Overview

In this notebook, you use encoder-decoder architecture to create a text translation function.

Learning Objectives

• Create a tf.data.Dataset for a seq2seq problem.
• Train an encoder-decoder model in Keras for a translation task.
• Save the encoder and the decoder as separate model.
• Merge the trained encoder and decoder into a translation function.

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.

Setup your environement

Enable the AI Platform Training & Prediction API

1. On the Navigation menu, navigate to APIs & services > Library and search for AI Platform Training & Prediction API in the search box.
2. Click on AI Platform Training & Prediction API, then click Enable.

Task 1. Create Cloud Storage Bucket

1. On the Navigation menu, navigate to Cloud Storage > Buckets and Click on Create bucket.
2. Set a unique name (use your project ID because it is unique) and click Continue.
3. For Location type select Region and from the dropdown select .
4. Click Create.
5. Select Confirm for the prompt Public access will be prevented.

Task 2. Launch Vertex AI Workbench instance

1. In the Google Cloud console, from the Navigation menu (), select Vertex AI.

2. Click Enable All Recommended APIs.

3. In the Navigation menu, click Workbench.

   At the top of the Workbench page, ensure you are in the Instances view.

4. Click Create New.

5. Configure the Instance:

   • Name: lab-workbench
   • Region: Set the region to
   • Zone: Set the zone to
   • Advanced Options (Optional): If needed, click "Advanced Options" for further customization (e.g., machine type, disk size).

6. Click Create.

This will take a few minutes to create the instance. A green checkmark will appear next to its name when it's ready.

7. Click OPEN JUPYTERLAB next to the instance name to launch the JupyterLab interface. This will open a new tab in your browser.

Click Check my progress to verify the objective. Launch Vertex AI Workbench instance

Task 3. Clone a course repo within your JupyterLab interface

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

Step 1

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

Step 2

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

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

Step 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.

Click Check my progress to verify the objective. Clone a course repo within your JupyterLab interface

Task 4. Reusable Embeddings

1. In the notebook interface, navigate to training-data-analyst > courses > machine_learning > deepdive2 > text_classification > labs > rnn_encoder_decoder.ipynb.

2. For the Select Kernel dialog pop-up, select TensorFlow 2-11 (Local) from the list of available kernels.

3. In the notebook interface, click on Edit > Clear All Outputs (click on Edit, then in the drop-down menu, select Clear All Outputs).

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

Tip: To run the current cell you can click the cell and hit SHIFT+ENTER. Other cell commands are found 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 (they are in white text).
• If you need more help, look at the complete solution by navigating to training-data-analyst > courses > machine_learning > deepdive2 > text_classification > solutions and opening rnn_encoder_decoder.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.

The number of stars indicates the following:

• 1 star = Very dissatisfied
• 2 stars = Dissatisfied
• 3 stars = Neutral
• 4 stars = Satisfied
• 5 stars = Very satisfied

You can close the dialog box if you don't want to provide feedback.

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