Book Image

Hands-On Machine Learning on Google Cloud Platform

By : Giuseppe Ciaburro, V Kishore Ayyadevara, Alexis Perrier
Book Image

Hands-On Machine Learning on Google Cloud Platform

By: Giuseppe Ciaburro, V Kishore Ayyadevara, Alexis Perrier

Overview of this book

Google Cloud Machine Learning Engine combines the services of Google Cloud Platform with the power and flexibility of TensorFlow. With this book, you will not only learn to build and train different complexities of machine learning models at scale but also host them in the cloud to make predictions. This book is focused on making the most of the Google Machine Learning Platform for large datasets and complex problems. You will learn from scratch how to create powerful machine learning based applications for a wide variety of problems by leveraging different data services from the Google Cloud Platform. Applications include NLP, Speech to text, Reinforcement learning, Time series, recommender systems, image classification, video content inference and many other. We will implement a wide variety of deep learning use cases and also make extensive use of data related services comprising the Google Cloud Platform ecosystem such as Firebase, Storage APIs, Datalab and so forth. This will enable you to integrate Machine Learning and data processing features into your web and mobile applications. By the end of this book, you will know the main difficulties that you may encounter and get appropriate strategies to overcome these difficulties and build efficient systems.
Table of Contents (18 chapters)
8
Creating ML Applications with Firebase

Setting up TensorBoard

In the previous chapter, we understood how Datalab can be set up. Installing TensorBoard in Datalab is as simple as specifying the following code:

Note that we need not make any separate installations for TensorBoard and it comes in prebuilt within the google.datalab.ml package.

Once the package is imported, we need to start TensorBoard by specifying the location of logs that contain the summaries written by the model fitting process.

The tb.start method works as follows:

Note that, in the first step, it checks whether the user is permitted to perform the calculation. Next, it picks up an unused port to open TensorBoard, and finally it starts TensorBoard along with printing the link to open TensorBoard.

We will learn more about writing to logs in the next section.