# Get started with TensorFlow Lite

TensorFlow Lite provides all the tools you need to convert and run TensorFlow
models on mobile, embedded, and IoT devices. The following guide walks through
each step of the developer workflow and provides links to further instructions.

[TOC]

## 1. Choose a model

<a id="1_choose_a_model"></a>

A TensorFlow model is a data structure that contains the logic and knowledge of
a machine learning network trained to solve a particular problem. There are many
ways to obtain a TensorFlow model, from using pre-trained models to training
your own.

To use a model with TensorFlow Lite, you must convert a full TensorFlow model
into the TensorFlow Lite format—you cannot create or train a model using
TensorFlow Lite. So you must start with a regular TensorFlow model, and then
[convert the model](#2_convert_the_model_format).

Note: TensorFlow Lite supports a limited subset of TensorFlow operations, so not
all models can be converted. For details, read about the
[TensorFlow Lite operator compatibility](ops_compatibility.md).


### Use a pre-trained model

The TensorFlow Lite team provides a set of pre-trained models that solve a
variety of machine learning problems. These models have been converted to work
with TensorFlow Lite and are ready to use in your applications.

The pre-trained models include:

*   [Image classification](../models/image_classification/overview.md)
*   [Object detection](../models/object_detection/overview.md)
*   [Smart reply](../models/smart_reply/overview.md)
*   [Pose estimation](../models/pose_estimation/overview.md)
*   [Segmentation](../models/segmentation/overview.md)

See our full list of pre-trained models in [Models](../models).

#### Models from other sources

There are many other places you can obtain pre-trained TensorFlow models,
including [TensorFlow Hub](https://www.tensorflow.org/hub). In most cases, these
models will not be provided in the TensorFlow Lite format, and you'll have to
[convert](#2_convert_the_model_format) them before use.

### Re-train a model (transfer learning)

Transfer learning allows you to take a trained model and re-train it to perform
another task. For example, an
[image classification](../models/image_classification/overview.md) model could
be retrained to recognize new categories of image. Re-training takes less time
and requires less data than training a model from scratch.

You can use transfer learning to customize pre-trained models to your
application. Learn how to perform transfer learning in the
<a href="https://codelabs.developers.google.com/codelabs/recognize-flowers-with-tensorflow-on-android">Recognize
flowers with TensorFlow</a> codelab.

### Train a custom model

If you have designed and trained your own TensorFlow model, or you have trained
a model obtained from another source, you must
[convert it to the TensorFlow Lite format](#2_convert_the_model_format).

You can also try [The TensorFlow Lite Model Maker library](model_maker.md) which
simplifies the process of training a TensorFlow Lite model using custom
datasets.

## 2. Convert the model

<a id="2_convert_the_model_format"></a>

TensorFlow Lite is designed to execute models efficiently on mobile and other
embedded devices with limited compute and memory resources. Some of
this efficiency comes from the use of a special format for storing models.
TensorFlow models must be converted into this format before they can be used by
TensorFlow Lite.

Converting models reduces their file size and introduces optimizations that do
not affect accuracy. The TensorFlow Lite converter provides options
that allow you to further reduce file size and increase speed of execution, with
some trade-offs.

Note: TensorFlow Lite supports a limited subset of TensorFlow operations, so not
all models can be converted. For details, read about the
[TensorFlow Lite operator compatibility](ops_compatibility.md).


### TensorFlow Lite converter

The [TensorFlow Lite converter](../convert) is a tool available as a Python API
that converts trained TensorFlow models into the TensorFlow Lite format. It can
also introduce optimizations, which are covered in section 4,
[Optimize your model](#4_optimize_your_model_optional).

The following example shows a
TensorFlow `SavedModel` being converted into the TensorFlow Lite format:

```python
import tensorflow as tf

converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
tflite_model = converter.convert()
open("converted_model.tflite", "wb").write(tflite_model)
```

You can [convert TensorFlow 2.0 models](../convert/index.md) in a similar way.

The converter can also be used from the
[command line](../convert/cmdline.md), but the Python API is recommended.

### Options

The converter can convert from a variety of input types.

When [converting TensorFlow 1.x models](../convert/python_api.md), these are:

*   [SavedModel directories](https://www.tensorflow.org/guide/saved_model)
*   Frozen GraphDef (models generated by
    [freeze_graph.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py))
*   [Keras](https://keras.io) HDF5 models
*   Models taken from a `tf.Session`

When [converting TensorFlow 2.x models](../convert/python_api.md), these are:

*   [SavedModel directories](https://www.tensorflow.org/guide/saved_model)
*   [`tf.keras` models](https://www.tensorflow.org/guide/keras/overview)
*   [Concrete functions](https://tensorflow.org/guide/concrete_function)

The converter can be configured to apply various optimizations that can improve
performance or reduce file size. This is covered in section 4,
[Optimize your model](#4_optimize_your_model_optional).

### Ops compatibility

TensorFlow Lite currently supports a
[limited subset of TensorFlow operations](ops_compatibility.md). The long term
goal is for all TensorFlow operations to be supported.

If the model you wish to convert contains unsupported operations, you can use
[TensorFlow Select](ops_select.md) to include operations from TensorFlow. This
will result in a larger binary being deployed to devices.

## 3. Run inference with the model

<a id="3_use_the_tensorflow_lite_model_for_inference_in_a_mobile_app"></a>

*Inference* is the process of running data through a model to obtain
predictions. It requires a model, an interpreter, and input data.

### TensorFlow Lite interpreter

The [TensorFlow Lite interpreter](inference.md) is a library that takes a model
file, executes the operations it defines on input data, and provides access to
the output.

The interpreter works across multiple platforms and provides a simple API for
running TensorFlow Lite models from Java, Swift, Objective-C, C++, and Python.

The following code shows the interpreter being invoked from Java:

```java
try (Interpreter interpreter = new Interpreter(tensorflow_lite_model_file)) {
  interpreter.run(input, output);
}
```

### GPU acceleration and Delegates

Some devices provide hardware acceleration for machine learning operations. For
example, most mobile phones have GPUs, which can perform floating point matrix
operations faster than a CPU.

The speed-up can be substantial. For example, a MobileNet v1 image
classification model runs 5.5x faster on a Pixel 3 phone when GPU acceleration
is used.

The TensorFlow Lite interpreter can be configured with
[Delegates](../performance/delegates.md) to make use of hardware acceleration on
different devices. The [GPU Delegate](../performance/gpu.md) allows the
interpreter to run appropriate operations on the device's GPU.

The following code shows the GPU Delegate being used from Java:

```java
GpuDelegate delegate = new GpuDelegate();
Interpreter.Options options = (new Interpreter.Options()).addDelegate(delegate);
Interpreter interpreter = new Interpreter(tensorflow_lite_model_file, options);
try {
  interpreter.run(input, output);
}
```

To add support for new hardware accelerators you can
[define your own delegate](../performance/delegates.md#how_to_add_a_delegate).

### Android and iOS

The TensorFlow Lite interpreter is easy to use from both major mobile platforms.
To get started, explore the [Android quickstart](android.md) and
[iOS quickstart](ios.md) guides.
[Example applications](https://www.tensorflow.org/lite/examples) are available
for both platforms.

To obtain the required libraries, Android developers should use the
[TensorFlow Lite AAR](android.md#use_the_tensorflow_lite_aar_from_jcenter). iOS
developers should use the
[CocoaPods for Swift or Objective-C](ios.md#add_tensorflow_lite_to_your_swift_or_objective-c_project).

### Linux

Embedded Linux is an important platform for deploying machine learning. To get
started using Python to perform inference with your TensorFlow Lite models,
follow the [Python quickstart](python.md).

To instead install the C++ library, see the build instructions for
[Raspberry Pi](build_rpi.md) or [Arm64-based boards](build_arm64.md) (for boards
such as Odroid C2, Pine64, and NanoPi).

### Microcontrollers

[TensorFlow Lite for Microcontrollers](../microcontrollers) is an experimental
port of TensorFlow Lite aimed at microcontrollers and other devices with only
kilobytes of memory.

### Operations

If your model requires TensorFlow operations that are not yet implemented in
TensorFlow Lite, you can use [TensorFlow Select](ops_select.md) to use them in
your model. You'll need to build a custom version of the interpreter that
includes the TensorFlow operations.

You can use [Custom operators](ops_custom.md) to write your own operations, or
port new operations into TensorFlow Lite.

[Operator versions](ops_version.md) allows you to add new functionalities and
parameters into existing operations.

## 4. Optimize your model

<a id="4_optimize_your_model_optional"></a>

TensorFlow Lite provides tools to optimize the size and performance of your
models, often with minimal impact on accuracy. Optimized models may require
slightly more complex training, conversion, or integration.

Machine learning optimization is an evolving field, and TensorFlow Lite's
[Model Optimization Toolkit](#model-optimization-toolkit) is continually growing
as new techniques are developed.

### Performance

The goal of model optimization is to reach the ideal balance of performance,
model size, and accuracy on a given device.
[Performance best practices](../performance/best_practices.md) can help guide
you through this process.

### Quantization

By reducing the precision of values and operations within a model, quantization
can reduce both the size of model and the time required for inference. For many
models, there is only a minimal loss of accuracy.

The TensorFlow Lite converter makes it easy to quantize TensorFlow models. The
following Python code quantizes a `SavedModel` and saves it to disk:

```python
import tensorflow as tf

converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_quantized_model = converter.convert()
open("converted_model.tflite", "wb").write(tflite_quantized_model)
```

TensorFlow Lite supports reducing precision of values from full floating point
to half-precision floats (float16) or 8-bit integers. There are trade-offs in
model size and accuracy for each choice, and some operations have optimized
implementations for these reduced precision types.

To learn more about quantization, see
[Post-training quantization](../performance/post_training_quantization.md).

### Model Optimization Toolkit

The [Model Optimization Toolkit](../performance/model_optimization.md) is a set
of tools and techniques designed to make it easy for developers to optimize
their models. Many of the techniques can be applied to all TensorFlow models and
are not specific to TensorFlow Lite, but they are especially valuable when
running inference on devices with limited resources.

## Next steps

Now that you're familiar with TensorFlow Lite, explore some of the following
resources:

*   If you're a mobile developer, visit [Android quickstart](android.md) or
    [iOS quickstart](ios.md).
*   If you're building Linux embedded devices, see the [Python quickstart](
    python.md) or C++ build instructions for [Raspberry Pi](build_rpi.md) and
    [Arm64-based boards](build_arm64.md).
*   Explore our [pre-trained models](../models).
*   Try our [example apps](https://www.tensorflow.org/lite/examples).
