorFlowLiteC` and `TensorFlowLiteSwift` depending on the language.

Step 3. Enable the GPU delegate

To enable the code that will use the GPU delegate, you will need to change TFLITE_USE_GPU_DELEGATE from 0 to 1 in CameraExampleViewController.h.


Step 4. Build and run the demo app

After following the previous step, you should be able to run the app.

Step 5. Release mode

While in Step 4 you ran in debug mode, to get better performance, you should change to a release build with the appropriate optimal Metal settings. In particular, To edit these settings go to the Product > Scheme > Edit Scheme.... Select Run. On the Info tab, change Build Configuration, from Debug to Release, uncheck Debug executable.

setting up release

Then click the Options tab and change GPU Frame Capture to Disabled and Metal API Validation to Disabled.

setting up metal options

Lastly make sure to select Release-only builds on 64-bit architecture. Under Project navigator -> tflite_camera_example -> PROJECT -> tflite_camera_example -> Build Settings set Build Active Architecture Only > Release to Yes.

setting up release options

Trying the GPU delegate on your own model


There are two ways to invoke model acceleration depending on if you are using Android Studio ML Model Binding or TensorFlow Lite Interpreter.

TensorFlow Lite Interpreter

Look at the demo to see how to add the delegate. In your application, add the AAR as above, import org.tensorflow.lite.gpu.GpuDelegate module, and use theaddDelegate function to register the GPU delegate to the interpreter:


    import org.tensorflow.lite.Interpreter
    import org.tensorflow.lite.gpu.CompatibilityList
    import org.tensorflow.lite.gpu.GpuDelegate

    val compatList = CompatibilityList()

    val options = Interpreter.Options().apply{
            // if the device has a supported GPU, add the GPU delegate
            val delegateOptions = compatList.bestOptionsForThisDevice
        } else {
            // if the GPU is not supported, run on 4 threads

    val interpreter = Interpreter(model, options)

    // Run inference
    interpreter.run(input, output)


    import org.tensorflow.lite.Interpreter;
    import org.tensorflow.lite.gpu.CompatibilityList;
    import org.tensorflow.lite.gpu.GpuDelegate;

    // Initialize interpreter with GPU delegate
    Interpreter.Options options = new Interpreter.Options();
    CompatibilityList compatList = CompatibilityList();

        // if the device has a supported GPU, add the GPU delegate
        GpuDelegate.Options delegateOptions = compatList.getBestOptionsForThisDevice();
        GpuDelegate gpuDelegate = new GpuDelegate(delegateOptions);
    } else {
        // if the GPU is not supported, run on 4 threads

    Interpreter interpreter = new Interpreter(model, options);

    // Run inference
    interpreter.run(input, output);



    import TensorFlowLite

    // Load model ...

    // Initialize TensorFlow Lite interpreter with the GPU delegate.
    let delegate = MetalDelegate()
    if let interpreter = try Interpreter(modelPath: modelPath,
                                         delegates: [delegate]) {
      // Run inference ...


    // Import module when using CocoaPods with module support
    @import TFLTensorFlowLite;

    // Or import following headers manually
    #import "tensorflow/lite/objc/apis/TFLMetalDelegate.h"
    #import "tensorflow/lite/objc/apis/TFLTensorFlowLite.h"

    // Initialize GPU delegate
    TFLMetalDelegate* metalDelegate = [[TFLMetalDelegate alloc] init];

    // Initialize interpreter with model path and GPU delegate
    TFLInterpreterOptions* options = [[TFLInterpreterOptions alloc] init];
    NSError* error = nil;
    TFLInterpreter* interpreter = [[TFLInterpreter alloc]
                                            delegates:@[ metalDelegate ]
    if (error != nil) { /* Error handling... */ }

    if (![interpreter allocateTensorsWithError:&error]) { /* Error handling... */ }
    if (error != nil) { /* Error handling... */ }

    // Run inference ...


C (Until 2.3.0)

        #include "tensorflow/lite/c/c_api.h"
        #include "tensorflow/lite/delegates/gpu/metal_delegate.h"

        // Initialize model
        TfLiteModel* model = TfLiteModelCreateFromFile(model_path);

        // Initialize interpreter with GPU delegate
        TfLiteInterpreterOptions* options = TfLiteInterpreterOptionsCreate();
        TfLiteDelegate* delegate = TFLGPUDelegateCreate(nil);  // default config
        TfLiteInterpreterOptionsAddDelegate(options, metal_delegate);
        TfLiteInterpreter* interpreter = TfLiteInterpreterCreate(model, options);


        NSMutableData *input_data = [NSMutableData dataWithLength:input_size * sizeof(float)];
        NSMutableData *output_data = [NSMutableData dataWithLength:output_size * sizeof(float)];
        TfLiteTensor* input = TfLiteInterpreterGetInputTensor(interpreter, 0);
        const TfLiteTensor* output = TfLiteInterpreterGetOutputTensor(interpreter, 0);

        // Run inference
        TfLiteTensorCopyFromBuffer(input, inputData.bytes, inputData.length);
        TfLiteTensorCopyToBuffer(output, outputData.mutableBytes, outputData.length);

        // Clean up

## Supported Models and Ops

With the release of the GPU delegate, we included a handful of models that can
be run on the backend:

*   [MobileNet v1 (224x224) image classification](https://ai.googleblog.com/2017/06/mobilenets-open-source-models-for.html) [[download]](https://storage.googleapis.com/download.tensorflow.org/models/tflite/gpu/mobilenet_v1_1.0_224.tflite)
    <br /><i>(image classification model designed for mobile and embedded based vision applications)</i>
*   [DeepLab segmentation (257x257)](https://ai.googleblog.com/2018/03/semantic-image-segmentation-with.html) [[download]](https://storage.googleapis.com/download.tensorflow.org/models/tflite/gpu/deeplabv3_257_mv_gpu.tflite)
    <br /><i>(image segmentation model that assigns semantic labels (e.g., dog, cat, car) to every pixel in the input image)</i>
*   [MobileNet SSD object detection](https://ai.googleblog.com/2018/07/accelerated-training-and-inference-with.html) [[download]](https://storage.googleapis.com/download.tensorflow.org/models/tflite/gpu/mobile_ssd_v2_float_coco.tflite)
    <br /><i>(image classification model that detects multiple objects with bounding boxes)</i>
*   [PoseNet for pose estimation](https://github.com/tensorflow/tfjs-models/tree/master/posenet) [[download]](https://storage.googleapis.com/download.tensorflow.org/models/tflite/gpu/multi_person_mobilenet_v1_075_float.tflite)
    <br /><i>(vision model that estimates the poses of a person(s) in image or video)</i>

To see a full list of supported ops, please see the
[advanced documentation](gpu_advanced).

## Non-supported models and ops

If some of the ops are not supported by the GPU delegate, the framework will
only run a part of the graph on the GPU and the remaining part on the CPU. Due
to the high cost of CPU/GPU synchronization, a split execution mode like this
will often result in slower performance than when the whole network is run on
the CPU alone. In this case, the user will get a warning like:

WARNING: op code #42 cannot be handled by this delegate.

We did not provide a callback for this failure, as this is not a true run-time failure, but something that the developer can observe while trying to get the network to run on the delegate.

Tips for optimization

Optimizing for mobile devices

Some operations that are trivial on the CPU may have a high cost for the GPU on mobile devices. Reshape operations are particularly expensive to run, including BATCH_TO_SPACE, SPACE_TO_BATCH, SPACE_TO_DEPTH, and so forth. You should closely examine use of reshape operations, and consider that may have been applied only for exploring data or for early iterations of your model. Removing them can significantly improve performance.

On GPU, tensor data is sliced into 4-channels. Thus, a computation on a tensor of shape [B,H,W,5] will perform about the same on a tensor of shape [B,H,W,8] but significantly worse than [B,H,W,4]. In that sense, if the camera hardware supports image frames in RGBA, feeding that 4-channel input is significantly faster as a memory copy (from 3-channel RGB to 4-channel RGBX) can be avoided.

For best performance, you should consider retraining the classifier with a mobile-optimized network architecture. Optimization for on-device inferencing can dramatically reduce latency and power consumption by taking advantage of mobile hardware features.

Reducing initialization time with serialization

The GPU delegate feature allows you to load from pre-compiled kernel code and model data serialized and saved on disk from previous runs. This approach avoids re-compilation and reduces startup time by up to 90%. For instructions on how to apply serialization to your project, see GPU Delegate Serialization.