1. Knative Serving for Scalable ML Model Predictions


    Knative Serving is a Kubernetes-based solution that enables serverless workloads. It allows you to deploy and manage scalable serverless applications easily. To deploy a scalable machine learning model using Knative, you would typically need to prepare a container image of your model wrapped in a web service, push that image to a container registry, and then define the Knative Service that references that image.

    Pulumi doesn't directly interface with Knative, but it can be used to set up the necessary infrastructure on Kubernetes to support Knative Serving. You would typically need to have Knative Serving pre-installed on your Kubernetes cluster. With Pulumi, you can manage Kubernetes resources to deploy your applications with Knative Serving.

    Below is a Pulumi program written in Python that shows how you could define a Kubernetes service using Knative. This is just a template to illustrate the approach; you would need to replace placeholders with your actual Docker image, service name, and other specifics.

    The program illustrates the following steps:

    1. Import the necessary Pulumi and Kubernetes modules.
    2. Define the Knative Service manifest.
    3. Deploy the manifest to the Kubernetes cluster.

    The example assumes that Knative Serving is already installed in your Kubernetes cluster and that you have credentials configured for Pulumi to access the cluster.

    import pulumi from pulumi_kubernetes.core.v1 import Service from pulumi_kubernetes.apiextensions import CustomResource from pulumi_kubernetes.apiextensions.CustomResource import CustomResourceArgs from pulumi_kubernetes.meta.v1 import ObjectMetaArgs # Define the Knative Serving Service # Note: Please replace `my-model-service`, `docker.io/my-model`, and other placeholders with your actual data. knative_serving_service = CustomResource( "my-model-service", api_version="serving.knative.dev/v1", kind="Service", metadata=ObjectMetaArgs( name="my-model-service", ), spec={ "template": { "spec": { "containers": [ { "image": "docker.io/my-model:latest", # Replace with the path to your model's container image "env": [ { "name": "MODEL_NAME", "value": "my-model" }, # Define other environment variables if needed ], # You can include other container-level specifications here (ports, resources requests/limits, etc.) } ] } }, # Define the traffic splitting if you want to do A/B testing or gradual rollouts "traffic": [ { "latestRevision": True, "percent": 100 } ] }) # Export the service name and URL, replacing `your-domain` with your actual domain if Knative is configured with one. pulumi.export("service_name", knative_serving_service.metadata["name"]) pulumi.export("service_url", pulumi.Output.concat("http://my-model-service.your-domain.com"))

    Here’s a breakdown of the Knative Service manifest defined above:

    • We imported pulumi_kubernetes as it provides methods to interact with Kubernetes resources, including custom resources like Knative services.
    • We used a CustomResource to define the Knative Service, since Knative Service is a CRD (Custom Resource Definition) and not included in the core Kubernetes API.
    • The api_version and kind specify that we're creating a Knative Service. The actual api_version and kind might differ based on the version of Knative Serving you're using.
    • The metadata section provides a name for the service.
    • The spec section defines the specifics of the service. It sets up the container image to run (which contains your ML model) and also defines traffic management rules (in this example, 100% of traffic is directed to the latest revision).
    • We export the service name and a hypothetical URL for accessing the model. Depending on your domain and DNS setup, this URL will vary.

    Remember to replace placeholder values with your actual configuration. You also need to have your kubeconfig file set up for Pulumi to access your Kubernetes cluster or use the Pulumi Service Account if running in a CI/CD pipeline.

    This program is a basic example for illustrative purposes. Depending on your requirements and setup, you might need additional configurations like setting up networking rules, persistent volumes, secrets, or other environmental variables for your model service.