TensorFlow Lite is designed to run on various platforms, including mobile devices and embedded systems. It supports the following platforms:

  1. Mobile Platforms: TensorFlow Lite has excellent support for running on mobile platforms, including both Android and iOS. It provides libraries and tools to deploy machine learning models on mobile devices efficiently. On Android, TensorFlow Lite supports both Java and C++ APIs, allowing developers to integrate machine learning directly into their Android apps. On iOS, TensorFlow Lite supports Swift and Objective-C APIs, enabling developers to leverage machine learning capabilities in their iOS apps.

  2. Embedded Systems: TensorFlow Lite also supports running on embedded systems, such as microcontrollers and single-board computers. It provides a set of tools and kernels optimized for efficient inference on resource-constrained devices. TensorFlow Lite Micro is specifically designed for running on microcontrollers, enabling edge devices to perform machine learning tasks locally without relying on network connections.

  3. Linux: TensorFlow Lite can run on Linux-based systems, such as Ubuntu, Raspberry Pi, and other popular single-board computers. It provides support for various platforms and architectures, allowing developers to deploy and run TensorFlow Lite models on Linux environments.

  4. Windows and macOS: Although originally developed for mobile and embedded platforms, TensorFlow Lite can also run on Windows and macOS. Developers can use TensorFlow Lite with Python to load and run models on desktop systems. However, it's important to note that TensorFlow Lite is primarily optimized for mobile and edge devices, and other TensorFlow variants (such as TensorFlow with GPU support) may be more suitable for high-performance computing on desktop systems.

  Overall, TensorFlow Lite offers wide platform support, allowing developers to deploy machine learning models on a variety of devices and systems, ranging from mobile phones to microcontrollers and beyond.