1. Event-driven Machine Learning Pipelines on Knative


    Event-driven machine learning pipelines are powerful for creating systems that respond automatically to various events with intelligent processing. Creating one on Knative involves setting up a series of services and event sources that trigger workflows, such as training a machine learning model when new data becomes available.

    Here's how to set one up using Pulumi with Python:

    1. Set up Knative on your Kubernetes cluster. Knative serves as the foundation for building your event-driven services. It consists of Serving and Eventing components that facilitate deploying serverless applications and managing event subscriptions and delivery, respectively.
    2. Define Event Sources. Event sources in Knative correspond to the triggers of your pipeline. They can be anything from a webhook call to a file being placed in cloud storage.
    3. Create Services. In Knative, a service is a stateless, scalable endpoint. Each step of your ML pipeline (data preprocessing, model training, model evaluation, etc.) can be encapsulated as a Knative service.
    4. Chain the Services through Eventing. Knative's eventing constructs like Triggers, Channels, and Subscriptions help to route events from sources to appropriate services.

    Below is a Pulumi program to set up a simple pipeline where a Kubernetes job (representing an ML training task) is triggered every time a new message is posted to an Event Source:

    import pulumi import pulumi_kubernetes as k8s # Replace these values with your actual configuration namespace_name = 'ml-pipeline' event_source_name = 'new-data-source' ml_training_job_name = 'ml-training-job' # Set up the Kubernetes namespace namespace = k8s.core.v1.Namespace( namespace_name, metadata={ "name": namespace_name } ) # Define an example Event Source # This could be customized to a real event source implementation # and parameters appropriate to your cloud environment. event_source = k8s.core.v1.ConfigMap( event_source_name, metadata={ "namespace": namespace.metadata["name"], "name": event_source_name }, data={"message": "New data available"} ) # Define a Kubernetes job which represents the ML training process. # The job is triggered by the event source indicating the availability # of new data. ml_training_job = k8s.batch.v1.Job( ml_training_job_name, metadata={ "namespace": namespace.metadata["name"], "name": ml_training_job_name }, spec={ "template": { "spec": { "containers": [{ "name": "ml-container", "image": "python:3.8", # Replace with your ML training image "command": ["python", "-c", """ import time print("Training model...") time.sleep(60) print("Model trained successfully!") """] }], "restartPolicy": "Never", } } } ) # Export the namespace name and the job name pulumi.export("namespace", namespace.metadata["name"]) pulumi.export("ml_training_job", ml_training_job.metadata["name"])

    In this Pulumi program, we're creating a Namespace to contain all our resources, an EventSource which is simulated with a ConfigMap here, but would be replaced with your event source implementation, and a Job which represents our machine learning training task.

    To actually connect the Event Source and the Kubernetes Job, you would define a Knative Trigger or other resources as part of Knative Eventing. The Pulumi Kubernetes package does not directly include these custom resources as of my knowledge cutoff in September 2021, but you can use CustomResource within Pulumi to define Knative-specific resources, assuming the Knative CRDs are installed on your cluster.

    Please note that you will also need to replace "python:3.8" with your machine learning training image and pass the correct command that triggers your ML process.

    Remember, this setup is skeletal and intended to illustrate the concept. In a real-world scenario, you would have much more sophisticated error handling, dynamic resource allocation, and actual event sourcing beyond simple ConfigMap updates.