Build TensorFlow Lite for ARM64 boards

This page describes how to build the TensorFlow Lite static and shared libraries for ARM64-based computers. If you just want to start using TensorFlow Lite to execute your models, the fastest option is to install the TensorFlow Lite runtime package as shown in the Python quickstart.

Note: This page shows how to compile only the C++ static and shared libraries for TensorFlow Lite. Alternative install options include: install just the Python interpreter API (for inferencing only); install the full TensorFlow package from pip; or build the full TensorFlow package.

Cross-compile for ARM64 with Make

To ensure the proper build environment, we recommend using one of our TensorFlow Docker images such as tensorflow/tensorflow:devel.

To get started, install the toolchain and libs:

sudo apt-get update
sudo apt-get install crossbuild-essential-arm64

If you are using Docker, you may not use sudo.

Now git-clone the TensorFlow repository (https://github.com/tensorflow/tensorflow)—if you're using the TensorFlow Docker image, the repo is already provided in /tensorflow_src/—and then run this script at the root of the TensorFlow repository to download all the build dependencies:

./tensorflow/lite/tools/make/download_dependencies.sh

Note that you only need to do this once.

Then compile:

./tensorflow/lite/tools/make/build_aarch64_lib.sh

This should compile a static library in: tensorflow/lite/tools/make/gen/linux_aarch64/lib/libtensorflow-lite.a.

Compile natively on ARM64

These steps were tested on HardKernel Odroid C2, gcc version 5.4.0.

Log in to your board and install the toolchain:

sudo apt-get install build-essential

Now git-clone the TensorFlow repository (https://github.com/tensorflow/tensorflow) and run this at the root of the repository:

./tensorflow/lite/tools/make/download_dependencies.sh

Note that you only need to do this once.

Then compile:

./tensorflow/lite/tools/make/build_aarch64_lib.sh

This should compile a static library in: tensorflow/lite/tools/make/gen/linux_aarch64/lib/libtensorflow-lite.a.

Cross-compile for ARM64 with Bazel

You can use ARM GCC toolchains with Bazel to build an ARM64 shared library.

Note: The generated shared library requires glibc 2.28 or higher to run.

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.

Note: If you're using the TensorFlow Docker image, Bazel is already available.

Step 2. Clone TensorFlow repository

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

Note: If you're using the TensorFlow Docker image, the repo is already provided in /tensorflow_src/.

Step 3. Build ARM64 binary

C library

bazel build --config=elinux_aarch64 -c opt //tensorflow/lite/c:libtensorflowlite_c.so

Check TensorFlow Lite C API page for the detail.

C++ library

bazel build --config=elinux_aarch64 -c opt //tensorflow/lite:libtensorflowlite.so

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

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.

Etc

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