Scheduled Cloud Functions for AI Workload Management

Question

Pulumi · Accepted Answer

To achieve scheduled Cloud Functions for AI Workload Management using Pulumi, you can leverage Google Cloud's Cloud Functions and Cloud Scheduler. This setup allows you to run code in response to events, which in this case will be the schedule set up with Cloud Scheduler.

Here's a breakdown of the steps we'll be taking in the Pulumi Python program:

1. Create a Cloud Function: A piece of code that executes when triggered. We'll write a function that performs your desired AI workload management task.
2. Set up Cloud Scheduler: A managed service that allows you to run scheduled jobs, which will trigger our Cloud Function on the specified schedule.

Now, let's get started with the actual Pulumi program:

```python
import pulumi
import pulumi_gcp as gcp

# Replace 'your_function_name' with the desired Cloud Function name.
# This Cloud Function will contain the code for AI workload management.
ai_workload_function = gcp.cloudfunctions.Function("your_function_name",
    source_archive_bucket=gcp.storage.Bucket("source_bucket").name,
    source_archive_object=gcp.storage.BucketObject("source_archive.zip",
        bucket="source_bucket",
        source=pulumi.FileAsset("path_to_your_source_code.zip")
    ).name,
    runtime="python37",  # Choose the runtime version according to your code
    entry_point="handler",  # Entry point in your code
    timeout=60,
    available_memory_mb=128,
    trigger_http=True,  # We use an HTTP trigger and link it to Cloud Scheduler
)

# Once the Cloud Function is deployed, it creates an HTTPsTrigger
# which contains a URL that's used for invoking the function.
invoker_url = ai_workload_function.https_trigger_url

# IAM member granting invoker permissions to the service account running Cloud Scheduler
invoker_iam = gcp.cloudfunctions.FunctionIamMember("invoker",
    cloud_function=ai_workload_function.name,
    role="roles/cloudfunctions.invoker",
    member="serviceAccount:your-cloud-scheduler@your-project-id.iam.gserviceaccount.com",
)

# Create a Cloud Scheduler job to trigger the Cloud Function based on a schedule
scheduler_job = gcp.cloudscheduler.Job("ai_scheduler_job",
    description="Schedule for AI Workload Management Cloud Function",
    schedule="0 */4 * * *",  # This schedule runs every 4 hours, adjust it as needed
    time_zone="Etc/UTC",  # Set your preferred timezone
    http_target=gcp.cloudscheduler.JobHttpTargetArgs(
        http_method="GET",
        url=invoker_url,  # URL from the Cloud Function's HTTPS trigger
        oidc_token=gcp.cloudscheduler.JobHttpTargetOidcTokenArgs(
            service_account_email="your-cloud-scheduler@your-project-id.iam.gserviceaccount.com",
        ),
    ),
    depends_on=[invoker_iam],  # Ensure IAM permissions are set before creating the job
)

# Export the invoker URL to check the function or trigger it manually
pulumi.export('invoker_url', invoker_url)
```

Explanation:
- We've defined a Cloud Function `ai_workload_function` that points to a ZIP archive of your Python code. This code will manage your AI workload.
- We then set up IAM permissions with `invoker_iam`, so that the Cloud Scheduler has the necessary permissions to invoke the function.
- Lastly, we create a Cloud Scheduler job `scheduler_job` that runs according to a given `schedule` and uses `http_target` to trigger the Cloud Function.

This will create an infrastructure where your Cloud Function is invoked as per the schedule set up in Cloud Scheduler, which effectively allows you to manage your AI workloads on schedule.

Ensure that you replace placeholders like `path_to_your_source_code.zip`, `your-cloud-scheduler@your-project-id.iam.gserviceaccount.com`, and `your_function_name` with actual values related to your project. Also, be sure to define the correct IAM service account email that Cloud Scheduler uses to invoke the Cloud Function.

Let me know if you need further explanation on any specific part of the code or the resources we’ve used!