We hope this guide has been helpful in your TensorFlow Lite development endeavors. By following these steps, you can take advantage of the benefits of a static library and make it easier to distribute your application. In this article, we’ve shown you how to build TensorFlow Lite as a static library and link to it from a separate CMake project. out) Step 1: Compile C code to object file. Compile the C++ code using the header library file using the shared library (using g++) Set LDLIBRARYPATH. This will build your project and link it to the TensorFlow Lite static library. There are four steps: Compile C++ library code to object file (using g++) Create shared library file (. Replace your_project with the name of your CMake target. STATIC libraries are archives of object files for use when linking other targets. Target_link_libraries(your_project tf_lite) STATIC, SHARED, or MODULE may be given to specify the type of library to be created. Link to the TensorFlow Lite library in your project:.Replace /path/to/tflite with the path to the TensorFlow Lite build directory. In order to build the library and a few test programs Im using CMake to build the library eeg on Linux and on windows using g++ and MSVC++ respectively. Set_target_properties(tf_lite PROPERTIES IMPORTED_LOCATION /path/to/tflite/lib/libtensorflow-lite.a) Create a new CMake project and add the following lines to your CMakeLists.txt file:.Now that we have built TensorFlow Lite as a static library, we can link to it from a separate CMake project. Linking to the TensorFlow Lite Static Library This will build the TensorFlow Lite static library, which will be located in the lib directory. This tells CMake to build TensorFlow Lite as a static library. Navigate to the TensorFlow Lite directory and create a build directory:.Clone the TensorFlow Lite repository from GitHub:.To build TensorFlow Lite as a static library, follow these steps: Visual Studio on Windows or Xcode on macOS)īuilding TensorFlow Lite as a Static Library Prerequisitesīefore we get started, make sure you have the following tools and software installed: Second, it makes it easier to distribute your application, as you won’t have to worry about distributing the TensorFlow Lite library separately. First, it allows you to compile the library into your application, which can result in faster start-up times and reduced memory usage. Why Build TensorFlow Lite as a Static Library?īuilding TensorFlow Lite as a static library provides several benefits. In this article, we’ll walk you through the steps to achieve this. In some cases, you may need to build TensorFlow Lite as a static library and link to it from a separate CMake project. As a data scientist, you might find yourself working with TensorFlow Lite, the lightweight version of the popular TensorFlow machine learning library designed for mobile and embedded devices.
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