
准备工作
- 实验会创建一个 Google Cloud 项目和一些资源,供您使用限定的一段时间
- 实验有时间限制,并且没有暂停功能。如果您中途结束实验,则必须重新开始。
- 在屏幕左上角,点击开始实验即可开始
Launch Vertex AI Workbench instance
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Execute a BigQuery query
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The purpose of this lab is to show learners how to instantiate a Jupyter notebook running on Google Cloud Platform's Vertex AI service. To aid in the demonstration, a dataset with various flight departure and arrival times will be leveraged.
In this lab, you learn to perform the following tasks:
For each lab, you get a new Google Cloud project and set of resources for a fixed time at no cost.
Sign in to Qwiklabs using an incognito window.
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.
When ready, click Start lab.
Note your lab credentials (Username and Password). You will use them to sign in to the Google Cloud Console.
Click Open Google Console.
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.
Accept the terms and skip the recovery resource page.
The Welcome to BigQuery in the Cloud Console message box opens. This message box provides a link to the quickstart guide and lists UI updates.
In the Google Cloud console, from the Navigation menu (), select Vertex AI.
Click Enable All Recommended APIs.
In the Navigation menu, click Workbench.
At the top of the Workbench page, ensure you are in the Instances view.
Click Create New.
Configure the Instance:
This will take a few minutes to create the instance. A green checkmark will appear next to its name when it's ready.
Untitled.ipynb
file in the menu bar and select Rename Notebook to give it a meaningful name.Your environment is set up. You are now ready to start working with your Vertex AI Workbench notebook.
Click Check my progress to verify the objective.
The command makes use of the magic function %%bigquery
. Magic functions in notebooks provide an alias for a system command. In this case, %%bigquery
runs the query in the cell in BigQuery and stores the output in a Pandas DataFrame object named df
.
Click Check my progress to verify the objective.
We're going to use the Pandas DataFrame containing our query output to build a plot that depicts how arrival delays correspond to departure delays. Before continuing, if you are unfamiliar with Pandas the Ten Minute Getting Started Guide is recommended reading.
arrival_delay_deciles
into a Pandas Series object. The code also renames the resulting columns.percentiles
table to the departure_delay
field in our original DataFrame. Execute the following code in a new cell:0%
and 100%
fields. Execute the following code in a new cell:When you have completed your lab, click End Lab. Google Cloud Skills Boost 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:
You can close the dialog box if you don't want to provide feedback.
For feedback, suggestions, or corrections, please use the Support tab.
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