After releasing the beta version of TensorFlow 2.0 in June, Google announced its final release on Monday. This release comes with tighter integration with Keras, eager execution enabled by default, promises three times faster training performance, a cleaned-up API, and more.
Key updates in TensorFlow 2.0
Tighter Keras integration for better developer productivity
One of the important updates in TensorFlow 2.0 is its tighter integration with Keras, a popular high-level API used for easy and fast prototyping, building, and training deep learning models. This will enable developers to easily leverage its various model-building APIs including Sequential, Functional, and Subclassing.
Explaining the motivation behind this change, the TensorFlow team wrote, “By establishing Keras as the high-level API for TensorFlow, we are making it easier for developers new to machine learning to get started with TensorFlow. A single high-level API reduces confusion and enables us to focus on providing advanced capabilities for researchers.”
Eager execution enabled by default
In TensorFlow 1.x, developers were required to define an abstract data structure named Graph and to run this graph they needed an encapsulation called Session. TensorFlow 2.0 has eager execution enabled by default to “eagerly” run code, similar to normal Python code.
Eager execution enables fast iteration and intuitive debugging without building a graph. It also makes creating and experimenting with models using TensorFlow much easier. It can be especially useful when using the tf.keras model subclassing API.
Distribution Strategy API
The Distribution Strategy API in TensorFlow 2.0 allows machine learning researchers to distribute training across a wide variety of compute configurations. This will allow them to “attain great out-of-the-box performance” with minimal code changes. This release also allows distributed training with Keras’ model.fit and custom training loops.
Performance improvements on GPUs
TensorFlow 2.0 includes multi-GPU support and experimental support for multi worker and Cloud TPUs. This release also has a number of performance improvements on GPUs. It promises three times faster training performance when using mixed precision on NVIDIA’s Volta and Turing GPUs. It includes tight integration with NVIDIA TensorRT, a platform for high-performance deep learning inference.
The standardized SavedModel file format
The SavedModel API allows you to save your trained ML model into a language-neutral format. With TensorFlow 2.0, all TensorFlow ecosystem projects including TensorFlow Lite, TensorFlow JS, TensorFlow Serving, and TensorFlow Hub, support SavedModels.
Standardizing the SavedModel file format will enable developers to run their models on a variety of runtimes including the cloud, web, browser, Node.js, mobile, and embedded systems.
“This allows you to run your models with TensorFlow, deploy them with TensorFlow Serving, use them on mobile and embedded systems with TensorFlow Lite, and train and run in the browser or Node.js with TensorFlow.js,” the team writes.
TensorFlow 2.0 includes a number of API updates. Many API symbols are removed or renamed for better consistency and clarity. Also, the tf.app, tf.flags, and tf.logging API are removed in favor of abseil-py.
Because of the huge number of API changes, developers in a discussion on Hacker News expressed that transitioning from TensorFlow 1.X to TensorFlow 2.0 is quite complicated. Some also mentioned switching to PyTorch instead.
A user commented, “As someone who uses TensorFlow a lot, I predict an enormous clusterfuck of a transition. Tensorflow has turned into a multiheaded monster, supporting many things and approaches but none of them very well…In my opinion, there are some architectural problems with TF, which have not been addressed in this update…If you need to transition from TF1 to TF2, consider doing the TF1 to PyTorch transition instead.”
While some others were happy with the recommended Keras API and eager execution. “I don’t know if I’m the only one, but I actually love the changes they’ve made since v1. Eager execution and tf.function are fantastic, and the built-in Keras is even better than the standalone version. A big improvement compared to TF from last year,” a user commented on Reddit.
Another user added, “The most important change in terms of usability, IMO, is the use of tf.keras as the recommended interface to TensorFlow. There hasn’t been a case yet where I’ve needed to dip outside of Keras into raw TensorFlow, but the option is there and is easy to do. That said, TF 2.0 changes a lot. Many repos might break, so expect to see lots of tensorflow==1.14 in requirement.txt files from now on.”