By design, TensorFlow is able to tackle a much wider range of machine learning problems than its predecessor. It allows users to design deep neural networks and run them on a single smartphone or across thousands of computers in data centers. As Jeff explains, “We wanted to keep the scalable attributes and production-readiness of our first system, but make it a much more flexible platform.” Building from scratch allowed us to take advantage of some of the exciting ideas we had seen externally and optimize for the newest hardware. Just a few months into the project, the team decided to make another ambitious choice: once TensorFlow was built and ready for production, they agreed they would open source it for anyone to use. “The thinking was that if we could share source code, it could accelerate progress in machine learning for everyone,” explained TensorFlow Director Rajat Monga.
Over the months that followed, teams across the company stress-tested and polished the framework until it was ready for release. TensorFlow quickly rose in popularity as a machine learning system at Google, powering ML implementations in products like Search, Gmail, Translate and more.
With over 50 internal teams using TensorFlow, we saw first-hand what it could do for our own products, but knew that these use cases were just the beginning. Since we open sourced the project in 2015, it’s become the most popular machine learning library on GitHub, with over 13 million downloads and over 1,300 contributors outside of Google. And each of those users comes with a story to tell. We’ve seen amazing work from researchers, developers, startups and enterprises across industries using TensorFlow to find creative solutions to a wide range of challenges.