STT-tensorflow/tensorflow/lite/tools/cmake

Build TensorFlow Lite with CMake

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

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

Note: This is an experimental that is subject to change.

Note: The following are not currently supported: iOS, Tests and Host Tools (i.e analysis tools etc.)

Step 1. Install CMake tool

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

sudo apt-get install cmake

Or you can follow the offcial 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 and run CMake tool

mkdir tflite_build
cd tflite_build
cmake ../tensorflow_src/tensorflow/lite

It generates release binary by default. If you need to produce debug builds, you need to provide '-DCMAKE_BUILD_TYPE=Debug' option.

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

If you want to configure Android build with GPU delegate support,

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

Step 4. Build TensorFlow Lite

In the tflite_build directory,

cmake --build . -j

Or

make -j

Note: This should compile a static library libtensorflow-lite.a in the current directory.

Step 5. Build TensorFlow Lite Benchmark Tool

In the tflite_build directory,

cmake --build . -j -t benchmark_model

Or

make benchmark_model -j