At this week’s 2019 TensorFlow Dev Summit, Google announced a major upgrade on the framework, the TensorFlow 2.0 Alpha version. TensorFlow 2.0 focuses on simplicity and ease of use, with updates like eager execution, intuitive higher-level APIs, and flexible model building on any platform.
Last August Google Brain Software Engineer Martin Wicke posted in Google Groups that TensorFlow 2.0 would be a major milestone, which led many in the machine learning community to expect the following upgrades:
According to the TensorFlow 2.0 official guide, Google has delivered on the expectations. The new release removes redundant APIs, makes APIs more consistent (Unified RNNs, Unified Optimizers), and better integrates Python runtime with Eager execution.
Here are the major changes:
The overarching goal of TensorFlow 2.0 Alpha is to lower the tech barrier for machine learning, enabling researchers with limited experience or expertise to build models efficiently. For example, easy model building is enabled with Keras, which provides Sequential, Functional, and Subclassing as model-building APIs to enable developers to use the right level of abstraction.
Google is also partnering with online learning platforms such as Fast.ai, Udacity, and Coursera to offer a range of courses, specializations, and degrees for TensorFlow learners. Andrew Ng announced today that Deeplearning.ai ‘s newest specialization course, TensorFlow: From Basics to Mastery, will teach useful TensorFlow practices.
There was one more surprise: Google introduced Coral Dev Board, a machine learning platform for building AI products on the edge. The US$149.99 dev board features Edge TPU, a cut-down Google ASIC designed to empower TensorFlow Lite machine learning models and accelerate inference at the edge. Google also released the Coral USB Accelerator for adding the Edge TPU to an existing design, enabling easy integration into any Linux system (including Raspberry Pi boards) over USB 2.0 and 3.0.
TensorFlow is an open source software library for computation developed by the Google Brain team. Its robust machine learning framework has enabled broad usage across many different platforms.
Journalist: Fangyu Cai, Tony Peng| Editor: Michael Sarazen
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