Building the Future of TensorFlow

Posted by the TensorFlow team

nearly seven years ago, on November 9, 2015. Since then, thanks to thousands of open-source contributors and our incredible community of Google Developer Experts, community organizers, researchers, and educators around the globe, TensorFlow has come to define its category. 

Today, TensorFlow is the most-used machine learning platform, adopted by millions of developers. It’s the 3rd most-starred software repository on GitHub (right behind Vue and React) and the most-downloaded machine learning package on PyPI. It has brought machine learning to the mobile ecosystem: TFLite now runs on four billion devices (maybe on yours, too!). TensorFlow has also brought machine learning to the Web: TensorFlow.js is now downloaded 170 thousand times weekly.

Across Google's product lineup, TensorFlow powers virtually all production machine learning, from Search, GMail, YouTube, Maps, Play, Ads, Photos, and many more. Beyond Google, at other Alphabet companies, TensorFlow and Keras enable the machine intelligence in Waymo's self-driving cars. 

In the broader industry, TensorFlow powers machine learning systems at thousands of companies, including most of the largest machine learning users in the world – Apple, ByteDance, Netflix, Tencent, Twitter, and countless more. And in the research world, every month, Google Scholar is indexing over 3,000 new scientific publications that mention TensorFlow or Keras.

Today, our user base and developer ecosystem are larger than ever, and growing!

We see the growth of TensorFlow not just as an achievement to celebrate, but as an opportunity to go further and deliver more value for the machine learning community.

Our goal is to provide the best machine learning platform on the planet. Software that will become a new superpower in the toolbox of every developer. Software that will turn machine learning from a niche craft into an industry as mature as web development.

To achieve this, we listen to the needs of our users, anticipate new industry trends, iterate on our APIs, and work to make it increasingly easy for you to innovate at scale. In the same way that TensorFlow originally helped the rise of deep learning, we want to continue to facilitate the evolution of machine learning by giving you the platform that lets you push the boundaries of what's possible. Machine learning is evolving rapidly, and so is TensorFlow.

Today, we're excited to announce we've started working on the next iteration of TensorFlow that will enable the next decade of machine learning development. We are building on TensorFlow's class-leading capabilities, and focusing on four pillars.

Four pillars of TensorFlow

Fast and scalable

• XLA Compilation. We are focusing on XLA compilation and aim to make most model training and inference workflows faster on GPU and CPU, building on XLA’s performance wins on TPU. We intend for XLA to become the industry-standard deep learning compiler, and we’ve opened it up to open-source collaboration as part of the , a new API for large-scale model parallelism. DTensor unlocks the future of ultra-large model training and deployment and allows you to develop your model as if you were training on a single device, even while using multiple clients. DTensor will be unified with the tf.distribute API, allowing for flexible model and data parallelism.