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Google Cloud Run Functions: Step-by-Step Guide Using the GUI

This guide helps students create, deploy, and test Google Cloud Run functions entirely using the Google Cloud Console GUI. Follow the instructions to create a "Hello World" function and deploy a flower classification model using a machine learning repository. End with a task to apply your learning.

Prerequisites

Before starting, ensure:

  1. Google Cloud Account: Create or sign in to your Google Cloud account.
  2. Billing Enabled: Activate billing to use Cloud Run services (you can use the free tier).
  3. Google Cloud Console: Open Google Cloud Console in your browser.

1. Creating a Cloud Run Function: "Hello World"

Steps:

  1. Access Cloud Run:

    • In the Google Cloud Console, search for Cloud Run in the top navigation bar.
    • Select Cloud Run and enable the service if prompted.

  2. Create Service:

    • Click Create Service.
    • In the Source section, choose Upload source code.
    • Download the sample "Hello World" Python app provided in the course materials or use a zip file provided by your instructor.
    • Upload the zip file containing the code.

  3. Set Runtime and Configuration:

    • Select the runtime environment (e.g., Python 3.9).
    • Set the Region (choose one close to you).
    • In Authentication, select Allow unauthenticated invocations to make the function accessible.

  4. Deploy:

    • Review your settings and click Deploy.
    • Wait for the deployment process to complete.

  5. Test the Function:

    • Once deployment is complete, click on the service name to view details.
    • Copy the provided URL and paste it into your browser.
    • You should see "Hello, World!" displayed.

2. Deploying the Flower Classification Model

Steps:

  1. Clone and Modify Repository:

    • Download or clone the repository gcp_serverless_ml on your local machine. Ensure you review the materials if provided by your instructor.

  2. Create a Cloud Storage Bucket:

    • Go to the Storage section in the Google Cloud Console.
    • Click Create Bucket.
    • Name your bucket (e.g., flower-classifier-model) and set the desired region.
    • Upload the model files (from the model folder in the repository) by clicking Upload Files.

  3. Set Up a New Cloud Run Service:

    • Navigate back to Cloud Run in the Google Cloud Console.
    • Click Create Service and select Upload source code.
    • Upload the zip file for the repository or a folder containing the modified files.

  4. Configure Environment Variables:

    • During the configuration process, add an environment variable to specify your bucket name.
      • Click Add Variable and set the key as BUCKET_NAME and the value as your bucket name.

  5. Allow Unauthenticated Access:

    • In the Authentication settings, select Allow unauthenticated invocations to enable public access to the service.

  6. Deploy the Service:

    • Click Deploy and wait for the service to be deployed.

  7. Test the Model:

    • Once deployed, copy the URL of the service.
    • Use tools like Postman or the API Gateway in the Cloud Console to send a test JSON payload: 
      {
  "sepal_length": 5.8,
  "sepal_width": 2.7,
  "petal_length": 5.1,
  "petal_width": 1.9
}
    • The response should classify the flower as Virginica.

Student Task

  1. Customize the Flower Classification Model:
    • Modify the uploaded model to classify different datasets (e.g., handwritten digits or stock trends).
    • Re-deploy the model as a new Cloud Run service using the steps above.
    • Test the model with sample data and share the results.