Build TensorFlow Lite with CMake

This page describes how to build and use the TensorFlow Lite library with CMake tool.

The following instructions have been tested on Ubuntu 16.04.3 64-bit PC (AMD64) , macOS Catalina (x86_64), Windows 10 and TensorFlow devel Docker image tensorflow/tensorflow:devel.

Note: This feature is available since version 2.4.

Step 1. Install CMake tool

It requires CMake 3.16 or higher. On Ubuntu, you can simply run the following command.

sudo apt-get install cmake

Or you can follow the official cmake installation guide

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. Create CMake build directory

mkdir tflite_build
cd tflite_build

Step 4. Run CMake tool with configurations

Release build

It generates an optimized release binary by default. If you want to build for your workstation, simply run the following command.

cmake ../tensorflow_src/tensorflow/lite

Debug build

If you need to produce a debug build which has symbol information, you need to provide the -DCMAKE_BUILD_TYPE=Debug option.

cmake ../tensorflow_src/tensorflow/lite -DCMAKE_BUILD_TYPE=Debug

Build with kernel unit tests

In order to be able to run kernel tests, you need to provide the -DTFLITE_KERNEL_TEST=on flag. Unit test cross-compilation specifics can be found in the next subsection.

cmake ../tensorflow_src/tensorflow/lite -DTFLITE_KERNEL_TEST=on

Build installable package

To build an installable package that can be used as a dependency by another CMake project with find_package(tensorflow-lite CONFIG), use the -DTFLITE_ENABLE_INSTALL=ON option.

You should ideally also provide your own versions of library dependencies. These will also need to used by the project that depends on TF Lite. You can use the -DCMAKE_FIND_PACKAGE_PREFER_CONFIG=ON and set the <PackageName>_DIR variables to point to your library installations.

cmake ../tensorflow_src/tensorflow/lite -DTFLITE_ENABLE_INSTALL=ON \
  -DCMAKE_FIND_PACKAGE_PREFER_CONFIG=ON \
  -DSYSTEM_FARMHASH=ON \
  -DSYSTEM_PTHREADPOOL=ON \
  -Dabsl_DIR=<install path>/lib/cmake/absl \
  -DEigen3_DIR=<install path>/share/eigen3/cmake \
  -DFlatBuffers_DIR=<install path>/lib/cmake/flatbuffers \
  -Dgemmlowp_DIR=<install path>/lib/cmake/gemmlowp \
  -DNEON_2_SSE_DIR=<install path>/lib/cmake/NEON_2_SSE \
  -Dcpuinfo_DIR=<install path>/share/cpuinfo \
  -Druy_DIR=<install path>/lib/cmake/ruy

Note: Refer to CMake documentation for find_package to learn more about handling and locating packages.

Cross-compilation

You can use CMake to build binaries for ARM64 or Android target architectures.

In order to cross-compile the TF Lite, you namely need to provide the path to the SDK (e.g. ARM64 SDK or NDK in Android's case) with -DCMAKE_TOOLCHAIN_FILE flag.

cmake -DCMAKE_TOOLCHAIN_FILE=<CMakeToolchainFileLoc> ../tensorflow/lite/

Specifics of Android cross-compilation

For Android cross-compilation, you need to install Android NDK and provide the NDK path with -DCMAKE_TOOLCHAIN_FILE flag mentioned above. You also need to set target ABI with-DANDROID_ABI flag.

cmake -DCMAKE_TOOLCHAIN_FILE=<NDK path>/build/cmake/android.toolchain.cmake \
  -DANDROID_ABI=arm64-v8a ../tensorflow_src/tensorflow/lite

Specifics of kernel (unit) tests cross-compilation

Cross-compilation of the unit tests requires flatc compiler for the host architecture. For this purpose, there is a CMakeLists located in tensorflow/lite/tools/cmake/native_tools/flatbuffers to build the flatc compiler with CMake in advance in a separate build directory using the host toolchain.

mkdir flatc-native-build && cd flatc-native-build
cmake ../tensorflow_src/tensorflow/lite/tools/cmake/native_tools/flatbuffers
cmake --build .

It is also possible to install the flatc to a custom installation location (e.g. to a directory containing other natively-built tools instead of the CMake build directory):

cmake -DCMAKE_INSTALL_PREFIX=<native_tools_dir> ../tensorflow_src/tensorflow/lite/tools/cmake/native_tools/flatbuffers
cmake --build .

For the TF Lite cross-compilation itself, additional parameter -DTFLITE_HOST_TOOLS_DIR=<flatc_dir_path> pointing to the directory containing the native flatc binary needs to be provided along with the -DTFLITE_KERNEL_TEST=on flag mentioned above.

cmake -DCMAKE_TOOLCHAIN_FILE=${OE_CMAKE_TOOLCHAIN_FILE} -DTFLITE_KERNEL_TEST=on -DTFLITE_HOST_TOOLS_DIR=<flatc_dir_path> ../tensorflow/lite/

Cross-compiled kernel (unit) tests launch on target

Unit tests can be run as separate executables or using the CTest utility. As far as CTest is concerned, if at least one of the parameters TFLITE_ENABLE_NNAPI, TFLITE_ENABLE_XNNPACK or TFLITE_EXTERNAL_DELEGATE is enabled for the TF Lite build, the resulting tests are generated with two different labels (utilizing the same test executable): - plain - denoting the tests ones run on CPU backend - delegate - denoting the tests expecting additional launch arguments used for the used delegate specification

Both CTestTestfile.cmake and run-tests.cmake (as referred below) are available in <build_dir>/kernels.

Launch of unit tests with CPU backend (provided the CTestTestfile.cmake is present on target in the current directory):

ctest -L plain

Launch examples of unit tests using delegates (provided the CTestTestfile.cmake as well as run-tests.cmake file are present on target in the current directory):

cmake -E env TESTS_ARGUMENTS=--use_nnapi=true\;--nnapi_accelerator_name=vsi-npu ctest -L delegate
cmake -E env TESTS_ARGUMENTS=--use_xnnpack=true ctest -L delegate
cmake -E env TESTS_ARGUMENTS=--external_delegate_path=<PATH> ctest -L delegate

A known limitation of this way of providing additional delegate-related launch arguments to unit tests is that it effectively supports only those with an expected return value of 0. Different return values will be reported as a test failure.

OpenCL GPU delegate

If your target machine has OpenCL support, you can use GPU delegate which can leverage your GPU power.

To configure OpenCL GPU delegate support:

cmake ../tensorflow_src/tensorflow/lite -DTFLITE_ENABLE_GPU=ON

Note: It's experimental and available starting from TensorFlow 2.5. There could be compatibility issues. It's only verified with Android devices and NVidia CUDA OpenCL 1.2.

Step 5. Build TensorFlow Lite

In the tflite_build directory,

cmake --build . -j

Note: This generates a static library libtensorflow-lite.a in the current directory but the library isn't self-contained since all the transitive dependencies are not included. To use the library properly, you need to create a CMake project. Please refer the "Create a CMake project which uses TensorFlow Lite" section.

Step 6. Build TensorFlow Lite Benchmark Tool and Label Image Example (Optional)

In the tflite_build directory,

cmake --build . -j -t benchmark_model

cmake --build . -j -t label_image

Available Options to build TensorFlow Lite

Here is the list of available options. You can override it with -D<option_name>=[ON|OFF]. For example, -DTFLITE_ENABLE_XNNPACK=OFF to disable XNNPACK which is enabled by default.

Create a CMake project which uses TensorFlow Lite

Here is the CMakeLists.txt of TFLite minimal example.

You need to have add_subdirectory() for TensorFlow Lite directory and link tensorflow-lite with target_link_libraries().

cmake_minimum_required(VERSION 3.16)
project(minimal C CXX)

set(TENSORFLOW_SOURCE_DIR "" CACHE PATH
  "Directory that contains the TensorFlow project" )
if(NOT TENSORFLOW_SOURCE_DIR)
  get_filename_component(TENSORFLOW_SOURCE_DIR
    "${CMAKE_CURRENT_LIST_DIR}/../../../../" ABSOLUTE)
endif()

add_subdirectory(
  "${TENSORFLOW_SOURCE_DIR}/tensorflow/lite"
  "${CMAKE_CURRENT_BINARY_DIR}/tensorflow-lite" EXCLUDE_FROM_ALL)

add_executable(minimal minimal.cc)
target_link_libraries(minimal tensorflow-lite)

Build TensorFlow Lite C library

If you want to build TensorFlow Lite shared library for C API, follow step 1 to step 3 first. After that, run the following commands.

Linux / MacOS

cmake ../tensorflow_src/tensorflow/lite/c
cmake --build . -j

Windows

cmake ../tensorflow_src/tensorflow/lite/c
cmake --build . -j --config Release

Compiled Library

The above command generates the following shared library in the current directory.

Note: You need the public headers (tensorflow/lite/c_api.h, tensorflow/lite/c_api_experimental.h, tensorflow/lite/c_api_types.h, and tensorflow/lite/common.h), and the private headers that those public headers include (tensorflow/lite/core/builtin_ops.h, tensorflow/lite/core/c/*.h, and tensorflow/lite/core/async/c/*.h, ) to use the generated shared library.