Overview

TPUs are very fast, and the stream of training data must keep up with their training speed. In this lab, you will learn how to load data from Cloud Storage with the tf.data.Dataset API to feed your TPU.

Objectives

You will learn:

• To use the tf.data.Dataset API to load training data.
• To use TFRecord format to load training data efficiently from Cloud Storage.

Setup and requirements

For each lab, you get a new Google Cloud project and set of resources for a fixed time at no cost.

1. Sign in to Qwiklabs using an incognito window.

2. Note the lab's access time (for example, 1:15:00), and make sure you can finish within that time.
   There is no pause feature. You can restart if needed, but you have to start at the beginning.

3. When ready, click Start lab.

4. Note your lab credentials (Username and Password). You will use them to sign in to the Google Cloud Console.

5. Click Open Google Console.

6. Click Use another account and copy/paste credentials for this lab into the prompts.
   If you use other credentials, you'll receive errors or incur charges.

7. Accept the terms and skip the recovery resource page.

Set up your environment

Enable All Recommended API

1. Click to open the Vertex AI Dashboard.

2. Click Enable All Recommended APIS.

Task 1. Launch Vertex AI Notebooks

To create and launch a Vertex AI Workbench notebook:

1. In the Navigation Menu Navigation menu icon, click Vertex AI > Workbench.

2. On the User-Managed Notebook page, click Enable Notebooks API (if it isn't enabled yet), then click Create New.

3. In the New instance menu, choose the latest version of TensorFlow Enterprise 2.11 (Intel® MKL-DNN/MKL) in Environment.

4. Name the notebook.

5. Set Region to and Zone to .

6. Leave the remaining fields at their default and click Create.

After a few minutes, the Workbench page lists your instance, followed by Open JupyterLab.

7. Click Open JupyterLab to open JupyterLab in a new tab. If you get a message saying beatrix jupyterlab needs to be included in the build, just ignore it.

Task 2. Clone course repo within your Vertex AI Notebooks instance

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

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

2. At the command-line prompt, run the following command:

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

3. To confirm that you have cloned the repository, double-click on the training-data-analyst directory and ensure that you can see its contents.
   The files for all the Jupyter notebook-based labs throughout this course are available in this directory.

Task 3. TPU-Speed Data Pipelines: tf.data.Dataset and TFRecords

1. In the notebook interface, navigate to training-data-analyst > courses > machine_learning > deepdive2 > production_ml > labs, and open tpu_speed_data_pipelines.ipynb.

2. In the notebook interface, click Edit > Clear All Outputs.

3. Carefully read through the notebook instructions and fill in lines marked with #TODO where you need to complete the code.

Tip: To run the current cell, click the cell and press SHIFT+ENTER. Other cell commands are listed in the notebook UI under Run.

• Hints may also be provided for the tasks to guide you. Highlight the text to read the hints, which are in white text.
• To see the complete solution, navigate to training-data-analyst > courses > machine_learning > deepdive2 > production_ml > solutions, and open tpu_speed_data_pipelines.ipynb.

End your lab

When you have completed your lab, click End Lab. Qwiklabs removes the resources you’ve used and cleans the account for you.

You will be given an opportunity to rate the lab experience. Select the applicable number of stars, type a comment, and then click Submit.

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