Abstract:

This video provides a comprehensive tutorial on installing TensorFlow Lite C++ on macOS by building it directly from source. The guide meticulously covers all necessary prerequisites, including Python package installations (numpy, keras-preprocessing), the setup of the Bazel build tool, and the download and configuration of the TensorFlow source code. It then demonstrates the process of compiling the TensorFlow Lite library and organizing the resulting artifacts for C++ project integration. The installation is thoroughly verified by building and executing a minimal TensorFlow Lite C++ example with a pre-trained model.

Summary:

• 0:03 The video outlines the process of installing TensorFlow Lite C++ on macOS.
• 0:15 The first step involves navigating to the official TensorFlow website's "Build from Source" guide for macOS.
• 0:27 It advises installing Python if not already present, noting that Anaconda users can skip this.
• 0:35 numpy and keras-preprocessing Python packages are installed using pip.
• 0:48 The Bazel build tool is installed by downloading its macOS release, renaming it to bazel, granting executable permissions (chmod +x), and moving it to /usr/local/bin for system-wide access.
• 1:30 TensorFlow source files (version 2.4.1) are downloaded as a zip, unzipped, and the terminal is navigated into the extracted directory.
• 1:49 The ./configure command is run to set up the build environment, specifying Python paths and opting out of unnecessary features like OR/OCM, Keras, fresh Clang, Android, and iOS builds.
• 2:13 The TensorFlow Lite library is built using Bazel with the command bazel -c opt //tensorflow/lite:tensorflowlite.tflib for an optimized compilation.
• 2:42 The compiled tensorflowlite.tflib library and essential include directories (tensorflow and flatbuffers from the source) are copied into a new tflight_dist folder, structured for reuse in C++ projects.
• 4:07 A simple C++ project named tf_light_check is created to verify the installation, including a CMakeLists.txt file (requiring CMake, which can be installed via Homebrew) and main.cpp using the official TensorFlow Lite minimal example.
• 5:56 The C++ project is built by creating a build directory, navigating into it, and executing cmake .. followed by make.
• 6:05 A .tflite model (e.g., MobileNet) is downloaded from the TensorFlow Lite examples website to test the executable.
• 6:23 The tf_light_check executable is run with the downloaded model (./tflight_check --model_path downloads/mobilenet.tflite), demonstrating successful functionality.
• 6:40 All project code is made available on a GitHub repository for reference.