1. Scheduled AI Pipeline Workflows with Cloud Scheduler


    Scheduled workflows in a cloud environment, such as AI pipeline executions, are often orchestrated using a job scheduler that can handle triggering events based on a specific schedule. Google Cloud Scheduler is a fully managed cron job service that allows you to execute, retry, and manage scheduled operations across Google Cloud products.

    In our scenario, we want to schedule an AI pipeline, which can mean we need to perform a series of steps like data processing, training machine learning models, and making predictions. It’s important that these steps are executed reliably and at scheduled intervals.

    Using Pulumi with the Google Cloud provider, we can define these scheduled jobs programmatically. Below, I will guide you through how to set up a Cloud Scheduler job using Pulumi in Python. First, you need to make sure that you have Pulumi CLI installed and have configured the Google Cloud SDK with the proper credentials.

    In the program below, I'm going to illustrate how to create a Cloud Scheduler job that sends a message to a specific HTTP endpoint. This HTTP endpoint could be the kickoff point for your AI pipeline, where you have set up an HTTP-triggered Cloud Function or Cloud Run service to initiate the pipeline.

    Here's how you can do it:

    1. Install the Pulumi GCP plugin using pulumi plugin install resource gcp v6.67.0 if not already installed.
    2. Define your GCP project and region where the Cloud Scheduler job should be deployed.
    3. Define the job with a schedule (e.g., "* * * * *" for every minute) and an HTTP target.
    4. Configure properties such as the HTTP method, endpoint URL, and any headers or body content that your AI pipeline's entry point requires.
    5. Deploy the job and manage its state using Pulumi.

    Here is the Python program for setting up a Cloud Scheduler job:

    import pulumi import pulumi_gcp as gcp # Replace these variables according to your specific configuration project = 'your-gcp-project-id' location = 'your-gcp-region' scheduler_job_name = 'ai-pipeline-workflow' http_target_url = 'https://example.com/your-ai-pipeline-trigger-endpoint' http_method = gcp.cloudscheduler.HttpMethod.POST schedule = '* * * * *' # This schedule runs the job every minute. Customize it to your needs. time_zone = 'UTC' # Set the timezone that the schedule should use # Define the Cloud Scheduler job scheduler_job = gcp.cloudscheduler.Job( scheduler_job_name, project=project, location=location, description="Cloud Scheduler job for AI Pipeline Workflows", schedule=schedule, time_zone=time_zone, http_target=gcp.cloudscheduler.JobHttpTargetArgs( uri=http_target_url, http_method=http_method, # Add headers and body if required by your AI pipeline's HTTP endpoint ) ) # Export the job name and URL to be easily retrieved from the Pulumi stack pulumi.export('job_name', scheduler_job.name) pulumi.export('http_target', scheduler_job.http_target.apply(lambda ht: ht.uri))

    In this program:

    • You create a Cloud Scheduler job named ai-pipeline-workflow using the gcp.cloudscheduler.Job resource.
    • The schedule parameter is where you define the cron schedule. In our case, it is set to run every minute, but in a real-world scenario, you would set this to the frequency at which your AI pipeline needs to run.
    • The time_zone parameter specifies the timezone for the schedule.
    • The http_target is an instance of gcp.cloudscheduler.JobHttpTargetArgs, which specifies the details of the HTTP request that the Cloud Scheduler will make. This should point to the endpoint that triggers your AI pipeline.

    Note that the above example assumes you have an HTTP endpoint set up to trigger your AI pipeline. If your pipeline is set up differently (for example, using Pub/Sub or another trigger), you would configure the relevant target in the scheduler_job resource.

    Once the code is ready, deploying it is as simple as running pulumi up in your command line, which will prompt you for confirmation of the resources to be created, updated, or deleted.

    As you use Pulumi to define your infrastructure as code, it can be version-controlled, reviewed, and managed just like any other software component, bringing the benefits of CI/CD practices to your cloud infrastructure.