GSP207

In this lab you will set up your Python development environment, get the Cloud Dataflow SDK for Python, and run an example pipeline using the Cloud Console.

Setup and Requirements

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.

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 pop-up opens for you to select your payment method. On the left is a panel populated with the temporary credentials that you must use for this lab.

   

2. Copy the username, and then click Open Google Console. The lab spins up resources, and then opens another tab that shows the Sign in page.

   

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

3. In the Sign in page, paste the username that you copied from the Connection Details panel. Then copy and paste the password.

   Important: You must use the credentials from the Connection Details panel. Do not use your Qwiklabs credentials. If you have your own Google Cloud account, do not use it for this lab (avoids incurring charges).

4. 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 Cloud Console opens in this tab.

Activate Cloud Shell

Cloud Shell is a virtual machine that is loaded with development tools. It offers a persistent 5GB home directory and runs on the Google Cloud. Cloud Shell provides command-line access to your Google Cloud resources.

In the Cloud Console, in the top right toolbar, click the Activate Cloud Shell button.

Click Continue.

It takes a few moments to provision and connect to the environment. When you are connected, you are already authenticated, and the project is set to your PROJECT_ID. For example:

gcloud is the command-line tool for Google Cloud. It comes pre-installed on Cloud Shell and supports tab-completion.

You can list the active account name with this command:

gcloud auth list
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(Output)

Credentialed accounts:
 - <myaccount>@<mydomain>.com (active)content_copy

(Example output)

Credentialed accounts:
 - google1623327_student@qwiklabs.netcontent_copy

You can list the project ID with this command:

gcloud config list project
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(Output)

[core]
project = <project_ID>content_copy

(Example output)

[core]
project = qwiklabs-gcp-44776a13dea667a6content_copy

Check project permissions

Before you begin your work on Google Cloud, you need to ensure that your project has the correct permissions within Identity and Access Management (IAM).

1. In the Google Cloud console, on the Navigation menu (), click IAM & Admin > IAM.

2. Confirm that the default compute Service Account {project-number}[email protected] is present and has the editor role assigned. The account prefix is the project number, which you can find on Navigation menu > Home.

If the account is not present in IAM or does not have the editor role, follow the steps below to assign the required role.

• In the Google Cloud console, on the Navigation menu, click Home.

• Copy the project number (e.g. 729328892908).

• On the Navigation menu, click IAM & Admin > IAM.

• At the top of the IAM page, click Add.

• For New members, type:

{project-number}[email protected]
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Replace {project-number} with your project number.

• For Role, select Project (or Basic) > Editor. Click Save.

Create a Cloud Storage bucket

1. In the Cloud Console, click on Navigation menu and then click on Storage.

2. Click Create bucket.

3. In the Create bucket dialog, specify the following attributes:

• Name: A unique bucket name. Do not include sensitive information in the bucket name, as the bucket namespace is global and publicly visible.

• Location type: Region

• Location: us-central1

• A location where bucket data will be stored.

4. Click Create.

Test Completed Task

Click Check my progress to verify your performed task. If you have completed the task successfully you will granted with an assessment score.

Create a Cloud Storage bucket.

Install pip and the Cloud Dataflow SDK

1. The Cloud Dataflow SDK for Python requires Python version 3.7. Check that you are using Python version 3.7 by running:

python3 --version
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2. Install pip, Python's package manager. Check if you already have pip installed by running:

pip3 --version
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3. After installation, check that you have pip version 7.0.0 or newer. To update pip, run the following command:

sudo pip3 install -U pip
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This step is optional but highly recommended. Install and create a Python virtual environment for initial experiments:

If you do not have virtualenv version 13.1.0 or newer, install it by running:

sudo pip3 install --upgrade virtualenv
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A virtual environment is a directory tree containing its own Python distribution. To create a virtual environment, run the following:

virtualenv -p python3.7 env
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A virtual environment needs to be activated for each shell that will use it. Activating it sets some environment variables that point to the virtual environment's directories. To activate a virtual environment in Bash, run:

source env/bin/activate
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This command sources the script bin/activate under the virtual environment directory you created. For instructions using other shells, see the virtualenv documentation.

4. Install the latest version of the Apache Beam for Python by running the following command from a virtual environment:

pip install apache-beam[gcp]
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You will see some warnings returned that are related to dependencies. It is safe to ignore them for this lab.

5. Run the wordcount.py example locally by running the following command:

python -m apache_beam.examples.wordcount --output OUTPUT_FILE
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You may see a message similar to the following:

INFO:root:Missing pipeline option (runner). Executing pipeline using the default runner: DirectRunner.
INFO:oauth2client.client:Attempting refresh to obtain initial access_tokencontent_copy

This message can be ignored.

You can now list the files that are on your local cloud environment to get the name of the OUTPUT_FILE:

ls
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Copy the name of the OUTPUT_FILE and cat into it:

cat <file name>
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Your results show each word in the file and how many times it appears.

Run an Example Pipeline Remotely

Set the BUCKET environment variable to the bucket you created earlier.

BUCKET=gs://<bucket name provided earlier>
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Now you'll run the wordcount.py example remotely:

python -m apache_beam.examples.wordcount --project $DEVSHELL_PROJECT_ID \
  --runner DataflowRunner \
  --staging_location $BUCKET/staging \
  --temp_location $BUCKET/temp \
  --output $BUCKET/results/output \
  --region us-central1
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In your output, wait until you see the message:

JOB_MESSAGE_DETAILED: Workers have started successfully.content_copy

Then continue with the lab.

Check that your job succeeded

Open the navigation menu and select Dataflow from the list of services:

You should see your wordcount job with a status of Running at first.

Click on the name to watch the process. When all the boxes are checked off, you can continue watching the logs in Cloud Shell.

The process is complete when the status is Succeeded:

Test Completed Task

Click Check my progress to verify your performed task. If you have completed the task successfully you will granted with an assessment score.

Run an Example Pipeline Remotely.

Click on Storage in the Console.

Click on the name of your bucket. In your bucket, you should see the results and staging directories:

Click on the results folder and you should see the output files that your job created:

Click on a file to see the word counts it contains.

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!

Finish Your Quest

Continue your Quest with Baseline: Data, ML, AI. A Quest is a series of related labs that form a learning path. Completing this Quest earns you the badge above, 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 this Quest and get immediate completion credit if you've taken this lab. See other available Qwiklabs Quests.

Next Steps / Learn More

This lab is 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!

To get your own copy of the book this lab is based on: Data Science on the Google Cloud Platform: O'Reilly Media, Inc.

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Manual Last Updated October 12, 2020

Lab Last Tested October 12, 2020

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