- 1 year ago
How to install tensorflow lite c++ on windows?
To install TensorFlow Lite C++ on Windows, you can follow these steps:
Install CMake: TensorFlow Lite requires CMake to build the library. Download and install CMake from the official CMake website: https://cmake.org/download/
Install Git: Git is required to clone the TensorFlow repository. Download and install Git from the official Git website: https://git-scm.com/downloads
Clone the TensorFlow repository: Open a terminal or command prompt and navigate to the directory where you want to clone the TensorFlow repository. Then, run the following command to clone the repository:
bashgit clone https://github.com/tensorflow/tensorflow.git
Build TensorFlow Lite C++ library:
- Open a terminal or command prompt and navigate to the
tensorflow
directory. - Configure the build using CMake:bash
cmake -DTFLITE_ENABLE_SHARED=ON -DTFLITE_BUILD_TESTS=OFF -DCMAKE_BUILD_TYPE=Release -S . -B build
- Build the library using CMake:bash
cmake --build build
This will build the TensorFlow Lite C++ library with shared libraries enabled and without tests.
- Open a terminal or command prompt and navigate to the
Install TensorFlow Lite C++ headers and libraries:
- Copy the
tensorflow/lite
directory from thetensorflow
repository to your desired location. - Copy the built
.dll
or.so
files from thetensorflow/build
directory to a location where they can be accessed by your project.
- Copy the
Set up your C++ project:
- In your C++ project, include the necessary TensorFlow Lite C++ headers.
- Link against the TensorFlow Lite C++ libraries (
tensorflow/lite/libtensorflowlite.so
ortensorflow/lite/libtensorflowlite.dll
) during compilation.
That's it! You have now installed TensorFlow Lite C++ on Windows and can use it in your C++ projects.
Please note that these steps provide a general guideline for installing TensorFlow Lite C++ on Windows. Depending on your specific setup and requirements, you may need to adjust the build options or perform additional configuration steps. For more detailed and up-to-date instructions, refer to the TensorFlow documentation or the TensorFlow GitHub repository.