This page describes how to build the LiteRT libraries for ARM-based computers.
LiteRT 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 |
| supported | not supported | |
| only available for Android | any platform that supports OpenCL | |
|
XNNPack
|
supported | supported |
| supported | supported | |
| supported | supported | |
| 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 LiteRT 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 LiteRT 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 ARM binary
C library
bazel
build
--config =
elinux_aarch64
-c
opt
//tensorflow/lite/c:libtensorflowlite_c.so
You can find a shared library in: bazel-bin/tensorflow/lite/c/libtensorflowlite_c.so
.
Check LiteRT 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
