What is the architecture of TensorFlow?

What is the architecture of TensorFlow?

TensorFlow's architecture is designed to facilitate the development of machine learning models. It consists of a flexible architecture that allows for deployment across various platforms, including CPUs, GPUs, and TPUs.

Can you explain the components of TensorFlow's architecture?

Sure! The main components include the TensorFlow core, which handles the computation graph, and the high-level APIs like Keras for easier model building. There's also the TensorFlow Serving for deploying models and TensorFlow Lite for mobile and embedded devices.

What is a computation graph?

A computation graph is a way to represent the operations and data flow in TensorFlow. Nodes in the graph represent operations, while edges represent the tensors that flow between them. This allows TensorFlow to optimize and execute computations efficiently.

How does TensorFlow handle distributed computing?

TensorFlow supports distributed computing through its distributed strategies, allowing you to train models across multiple devices and machines. This is achieved by partitioning the data and computations, enabling parallel processing.