1. Auto-Scaling Web Applications for AI Dashboards on App Engine


    To create an auto-scaling web application for AI dashboards on Google App Engine, you will need to define an App resource which stands for an App Engine application, and a Version resource which represents the different versions of the application that can be deployed.

    The Version resource will contain the configuration for automaticScaling, where you can specify how the application should scale based on factors like CPU usage, request latency, concurrent requests, and more.

    Here's a Pulumi Python program that defines an App Engine app with auto-scaling capabilities:

    import pulumi import pulumi_google_native.appengine as appengine # Define the App Engine application app = appengine.Application("my-app", location_id="us-central", # Use the appropriate location for your app # Additional properties like iap (Identity-Aware Proxy) can be configured here ) # Define a version of your app that will be auto-scaled version = appengine.Version("v1", # Link to the application defined above apps_id=app.name, # The service within the App Engine application to associate with this version service_id="default", # Define the runtime and environment for the deployment runtime="python27", # Use an appropriate runtime for your application env="standard", # The actual deployment configuration deployment=appengine.VersionDeploymentArgs( # Specify your application files or container image here files={ "main.py": appengine.VersionDeploymentFilesArgs( sha1_sum="hash_of_main_py_file", source_url="gs://bucket_name/main.py" ), # Additional files can be included similarly }, ), # Configure automatic scaling for the app automatic_scaling=appengine.VersionAutomaticScalingArgs( cool_down_period="120s", # Cool down period between changes to the number of instances cpu_utilization=appengine.VersionAutomaticScalingCpuUtilizationArgs( target_utilization=0.6 # Target CPU utilization to trigger scaling ), # Further scaling parameters like max and min instances can be set below max_total_instances=10, # Maximum number of instances for auto-scaling min_total_instances=2, # Minimum number of instances for auto-scaling # Target latencies and concurrent requests configurations can also be included ), # Additional configurations like env_variables, entrypoint, handlers etc. can be added ) # Export the URL of the deployed application pulumi.export("app_url", pulumi.Output.concat("https://", app.location_id, ".appspot.com"))

    In this code:

    • We create an appengine.Application resource named "my-app", specifying the location where the app will reside in App Engine.
    • We define a appengine.Version resource "v1" which is connected to the "my-app" application through the apps_id parameter.
    • We specify the runtime and environment details that our application will run on. In the example above, we use Python 2.7 in the standard environment. Update the runtime parameter to match your application's requirements.
    • The deployment details are provided with a simple dictionary specifying the source URLs of our application files stored in Google Cloud Storage.
    • The automatic_scaling configuration details of the application are set within appengine.VersionAutomaticScalingArgs. Here, we've configured the app to scale automatically based on CPU utilization, with specific minimum (2) and maximum (10) instance counts.
    • For simplicity, the pulumi.export statement at the end provides the URL where the app will be accessible once deployed.

    Remember to replace placeholder values like sha1_sum, source_url, bucket_name, and hash_of_main_py_file with actual values that correspond to your application's deployment details. Additionally, the example uses the deprecated Python 2.7 runtime for demonstration purposes; be sure to use a current, supported runtime environment for your implementation.