Abstract:

This video tutorial provides a comprehensive guide on installing TensorFlow Lite (TFLite) C++ from source on a Linux system. It outlines all necessary prerequisites, including the installation and configuration of Python packages, the Java Development Kit (JDK), and the Bazel build tool. The presenter demonstrates how to download and configure the TensorFlow source code, compile the TFLite library with optimized settings, and systematically organize the resulting build artifacts for straightforward integration into custom C++ projects. The successful installation is rigorously verified by building and executing a minimal TFLite C++ example using a pre-trained model.

Comprehensive Guide: TensorFlow Lite C++ Installation on Linux

• 0:02 TFLite C++ Installation on Linux: This video offers a detailed tutorial on installing TensorFlow Lite C++ on Linux by building the library directly from source.
• 0:15 Python and Essential Packages: Python 3.8 or higher is required, with numpy, keras-preprocessing, absl-py, wheel, and setuptools to be installed using pip.
• 0:45 Java Development Kit (JDK): The Java Development Kit (version 8 or newer) must be installed.
• 0:54 Bazel Build Tool Setup: Bazel (version 3.7.2) is installed from bazelbuild.github.io, renamed to "bazel," and its path is added to the system's ~/.bashrc file for global access.
• 1:56 Download TensorFlow Source: The TensorFlow source code (specifically version 2.4.1) is downloaded from GitHub, extracted, and the terminal is navigated into its directory.
• 2:16 Configure Build Environment: The ./configure script is executed within the TensorFlow directory. Key configurations include specifying Python paths, disabling ROCm and GPU support (as TFLite C++ on Linux doesn't natively support NVIDIA GPUs), skipping Android and iOS builds, and enabling optimization settings.
• 2:46 Build TensorFlow Lite Library: The TFLite library is compiled using the Bazel command: bazel build -c opt //tensorflow/lite:tensorflowlite.tflib for an optimized build.
• 3:19 Organize Compiled Files: The compiled tensorflowlite.tflib library, along with necessary include directories (tensorflow and flatbuffers from the source), are copied into a new tflight_dist folder, structured for easy reuse in C++ projects.
• 4:33 Verify with Example Project: A simple C++ project, tf_light_check, is created to verify the installation, featuring a CMakeLists.txt file and main.cpp based on the official TensorFlow Lite minimal example.
• 5:52 Build and Run Example: The C++ example project is built using CMake (mkdir build, cd build, cmake .., make). A MobileNet .tflite model (downloaded from TensorFlow Lite examples) is then used to test the compiled executable (./tflight_check --model_path downloads/mobilenet.tflite), successfully demonstrating TFLite functionality.
• 6:14 GitHub Repository: All project code and configuration files are made available on a GitHub repository for reference.