Overview

In this lab, you build a Contextual Bandits agent in order to recommend another movie to watch (based on the Movielens dataset) to a user. For this, you first learn how to instantiate a Vertex AI Workbench notebook instance and eventually how to load data to Tensorflow (TF) and build an agent using the TF Agents library.

Learning objectives

• Install and import required libraries.
• Initialize and configure the MovieLens Environment.
• Initialize the Agent.
• Define and link the evaluation metrics.
• Initialize and configure the Replay Buffer.
• Set up and train the model.
• Observe the results of trained model and Vertex AI Tensorboard evaluation.

Setup

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.

Task 1. Set up your environment

Enable the Recommended APIs

1. In the Google Cloud Console, on the Navigation menu, click Vertex AI.
2. Click Enable All Recommended API.

Task 2. Launch a Vertex AI Workbench instance

1. In the Google Cloud Console, on the Navigation Menu, click Vertex AI > Workbench. Select User-Managed Notebooks.

2. On the Notebook instances page, Click Create New and choose the latest version of TensorFlow Enterprise 2.6 (with LTS) in Environment.

3. In the New notebook instance dialog, confirm the name of the deep learning VM, if you don’t want to change the region and zone, leave all settings as they are and then click Create. The new VM will take 2-3 minutes to start.

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

Task 3. Clone a course repo within your Vertex AI Workbench 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 4. Build a RL model in your Vertex AI Workbench instance

1. In the notebook interface, navigate to training-data-analyst > courses > machine_learning > deepdive2 > recommendation_systems > labs, and open exercise_movielens_notebook.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 along. Highlight the text to read the hints, which are in white text.
• If you need more help, look at the complete solution at training-data-analyst > courses > machine_learning > deepdive2 > recommendation_systems > solutions, and open exercise_movielens_notebook.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.

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