1. Kubernetes-Based Model Experimentation and Versioning


    In the context of Kubernetes, model experimentation and versioning usually refer to the process of iterating over different versions of a machine learning model, tracking their performance, and managing their lifecycle. To achieve this, you typically need a combination of tools for machine learning workflows (such as Kubeflow), along with Kubernetes resources such as Deployments, Services, and PersistentVolumes for storing and serving models.

    Below is an example of how you might use Pulumi to set up a Kubernetes-based environment for model experimentation and versioning. The example involves creating a Kubernetes Namespace for separation of concerns, a PersistentVolumeClaim for model storage, a Deployment for running the model-serving application, and a Service to expose the application.

    import pulumi import pulumi_kubernetes as k8s # Create a Kubernetes Namespace where our resources will reside. # This provides a way to separate group resources within the cluster. model_ns = k8s.core.v1.Namespace("model-ns", metadata={ "name": "model-experimentation" }) # Create a PersistentVolumeClaim where you can store different versions of your models. # Persistent storage ensures that your models are safe across pod recreations and updates. model_pvc = k8s.core.v1.PersistentVolumeClaim("model-pvc", metadata={ "namespace": model_ns.metadata["name"] }, spec={ "accessModes": ["ReadWriteOnce"], # Allows the volume to be mounted as read-write by a single node "resources": { "requests": { "storage": "10Gi" # Specify the size of the storage } } }) # Create a Kubernetes Deployment to run the model-serving application. # This will use a container image that includes your machine learning model and a server (e.g., TensorFlow Serving). model_deployment = k8s.apps.v1.Deployment("model-deployment", metadata={ "namespace": model_ns.metadata["name"] }, spec={ "selector": { "matchLabels": { "app": "model-server" } }, "template": { "metadata": { "labels": { "app": "model-server" } }, "spec": { "containers": [{ "name": "model-container", "image": "your-docker-image-with-model", # Replace with the image that serves your model "ports": [{ "containerPort": 8501 }], "volumeMounts": [{ "mountPath": "/models/", "name": "model-storage" }] }], "volumes": [{ "name": "model-storage", "persistentVolumeClaim": { "claimName": model_pvc.metadata["name"] } }] } } }) # Create a Kubernetes Service to expose the model-serving application to external traffic. # This allows you to query the model over HTTP/TCP etc., using a stable endpoint. model_service = k8s.core.v1.Service("model-service", metadata={ "namespace": model_ns.metadata["name"] }, spec={ "selector": { "app": "model-server" }, "ports": [{ "port": 8501, "targetPort": 8501 }], "type": "LoadBalancer" # Exposes the service outside of the Kubernetes cluster }) # Export the cluster IP to access the model service outside the Kubernetes cluster. pulumi.export("model_service_ip", model_service.status["load_balancer"]["ingress"][0]["ip"])

    In this program:

    • We start by creating a Namespace which is a logical separation within your Kubernetes cluster to isolate your model experimentation workloads.
    • Next, we have a PersistentVolumeClaim where models can be stored and versioned in a persistent manner.
    • The Deployment object describes the desired state for your application instances. Here, we assume you have a container image (replace your-docker-image-with-model with the actual image name) that serves your model, which is using TensorFlow Serving as an example.
    • Then, we create a Service with type LoadBalancer, which makes the model-serving application accessible from outside the Kubernetes cluster through an external IP address.

    Once deployed, you can use the external IP address of the model service to send model inference requests.

    Please remember to replace "your-docker-image-with-model" with the actual Docker image that you want to deploy. This image needs to have the necessary software to serve your machine learning model (e.g., TensorFlow Serving or a Flask app with a model loaded).

    This is a basic setup and would likely need to be adjusted based on the specifics of your use case, such as how you version your models, how you track experiments, and how you serve your models. Tools like Kubeflow Pipelines can be added for orchestrating machine learning workflows and experiments.