4.5 KiB
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) , TensorFlow devel docker image tensorflow/tensorflow:devel and Windows 10.
Note: This feature is currently experimental and available since version 2.4 and may change.
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 -DCMAKE_BUILD_TYPE=Debug option.
cmake ../tensorflow_src/tensorflow/lite -DCMAKE_BUILD_TYPE=Debug
Cross-compilation for Android
You can use CMake to build Android binaries. You need to install
Android NDK and provide the NDK path with
-DDCMAKE_TOOLCHAIN_FILE flag. 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
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 only on master(r2.5) branch. There could be compatbility 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
In the tflite_build directory,
cmake --build . -j -t benchmark_model
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.
| Option Name | Feature | Default |
|---|---|---|
| TFLITE_ENABLE_RUY | Enable RUY matrix multiplication library | OFF |
| TFLITE_ENABLE_NNAPI | Enable NNAPI delegate | ON (Android) |
| TFLITE_ENABLE_GPU | Enable GPU delegate | OFF |
| TFLITE_ENABLE_XNNPACK | Enable XNNPACK delegate | ON |
| TFLITE_ENABLE_MMAP | Enable MMAP (unsupported on Windows) | ON |
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 ${CMAKE_DL_LIBS}