Build TensorFlow Lite for ARM boards

This page describes how to build the TensorFlow Lite libraries for ARM-based computers.

TensorFlow Lite supports two build systems and supported features from each build system are not identical. Check the following table to pick a proper build system.

Feature Bazel CMake
Predefined toolchains armhf, aarch64 armel, armhf, aarch64
Custom toolchains harder to use easy to use
Select TF ops supported not supported
GPU delegate only available for Android any platform that supports OpenCL
XNNPack supported supported
Python Wheel supported supported
C API supported supported
C++ API supported for Bazel projects supported for CMake projects

Cross-compilation for ARM with CMake

If you have a CMake project or if you want to use a custom toolchain, you'd better use CMake for cross compilation. There is a separate Cross compilation TensorFlow Lite with CMake page available for this.

Cross-compilation for ARM with Bazel

If you have a Bazel project or if you want to use TF ops, you'd better use Bazel build system. You'll use the integrated ARM GCC 8.3 toolchains with Bazel to build an ARM32/64 shared library.

Target Architecture Bazel Configuration Compatible Devices
armhf (ARM32) --config=elinux_armhf RPI3, RPI4 with 32 bit Raspberry Pi OS
AArch64 (ARM64) --config=elinux_aarch64 Coral, RPI4 with Ubuntu 64 bit

The following instructions have been tested on Ubuntu 16.04.3 64-bit PC (AMD64) and TensorFlow devel docker image tensorflow/tensorflow:devel.

To cross compile TensorFlow Lite with Bazel, follow the steps:

Step 1. Install Bazel

Bazel is the primary build system for TensorFlow. Install the latest version of the Bazel build system.

Step 2. Clone TensorFlow repository

git clone tensorflow_src

Step 3. Build ARM binary

C library
bazel build --config=elinux_aarch64 -c opt //tensorflow/lite/

You can find a shared library in: bazel-bin/tensorflow/lite/c/

Check TensorFlow Lite C API page for the detail.

C++ library
bazel build --config=elinux_aarch64 -c opt //tensorflow/

You can find a shared library in: bazel-bin/tensorflow/lite/

Currently, there is no straightforward way to extract all header files needed, so you must include all header files in tensorflow/lite/ from the TensorFlow repository. Additionally, you will need header files from FlatBuffers and Abseil.


You can also build other Bazel targets with the toolchain. Here are some useful targets.

  • //tensorflow/lite/tools/benchmark:benchmark_model
  • //tensorflow/lite/examples/label_image:label_image