python.md

Quickstart for Linux-based devices with Python

Using TensorFlow Lite with Python is great for embedded devices based on Linux, such as Raspberry Pi{:.external} and Coral devices with Edge TPU{:.external}, among many others.

This page shows how you can start running TensorFlow Lite models with Python in just a few minutes. All you need is a TensorFlow model converted to TensorFlow Lite. (If you don't have a model converted yet, you can experiment using the model provided with the example linked below.)

About the TensorFlow Lite runtime package

To quickly start executing TensorFlow Lite models with Python, you can install just the TensorFlow Lite interpreter, instead of all TensorFlow packages. We call this simplified Python package tflite_runtime.

The tflite_runtime package is a fraction the size of the full tensorflow package and includes the bare minimum code required to run inferences with TensorFlow Lite—primarily the Interpreter Python class. This small package is ideal when all you want to do is execute .tflite models and avoid wasting disk space with the large TensorFlow library.

Note: If you need access to other Python APIs, such as the TensorFlow Lite Converter, you must install the full TensorFlow package. For example, the [Select TF ops] (https://www.tensorflow.org/lite/guide/ops_select) are not included in the tflite_runtime package. If your models have any dependencies to the Select TF ops, you need to use the full TensorFlow package instead.

Install TensorFlow Lite for Python

You can install on Linux with pip:

python3 -m pip install tflite-runtime

Supported platforms

The tflite-runtime Python wheels are pre-built and provided for these platforms:

• Linux armv7l (e.g. Raspberry Pi 2, 3, 4 and Zero 2 running Raspberry Pi OS 32-bit)

• Linux aarch64 (e.g. Raspberry Pi 3, 4 running Debian ARM64)

• Linux x86_64

If you want to run TensorFlow Lite models on other platforms, you should either use the full TensorFlow package, or build the tflite-runtime package from source.

If you're using TensorFlow with the Coral Edge TPU, you should instead follow the appropriate Coral setup documentation.

Note: We no longer update the Debian package python3-tflite-runtime. The latest Debian package is for TF version 2.5, which you can install by following these older instructions.

Note: We no longer release pre-built tflite-runtime wheels for Windows and macOS. For these platforms, you should use the full TensorFlow package, or build the tflite-runtime package from source.

Run an inference using tflite_runtime

Instead of importing Interpreter from the tensorflow module, you now need to import it from tflite_runtime.

For example, after you install the package above, copy and run the label_image.py file. It will (probably) fail because you don't have the tensorflow library installed. To fix it, edit this line of the file:

import tensorflow as tf

So it instead reads:

import tflite_runtime.interpreter as tflite

And then change this line:

interpreter = tf.lite.Interpreter(model_path=args.model_file)

So it reads:

interpreter = tflite.Interpreter(model_path=args.model_file)

Now run label_image.py again. That's it! You're now executing TensorFlow Lite models.

Learn more

• For more details about the Interpreter API, read Load and run a model in Python.

• If you have a Raspberry Pi, check out a video series about how to run object detection on Raspberry Pi using TensorFlow Lite.

• If you're using a Coral ML accelerator, check out the Coral examples on GitHub.

• To convert other TensorFlow models to TensorFlow Lite, read about the TensorFlow Lite Converter.

• If you want to build tflite_runtime wheel, read Build TensorFlow Lite Python Wheel Package