GSP900

Overview

TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.

This lab uses transfer learning to train your machine. In transfer learning, when you build a new model to classify your original dataset, you reuse the feature extraction part and re-train the classification part with your dataset. This method uses less computational resources and training time. Deep learning from scratch can take days, but transfer learning can be done in short order.

What you'll do

• Examine and understand your image data
• Build an input pipeline using Keras ImageDataGenerator
• Use a pre-trained model for feature extraction
• Fine-tune a pre-trained 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 are made available to you.

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

To complete this lab, you need:

• Access to a standard internet browser (Chrome browser recommended).

Note: Use an Incognito (recommended) or private browser window to run this lab. This prevents conflicts between your personal account and the student account, which may cause extra charges incurred to your personal account.

• Time to complete the lab—remember, once you start, you cannot pause a lab.

Note: Use only the student account for this lab. If you use a different Google Cloud account, you may incur charges to that account.

How to start your lab and sign in to the Google Cloud console

1. Click the Start Lab button. If you need to pay for the lab, a dialog opens for you to select your payment method. On the left is the Lab Details pane with the following:

   • The Open Google Cloud console button
   • Time remaining
   • The temporary credentials that you must use for this lab
   • Other information, if needed, to step through this lab

2. Click Open Google Cloud console (or right-click and select Open Link in Incognito Window if you are running the Chrome browser).

   The lab spins up resources, and then opens another tab that shows the Sign in page.

   Tip: Arrange the tabs in separate windows, side-by-side.

   Note: If you see the Choose an account dialog, click Use Another Account.

3. If necessary, copy the Username below and paste it into the Sign in dialog.

   {{{user_0.username | "Username"}}}

   You can also find the Username in the Lab Details pane.

4. Click Next.

5. Copy the Password below and paste it into the Welcome dialog.

   {{{user_0.password | "Password"}}}

   You can also find the Password in the Lab Details pane.

6. Click Next.

   Important: You must use the credentials the lab provides you. Do not use your Google Cloud account credentials. Note: Using your own Google Cloud account for this lab may incur extra charges.

7. Click through the subsequent pages:

   • Accept the terms and conditions.
   • Do not add recovery options or two-factor authentication (because this is a temporary account).
   • Do not sign up for free trials.

After a few moments, the Google Cloud console opens in this tab.

Note: To access Google Cloud products and services, click the Navigation menu or type the service or product name in the Search field.

Task 1. Open the notebook in Vertex AI Workbench

1. In the Google Cloud console, on the Navigation menu (), click Vertex AI > Workbench.

2. Find the instance and click on the Open JupyterLab button.

The JupyterLab interface for your Workbench instance opens in a new browser tab.

Note: If you do not see notebooks in JupyterLab, please follow these additional steps to reset the instance:

1. Close the browser tab for JupyterLab, and return to the Workbench home page.

2. Select the checkbox next to the instance name, and click Reset.

3. After the Open JupyterLab button is enabled again, wait one minute, and then click Open JupyterLab.

Task 2. Copy the notebook from a Cloud Storage bucket

1. Click Terminal to open a terminal shell inside the Vertex AI Notebook instance.

2. Enter the following command in the terminal shell to import the lab files into your Vertex AI Workbench instance:

gcloud storage cp {{{project_0.startup_script.notebook_files_path|notebook_files_path}}} .

3. Run this command to clear all outputs in the notebook's cells:

jupyter nbconvert --clear-output --inplace {{{project_0.startup_script.full_notebook_name | Notebook file}}}

Click Check my progress to verify the objective. Copy the notebook from a Cloud Storage bucket

Task 3. Open and execute the notebook

1. Open the notebook file in Vertex AI Workbench.

Note: In the Select Kernel dialog, choose TensorFlow 2-11 (Local) from the list of available kernels.

2. From here, read the instructions in the notebook to complete the lab.

3. Execute the cells one by one and observe the results. A convenient way to progress through the cells is by clicking in a cell, then click Shift + Enter, waiting for each cell to complete before progressing.

4. Read the instructions and the comments in the code blocks carefully. You will be asked to edit some of the code blocks before running them. For example, you will be setting environment variables in the notebook, so add your bucket name and project ID before running the cell.

Congratulations!

You've compiled a model which accurately classifies images of dogs and cats within a reasonable amount of time.

Next steps / learn more

• For more information about using TensorFlow, go to the TensorFlow website or the TensorFlow Github project. There are lots of other resources available for TensorFlow, including a discussion group and whitepaper.
• Go have some fun in the TensorFlow Playground!
• Sign up for a full Coursera Course on Machine Learning

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Manual last updated December 10, 2024

Lab last tested December 10, 2024

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