Google today made available TensorFlow RunTime (TFRT), a new runtime for its TensorFlow machine learning framework that provides a unified, extensible infrastructure layer with high performance across a range of hardware. Its release in open source on GitHub follows a preview earlier this year during a session at the 2020 TensorFlow Dev Summit, where TFRT was shown to speed up core loops in a key benchmarking test. TFRT is intended to address the needs of data scientists looking for faster model iteration time and better error reporting, Google says, as well as app developers looking for improved performance while training and serving models in production. Tangibly, TFRT could reduce the time it takes to develop, validate, and deploy an enterprise-scale model, which surveys suggest can range from weeks to months (or years). And it might beat back Facebook's encroaching PyTorch framework, which continues to see rapid uptake among companies like OpenAI, Preferred Networks, and Uber.