Knative Event-driven Machine Learning Pipelines on Kubernetes

Question

Pulumi · Accepted Answer

To create Event-driven Machine Learning Pipelines on Kubernetes using Knative, you would need to install Knative Serving and Eventing components on your Kubernetes cluster, then set up your machine learning models, and finally create the event-driven pipelines that would use those models. Pulumi does not provide a high-level abstraction for Knative, but you can use Pulumi's Kubernetes provider to deploy Knative and set up the pipelines.

Here's a step-by-step process you would follow:

1. **Install Knative Serving and Eventing**: You need to apply the Knative YAML manifests to your cluster. This is typically done using `kubectl apply`, but with Pulumi you can import YAML manifests directly.

2. **Define your Machine Learning Models**: These would be your own Docker images that contain your ML model. You would publish them to a container registry that your Kubernetes cluster can access.

3. **Create Services for Your Models**: With Knative Serving, you define services that will respond to events by running your machine learning model.

4. **Set Up Event Sources**: Event sources in Knative Eventing will capture events from various sources and route them to your services.

5. **Set Up Triggers**: Triggers in Knative Eventing will filter and deliver events to various services based on certain conditions.

6. **Deploy Machine Learning Pipeline**: Once everything is set up, you create a sequence of services that process events one after another, forming a pipeline.

Below is an example of how you might use Pulumi to deploy the Knative Serving component. The workflow for the Eventing component and the other steps will be similar.

```python
import pulumi
import pulumi_kubernetes as k8s

# Load the Knative Serving manifest file.
# You would need to download this manifest from the Knative website or use one that suits your needs.
knative_serving_manifest_url = 'https://github.com/knative/serving/releases/download/v0.20.0/serving-crds.yaml'

# Use Pulumi to apply the manifest to the cluster.
knative_serving_manifest = k8s.yaml.ConfigFile('knative-serving-manifest',
    file=knative_serving_manifest_url)

# Export the status of the deployment.
pulumi.export('knative-serving-status', knative_serving_manifest.status)

# You would repeat the process with other Knative manifests, such as those for Eventing,
# and for any custom resources you need to create for your ML pipelines.
```

In this example, we are using Pulumi's `ConfigFile` function from the `pulumi_kubernetes.yaml` namespace to apply the Knative Serving manifest directly to our Kubernetes cluster. The URL points to a specific version of the manifest file, but you would replace this with the appropriate URL for your use case.

For the machine learning models, data sources, and pipeline logic, you would create Docker images containing your code, push these images to a registry, and create Kubernetes deployments and services to run these images. The services could then be triggered by Knative Eventing.

As you expand on this basic infrastructure, you’ll add more resources such as your ML model Docker images, Knative services for the models, and event sources and triggers to create the full event-driven workflow.

Please note that while Pulumi can set up the Kubernetes resources, the specifics of your ML models, their training, and inference code must be set up in the Docker images you create, and they are then referred to by the Kubernetes resources managed by Pulumi.