It uses a domain-specific compiler for linear algebra (XLA) to JIT-compile subgraphs of TensorFlow computations (data flow graphs). A version of XLA that supports Google Tensor Processing Units ...

Here are the nitty-gritty details: the TensorFlow system uses data flow graphs. In this system ... It’s difficult to lay out a concrete diagram of machine intelligence research at Google ...

data center dual GPU. The first part of Google's paper discusses the various types of deep neural networks it deploys, the specific benchmarks it uses, and offers a diagram of the TPU's physical ...

Machine learning frameworks like Google’s TensorFlow ease the process of acquiring data, training models, serving predictions, and refining future results. Created by the Google Brain team and ...

Recently released TensorFlow v2.9 introduces a new API for the model, data, and space-parallel (aka spatially tiled) deep network training. DTensor aims to decouple sharding directives from the model ...

This allows TensorFlow programs to access all of the data companies process on Apache Spark. According to Yahoo, transferring TensorFlow programs to TensorFlowOnSpark is a relatively painless ...

James Vincent is a senior reporter who has covered AI, robotics, and more for eight years at The Verge. Google has announced a new module for its machine learning framework, TensorFlow, that lets ...

LinkedIn today announced it has open sourced a tool it developed to convert Apache Spark-based Big Data into a format consumable by TensorFlow, the popular open source platform for machine learning.