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TensorFlow Lite inference

The term inference refers to the process of executing a TensorFlow Lite model on-device in order to make predictions based on input data. Inference is the final step in using the model on-device.

Inference for TensorFlow Lite models is run through an interpreter. The TensorFlow Lite interpreter is designed to be lean and fast. The interpreter uses a static graph ordering and a custom (less-dynamic) memory allocator to ensure minimal load, initialization, and execution latency.

This document outlines the various APIs for the interpreter, along with the supported platforms.

Important Concepts

TensorFlow Lite inference on device typically follows the following steps.

  1. Loading a Model

    The user loads the .tflite model into memory which contains the model's execution graph.

  2. Transforming Data Input data acquired by the user generally may not match the input data format expected by the model. For eg., a user may need to resize an image or change the image format to be used by the model.

  3. Running Inference

    This step involves using the API to execute the model. It involves a few steps such as building the interpreter, and allocating tensors as explained in detail in Running a Model.

  4. Interpreting Output

    The user retrieves results from model inference and interprets the tensors in a meaningful way to be used in the application.

    For example, a model may only return a list of probabilities. It is up to the application developer to meaningully map them to relevant categories and present it to their user.

Supported Platforms

TensorFlow inference APIs are provided for most common mobile/embedded platforms such as Android, iOS and Linux.


On Android, TensorFlow Lite inference can be performed using either Java or C++ APIs. The Java APIs provide convenience and can be used directly within your Android Activity classes. The C++ APIs offer more flexibility and speed, but may require writing JNI wrappers to move data between Java and C++ layers.

Visit the Android quickstart for a tutorial and example code.


TensorFlow Lite provides native iOS libraries written in Swift and Objective-C.

Visit the iOS quickstart for a tutorial and example code.


On Linux platforms such as Raspberry Pi, TensorFlow Lite C++ and Python APIs can be used to run inference.

API Guides

TensorFlow Lite provides programming APIs in C++, Java and Python, with experimental bindings for several other languages (C, Swift, Objective-C). In most cases, the API design reflects a preference for performance over ease of use. TensorFlow Lite is designed for fast inference on small devices so it should be no surprise that the APIs try to avoid unnecessary copies at the expense of convenience. Similarly, consistency with TensorFlow APIs was not an explicit goal and some variance is to be expected.

There is also a Python API for TensorFlow Lite.

Loading a Model


The FlatBufferModel class encapsulates a model and can be built in a couple of slightly different ways depending on where the model is stored:

class FlatBufferModel {
  // Build a model based on a file. Return a nullptr in case of failure.
  static std::unique_ptr<FlatBufferModel> BuildFromFile(
      const char* filename,
      ErrorReporter* error_reporter);

  // Build a model based on a pre-loaded flatbuffer. The caller retains
  // ownership of the buffer and should keep it alive until the returned object
  // is destroyed. Return a nullptr in case of failure.
  static std::unique_ptr<FlatBufferModel> BuildFromBuffer(
      const char* buffer,
      size_t buffer_size,
      ErrorReporter* error_reporter);
tflite::FlatBufferModel model(path_to_model);

Note that if TensorFlow Lite detects the presence of Android's NNAPI it will automatically try to use shared memory to store the FlatBufferModel.


TensorFlow Lite's Java API supports on-device inference and is provided as an Android Studio Library that allows loading models, feeding inputs, and retrieving inference outputs.

The Interpreter class drives model inference with TensorFlow Lite. In most of the cases, this is the only class an app developer will need.

The Interpreter can be initialized with a model file using the constructor:

public Interpreter(@NotNull File modelFile);

or with a MappedByteBuffer:

public Interpreter(@NotNull MappedByteBuffer mappedByteBuffer);

In both cases a valid TensorFlow Lite model must be provided or an IllegalArgumentException with be thrown. If a MappedByteBuffer is used to initialize an Interpreter, it should remain unchanged for the whole lifetime of the Interpreter.

Running a Model {#running_a_model}


Running a model involves a few simple steps:

  • Build an Interpreter based on an existing FlatBufferModel
  • Optionally resize input tensors if the predefined sizes are not desired.
  • Set input tensor values
  • Invoke inference
  • Read output tensor values

The important parts of public interface of the Interpreter are provided below. It should be noted that:

  • Tensors are represented by integers, in order to avoid string comparisons (and any fixed dependency on string libraries).
  • An interpreter must not be accessed from concurrent threads.
  • Memory allocation for input and output tensors must be triggered by calling AllocateTensors() right after resizing tensors.

In order to run the inference model in TensorFlow Lite, one has to load the model into a FlatBufferModel object which then can be executed by an Interpreter. The FlatBufferModel needs to remain valid for the whole lifetime of the Interpreter, and a single FlatBufferModel can be simultaneously used by more than one Interpreter. In concrete terms, the FlatBufferModel object must be created before any Interpreter objects that use it, and must be kept around until they have all been destroyed.

The simplest usage of TensorFlow Lite will look like this:

tflite::FlatBufferModel model(path_to_model);

tflite::ops::builtin::BuiltinOpResolver resolver;
std::unique_ptr<tflite::Interpreter> interpreter;
tflite::InterpreterBuilder(*model, resolver)(&interpreter);

// Resize input tensors, if desired.

float* input = interpreter->typed_input_tensor<float>(0);
// Fill `input`.


float* output = interpreter->typed_output_tensor<float>(0);


The simplest usage of Tensorflow Lite Java API looks like this:

try (Interpreter interpreter = new Interpreter(file_of_a_tensorflowlite_model)) {
  interpreter.run(input, output);

If a model takes only one input and returns only one output, the following will trigger an inference run:

interpreter.run(input, output);

For models with multiple inputs, or multiple outputs, use:

interpreter.runForMultipleInputsOutputs(inputs, map_of_indices_to_outputs);

where each entry in inputs corresponds to an input tensor and map_of_indices_to_outputs maps indices of output tensors to the corresponding output data. In both cases the tensor indices should correspond to the values given to the TensorFlow Lite Optimized Converter when the model was created. Be aware that the order of tensors in input must match the order given to the TensorFlow Lite Optimized Converter.

The Java API also provides convenient functions for app developers to get the index of any model input or output using a tensor name:

public int getInputIndex(String tensorName);
public int getOutputIndex(String tensorName);

If tensorName is not a valid name in model, an IllegalArgumentException will be thrown.

Releasing Resources After Use

An Interpreter owns resources. To avoid memory leak, the resources must be released after use by:

Supported Data Types

To use TensorFlow Lite, the data types of the input and output tensors must be one of the following primitive types:

  • float
  • int
  • long
  • byte

String types are also supported, but they are encoded differently than the primitive types. In particular, the shape of a string Tensor dictates the number and arrangement of strings in the Tensor, with each element itself being a variable length string. In this sense, the (byte) size of the Tensor cannot be computed from the shape and type alone, and consequently strings cannot be provided as a single, flat ByteBuffer argument.

If other data types, including boxed types like Integer and Float, are used, an IllegalArgumentException will be thrown.


Each input should be an array or multi-dimensional array of the supported primitive types, or a raw ByteBuffer of the appropriate size. If the input is an array or multi-dimensional array, the associated input tensor will be implicitly resized to the array's dimensions at inference time. If the input is a ByteBuffer, the caller should first manually resize the associated input tensor (via Interpreter.resizeInput()) before running inference.

When using 'ByteBuffer', prefer using direct byte buffers, as this allows the Interpreter to avoid unnecessary copies. If the ByteBuffer is a direct byte buffer, its order must be ByteOrder.nativeOrder(). After it is used for a model inference, it must remain unchanged until the model inference is finished.


Each output should be an array or multi-dimensional array of the supported primitive types, or a ByteBuffer of the appropriate size. Note that some models have dynamic outputs, where the shape of output tensors can vary depending on the input. There's no straightforward way of handling this with the existing Java inference API, but planned extensions will make this possible.

Writing Custom Operators

All TensorFlow Lite operators (both custom and builtin) are defined using a simple pure-C interface that consists of four functions:

typedef struct {
  void* (*init)(TfLiteContext* context, const char* buffer, size_t length);
  void (*free)(TfLiteContext* context, void* buffer);
  TfLiteStatus (*prepare)(TfLiteContext* context, TfLiteNode* node);
  TfLiteStatus (*invoke)(TfLiteContext* context, TfLiteNode* node);
} TfLiteRegistration;

Refer to context.h for details on TfLiteContext and TfLiteNode. The former provides error reporting facilities and access to global objects, including all the tensors. The latter allows implementations to access their inputs and outputs.

When the interpreter loads a model, it calls init() once for each node in the graph. A given init() will be called more than once if the op is used multiple times in the graph. For custom ops a configuration buffer will be provided, containing a flexbuffer that maps parameter names to their values. The buffer is empty for builtin ops because the interpreter has already parsed the op parameters. Kernel implementation that require state should initialize it here and transfer ownership to the caller. For each init() call, there will be a corresponding call to free(), allowing implementations to dispose of the buffer they might have allocated in init().

Whenever the input tensors are resized the interpreter will go through the graph notifying implementations of the change. This gives them the chance to resize their internal buffer, check validity of input shapes and types, and recalculate output shapes. This is all done through prepare() and implementation can access their state using node->user_data.

Finally, each time inference runs the interpreter traverses the graph calling invoke(), and here too the state is available as node->user_data.

Custom ops can be implemented in exactly the same way as builtin ops, by defined those four functions and a global registration function that usually looks like this:

namespace tflite {
namespace ops {
namespace custom {
  TfLiteRegistration* Register_MY_CUSTOM_OP() {
    static TfLiteRegistration r = {my_custom_op::Init,
    return &r;
}  // namespace custom
}  // namespace ops
}  // namespace tflite

Note that registration is not automatic and an explicit call to Register_MY_CUSTOM_OP should be made somewhere. While the standard BuiltinOpResolver (available from the :builtin_ops target) takes care of the registration of builtins, custom ops will have to be collected in separate custom libraries.

Customizing the kernel library

Behind the scenes the interpreter will load a library of kernels which will be assigned to execute each of the operators in the model. While the default library only contains builtin kernels, it is possible to replace it with a custom library.

The interpreter uses an OpResolver to translate operator codes and names into actual code:

class OpResolver {
  virtual TfLiteRegistration* FindOp(tflite::BuiltinOperator op) const = 0;
  virtual TfLiteRegistration* FindOp(const char* op) const = 0;
  virtual void AddOp(tflite::BuiltinOperator op, TfLiteRegistration* registration) = 0;
  virtual void AddOp(const char* op, TfLiteRegistration* registration) = 0;

Regular usage will require the developer to use the BuiltinOpResolver and write:

tflite::ops::builtin::BuiltinOpResolver resolver;

They can then optionally register custom ops:

resolver.AddOp("MY_CUSTOM_OP", Register_MY_CUSTOM_OP());

before the resolver is passed to the InterpreterBuilder.

If the set of builtin ops is deemed to be too large, a new OpResolver could be code-generated based on a given subset of ops, possibly only the ones contained in a given model. This is the equivalent of TensorFlow's selective registration (and a simple version of it is available in the tools directory).