Brad Miro talks TensorFlow 2.0 features and how Google is using it internally

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TensorFlow 2.0, released in October, has got developers excited about a myriad of features and its ease of use.

At the EuroPython Conference 2019, Brad Miro, developer programs engineer at Google talked about the updates being made to TensorFlow 2.0. He also gave an overview of how Google is using TensorFlow, moving on to why Python is important for TensorFlow development and how to migrate from TF 1.x to TF 2.0. EuroPython is one of the most popular Python programming language community conferences. Below are some highlights from Brad’s talk at EuroPython.

What is TensorFlow?

TensorFlow, an open-source deep learning library developed at Google, first released in 2015. It’s a Python framework that includes a number of utilities for helping you write deep neural networks supporting both GPUs and TPUs. A lot of deep learning involves using mathematics, statistics, and algebra and perform low-level optimizations with your system. TensorFlow removes a lot of those abstractions leaving you to focus on actually writing your model.

How TensorFlow is used internally at Google

Tensorflow is used internally at Google to power all of its machine learning and AI. Google’s data centers are powered using AI and TensorFlow to help optimize the usage of these data centers to reduce bandwidth, to ensure network connections are optimized, and to reduce power consumption. TensorFlow also is useful for performing global localization in Google Maps. It is also used heavily in the Google Pixel range of smartphones to help optimize the software. These technologies are also used in medical research specifically in the field of Computer Vision. For example, Tensorflow is used to distinguish between the retinal image of a healthy eye from the retinal image of an eye that has diabetic retinopathy.

Further Learning

If you want to learn to build more computer vision applications with TensorFlow 2.0, check out the book Hands-On Computer Vision with TensorFlow 2 by Benjamin Planche, and Eliot Andres. This book by Packt Publishing is a practical guide to building high-performance systems for object detection, segmentation, video processing, smartphone applications, and more. By the end of the book, you will have both the theoretical understanding and practical skills to solve advanced computer vision problems with TensorFlow 2.0.

Furthermore, Google is using AI and TensorFlow to predict whether or not objects in space are planets. To summarize, they use AI to predict whether or not fluctuations in the brightness of an object is due to it being a planet.

Why Python is so important for TensorFlow

Python has always been the choice for TensorFlow due to the language being extremely easy to use and having a rich ecosystem for data science including tools such as numpy, scikit-learn, and pandas. When TensorFlow was being built, the idea was that it should have the simplicity of numpy, performance of C but ease of use of Python.

What does TensorFlow 2.0 bring to the table

TensorFlow 2.0 is powerful, flexible, scalable and easily deployable.

What’s gone

Session.run
tf.control_dependencies
tf.global_variables_initializer
tf.cond, tf.while_loop
Tf.contrib

What’s new

Eager execution enabled by default
tf.function
Keras as main high level API
Distribution Strategy API
SavedModel API

TensorFlow 2.0 had a major API Cleanup. Many API symbols are removed or renamed for better consistency and clarity.

Session.run has been replaced with eager execution which effectively means that your tensorflow code runs like numpy code. Eager execution enables fast iteration and intuitive debugging without building a graph. It also makes creating and experimenting with models using TensorFlow easier. It can be especially useful when using the tf.keras model subclassing API.

TensorFlow 2.0 has tf.function, a python decorator that lets you run regular Python code which is later compiled down to TensorFlow code using AutoGraph.

The Distribution Strategy API in TensorFlow 2.0 allows machine learning researchers to distribute training across a wide variety of compute configurations. This release also allows distributed training with Keras’ model.fit and custom training loops.

Keras is introduced as the main high-level API. Keras is a popular high-level API used for easy and fast prototyping, building, and training of deep learning models. This will enable developers to easily leverage their various model-building APIs. Using Keras with TensorFlow has two main methods.

Symbolic (Keras sequential)

Your model is a graph of layers
Any graph you compile will run
TensorFlow helps you debug by catching errors at compile time

Imperative method (Keras subclassing)

Your model is Python bytecode
Complete flexibility and control
Harder to debug/ Harder to maintain
There are pros and cons of using each method; it really just depends on what your specific use cases are.

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.

On Tensorflow Hub, you can store and download pre-built models. You can use TensorFlow Extended which is Python library that can be run on your servers to productionalize your models. TensorFlow Lite lets you run your TensorFlow models on edge devices. With TensorFlow.js, you can run machine learning models using javascript in the browser or run them on servers using node.

TensorFlow also has Swift for TensorFlow to help developers use Swift to develop machine learning models. “Swift for TensorFlow provides a new programming model that combines the performance of graphs with the flexibility and expressivity of Eager execution, with a strong focus on improved usability at every level of the stack. This is not just a TensorFlow API wrapper written in Swift — we added compiler and language enhancements to Swift to provide a first-class user experience for machine learning developers.”

Other packages that exist in the TensorFlow ecosystem used for niche use cases are TF Probability, TF Agents (reinforcement learning), Tensor2Tensor, TF Ranking, TF Text (natural language processing), TF Federated, TF privacy and more.

How to upgrade from TensorFlow 1.x to TensorFlow 2.0

There are several migration guides available on TensorFlow’s website. You can also use the tf.compat.v1 library for backwards compatibility and the tf_upgrade_v2 script which you can execute on top of any Python script to convert TF 1.x code to 2.0 code. You can also read more about TF 2.0 migration in our book Hands-On Computer Vision with TensorFlow 2 which introduces the automatic migration tool and compares TensorFlow 1 concepts with their TensorFlow 2 counterparts with a detailed guide on migrating to idiomatic TensorFlow 2 code.

You can watch Brad’s full talk on YouTube. This video is licensed under the CC BY-NC-SA 3.0 license.