GSP076

Overview

This lab will give you hands-on practice with TensorFlow 2.x model training, both locally and on AI Platform. After training, you will learn how to deploy your model to AI Platform for serving (prediction). You'll train your model to predict income category of a person using the United States Census Income Dataset.

This lab gives you an introductory, end-to-end experience of training and prediction on AI Platform. The lab will use a census dataset to:

• Create a TensorFlow 2.x training application and validate it locally.
• Run your training job on a single worker instance in the cloud.
• Deploy a model to support prediction.
• Request an online prediction and see the response.

What you will build

The sample builds a classification model for predicting income category based on United States Census Income Dataset. The two income categories (also known as labels) are:

• >50K — Greater than 50,000 dollars
• <=50K — Less than or equal to 50,000 dollars

The sample defines the model using the Keras Sequential API. The sample defines the data transformations particular to the census dataset, then assigns these (potentially) transformed features to either the DNN or the linear portion of the model.

Setup

Before you click the Start Lab button

Read these instructions. Labs are timed and you cannot pause them. The timer, which starts when you click Start Lab, shows how long Google Cloud resources will be made available to you.

This Qwiklabs hands-on lab lets you do the lab activities yourself in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials that you use to sign in and access Google Cloud for the duration of the lab.

What you need

To complete this lab, you need:

• Access to a standard internet browser (Chrome browser recommended).
• Time to complete the lab.

Note: If you already have your own personal Google Cloud account or project, do not use it for this lab.

Note: If you are using a Pixelbook, open an Incognito window to run this lab.

Launch AI Platform Notebooks

To launch AI Platform Notebooks:

1. Click on the Navigation Menu and navigate to AI Platform, then to Notebooks.

2. On the Notebook instances page, click New Instance. Select the latest version of TensorFlow 2.x without GPUs.

In the pop-up, confirm the name of the deep learning VM, for Region, select us-central1 and for Zone, select a zone within that region. Leave the remaining fields with their default and click Create.

The new VM will take 2-3 minutes to start.

3. Click Open JupyterLab. A JupyterLab window will open in a new tab.

Clone the example repo within your AI Platform Notebooks instance

To clone the training-data-analyst notebook in your JupyterLab instance:

1. In JupyterLab, click the Terminal icon to open a new terminal.

2. At the command-line prompt, type in the following command and press Enter.

git clone https://github.com/GoogleCloudPlatform/training-data-analystcontent_copy

3. Confirm that you have cloned the repository by double clicking on the training-data-analyst directory and ensuring that you can see its contents. The files for all the Jupyter notebook-based labs throughout this course are available in this directory.

Navigate to the example notebook

In AI Platform Notebooks, navigate to training-data-analyst/self-paced-labs/ai-platform-qwikstart and open ai_platform_qwik_start.ipynb.

Clear all the cells in the notebook (look for the Clear button on the notebook toolbar) and then Run the cells one by one.

When prompted, come back to these instructions to check your progress.

Run your training job in the cloud

Test Completed Tasks - Step 3.1

Click Check my progress to verify your performed task.

Set up a Cloud Storage bucket.

Click Check my progress to verify your performed task.

Upload the data files to your Cloud Storage bucket.

Test Completed Task - Step 3.2

Click Check my progress to verify your performed task.

Run a single-instance trainer in the cloud.

Test Completed Tasks - Step 3.3

Click Check my progress to verify your performed task.

Create an AI Platform model.

Click Check my progress to verify your performed task.

Create a version v1 of your model.

Test your Understanding

Below are a multiple choice questions to reinforce your understanding of this lab's concepts. Answer them to the best of your abilities.

Congratulations!

In this lab you've learned how to train a TensorFlow model both locally and on AI Platform, and then how to use your trained model for prediction.

Finish your quest

This self-paced lab is part of the Qwiklabs Machine Learning APIs, Baseline: Data, ML, AI, Intro to ML: Language Processing, Intro to ML: Image Processing and Explore Machine Learning Models with Explainable AI Quests. A Quest is a series of related labs that form a learning path. Completing a Quest earns you a badge to recognize your achievement. You can make your badge (or badges) public and link to them in your online resume or social media account. Enroll in a Quest and get immediate completion credit if you've taken this lab. See other available Qwiklabs Quests.

Take your next lab

Try out another lab on Machine Learning APIs, like Extract, Analyze, and Translate Text from Images with the Cloud ML APIs or Awwvision: Cloud Vision API from a Kubernetes Cluster.

This lab is also part of a series of labs called Qwik Starts. These labs are designed to give you a little taste of the many features available with Google Cloud. Search for "Qwik Starts" in the lab catalog to find the next lab you'd like to take!

Next steps

• Sign up for the full Coursera Course on Machine Learning.
• Explore a wide range of TensorFlow tutorials.
• You can read more about wide and deep models in the Google Research Blog post named Wide & Deep Learning: Better Together with TensorFlow.
• Get your own version of Tensorflow.

Google Cloud Training & Certification

...helps you make the most of Google Cloud technologies. Our classes include technical skills and best practices to help you get up to speed quickly and continue your learning journey. We offer fundamental to advanced level training, with on-demand, live, and virtual options to suit your busy schedule. Certifications help you validate and prove your skill and expertise in Google Cloud technologies.

Manual Last Updated: July 29, 2020

Lab Last Tested: July 20, 2020

Copyright 2020 Google LLC All rights reserved. Google and the Google logo are trademarks of Google LLC. All other company and product names may be trademarks of the respective companies with which they are associated.