TensorFlow is optimized for performance with its static graph definition. PyTorch has made strides in catching up, particularly with its TorchScript for optimizing models.

TensorFlow is available on Windows, macOS, and Linux and can be installed via Python’s pip package manager. It supports cloud platforms like Google Cloud, AWS, and Azure for enterprise deployment.

This article will discuss the seven popular tools and frameworks used for developing AI applications: TensorFlow, PyTorch, Keras, Caffe, Microsoft Cognitive Toolkit, Theano and Apache MXNet.

Google today released TensorFlow Graph Neural Networks (TF-GNN) in alpha, a library designed to make it easier to work with graph structured data using TensorFlow, its machine learning framework.

PyTorch recreates the graph on the fly at each iteration step. In contrast, TensorFlow by default creates a single data flow graph, optimizes the graph code for performance, and then trains the model.

PyTorch recreates the graph on the fly at each iteration step. In contrast, TensorFlow by default creates a single data flow graph, optimizes the graph code for performance, and then trains the model.

If this is what matters most for you, then your choice is probably TensorFlow. A network written in PyTorch is a Dynamic Computational Graph (DCG). It allows you to do any crazy thing you want to do.

TensorFlow's eager mode provides an imperative programming environment that evaluates operations immediately, without building graphs. This is similar to PyTorch's eager mode in both advantages ...

Developers can submit ML training jobs created in TensorFlow, Keras, PyTorch, Scikit-learn, and XGBoost. Google now offers in-built algorithms based on linear classifier, wide and deep and XGBoost ...