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