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.

The article TensorFlow Optimizations on Modern Intel® Architecture introduces the specific graph optimizations, performance experiments, and details for building and installing TensorFlow with ...

Graph Optimizations We introduced a number of graph optimization passes to: 1. Replace default TensorFlow operations with Intel optimized versions when running on CPU.

Introduction Machine Learning (ML) stands as one of the most revolutionary technologies of our era, reshaping industries and creating new frontiers in data analysis and automation. At the heart of ...

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.

On its blog, Google just announced it has built a custom “TPU” (Tensor Processing Unit) ASIC chip for machine learning and it’s specifically optimized for TensorFlow.