1. Automated Machine Learning Pipelines Triggered by GCP Cloud Functions


    To set up an automated machine learning pipeline triggered by GCP Cloud Functions, we'll need several components at a high level:

    1. Cloud Functions: A serverless, event-driven computing service that allows you to execute code in response to various events within your GCP environment.
    2. Cloud Scheduler: An event handler to trigger the Cloud Functions at specified times if you want to run jobs on a schedule.
    3. Data Processing Jobs: These could be anything from big data jobs using DataProc, to specific machine learning job resources, to simpler jobs orchestrated using Workflows. It depends on the specific machine learning tasks you need to perform.
    4. IAM Permissions: To securely manage access and permissions for the relevant resources.

    The following Pulumi program demonstrates how you could set up a Cloud Function that could be used as part of a machine learning pipeline. The function itself will be a skeleton - the implementation of your machine learning tasks would need to be filled in according to your specific requirements.

    Let's go through the program steps which are:

    1. Define a Cloud Function that will execute your machine learning code.
    2. Set up IAM permissions, allowing the Cloud Function to perform its tasks securely.
    3. (Optional) Create a Cloud Scheduler job to trigger the function periodically.
    import pulumi import pulumi_gcp as gcp # Replace 'my-bucket' with the name of your Cloud Storage bucket and 'my-function-source' with the directory # where your Cloud Function's source code is located. The source must be a zip file containing the function's code. project = gcp.config.project bucket_name = 'my-bucket' source_directory = 'my-function-source' # Create a storage bucket to store the Cloud Function's source code. bucket = gcp.storage.Bucket('bucket', name=bucket_name) # Upload the source code to the storage bucket. bucket_object = gcp.storage.BucketObject('bucket-object', bucket=bucket.name, source=pulumi.FileArchive(source_directory)) # Create a Cloud Function triggered by HTTP requests. cloud_function = gcp.cloudfunctions.Function('ml-pipeline-function', source_archive_bucket=bucket.name, runtime='python39', # Make sure to choose the runtime that suits your function's requirements. source_archive_object=bucket_object.name, entry_point='handler', # The name of the function within your code to execute (e.g., `handler` function in Python file). trigger_http=True, # Triggers the function via HTTP. You can set up other triggers based on events. available_memory_mb=256) # Export the URL of the Cloud Function. pulumi.export('cloud_function_url', cloud_function.https_trigger_url) # (Optional) Set up IAM permissions for the Cloud Function. iam_member = gcp.cloudfunctions.FunctionIamMember('function-iam-member', project=project, region=cloud_function.region, cloud_function=cloud_function.name, role='roles/cloudfunctions.invoker', member='serviceAccount:your-service-account@example.iam.gserviceaccount.com') # (Optional) Create a Cloud Scheduler job to trigger the Cloud Function at a regular interval. # Replace '*/5 * * * *' with your desired schedule, using Unix-cron format. scheduler_job = gcp.cloudscheduler.Job('scheduler-job', project=project, # Note: App Engine app must be created in the project before using Cloud Scheduler. # Set up the schedule to trigger the function every 5 minutes as an example. schedule='*/5 * * * *', # Choose the appropriate time zone, e.g., 'America/New_York'. time_zone='Etc/UTC', http_target=gcp.cloudscheduler.JobHttpTargetArgs( uri=cloud_function.https_trigger_url, http_method='GET')) # You may want to use POST or other methods depending on your requirement. # When all the resources have been created, pulumi.export resolves the values so they can be easily viewed. pulumi.export('bucket_name', bucket.name) pulumi.export('bucket_object_name', bucket_object.name) pulumi.export('cloud_function_name', cloud_function.name) pulumi.export('scheduler_job_name', scheduler_job.name)

    This is a foundational setup. In reality, your Cloud Function (handler in the above code) will need to interact with many GCP services, like BigQuery, AI Platform, etc., depending on the specifics of your machine learning pipeline.

    To fill in the actual machine learning code, you will need to:

    • Write your machine learning code in Python or the language of your choice supported by GCP Cloud Functions.
    • Implement the code so it can be triggered by HTTP requests or other GCP events.
    • Package your code as directed by GCP Cloud Functions, typically in a zip file with all necessary dependencies.
    • Adjust the runtime parameter in the gcp.cloudfunctions.Function call accordingly.

    Remember, if you want to use other trigger types, such as Pub/Sub, you will need to configure the corresponding event trigger instead of trigger_http.

    For the IAM permissions setup, you must replace your-service-account@example.iam.gserviceaccount.com with the service account that the Cloud Function will use. This account should have the necessary permissions to execute your ML code and access any other GCP resources it may need.

    If you implement any job scheduling, make sure you have set up the App Engine app in your GCP project, as Cloud Scheduler requires it. The schedule field can be adjusted to meet the frequency with which you wish to trigger your machine learning pipeline.

    For Cloud Functions that are part of a larger machine learning pipeline, the above code will need to be adapted to include the services responsible for the actual machine learning work, such as AI Platform Jobs or DataProc, and possibly more complex IAM configurations.