Introduction

TensorFlow Lite (TFLite) is an open-source deep learning framework used predominantly for deploying machine learning models on mobile and embedded devices. While TensorFlow Lite natively supports platforms like Android and iOS, there may be a need to build it for other platforms, such as Linux, especially for edge devices or different Linux distributions. This article provides a comprehensive guide to building TensorFlow Lite on Linux, including technical nuances and examples.

Prerequisites

Before embarking on building TensorFlow Lite, the following prerequisites must be met:

• Bazel: TensorFlow Lite uses Bazel as its build system. Ensure that Bazel (version 0.29.1 or later) is installed on your Linux distribution.
• Python: Python 3 should be installed as it’s required for running various script utilities.
• CMake: For optional build configurations and required for the C++ API.
• GCC Compiler: A modern C++ compiler, ideally GCC, should be available. TensorFlow Lite is compatible with GCC 4.8 and above.
• Git: Essential for fetching the TensorFlow repository.

Fetching TensorFlow Repository

First, clone the TensorFlow repository from GitHub. Ensure your system is updated to manage permissions and dependencies more efficiently.

• Python Path: Enter the path where Python is installed.
• Python Libraries: Specify the libraries path for Python.
• CUDA Support: Deny CUDA support if not using Nvidia GPUs.
• libtensorflowlite.so: This command builds the shared library for TensorFlow Lite.
• quantize_weights: Performs optimization by quantizing weights, reducing model size and improving performance.
• libtensorflowlite_c.so: If using the C++ API, this additional build command is necessary.
• Bazel Version Compatibility: Ensure the installed Bazel version is compatible with the TensorFlow repository you downloaded. Discrepancies can lead to build failures.
• Library Path Issues: Incorrect library paths during configuration can cause build errors. Double-check the paths to Python and libraries.
• Dependencies: Missing dependencies, especially during the C++ build, can impede progress. Install missing packages via the package manager.