How do I deploy Kubernetes-based deep learning inference services?

In this guide, we will deploy deep learning inference services on a Kubernetes cluster using Pulumi. We will create a Kubernetes deployment for a TensorFlow Serving model and expose it using a Kubernetes service. This setup will allow us to serve machine learning models and perform inference in a scalable and reliable manner.

Key Points:

Kubernetes Cluster: We will use an existing Kubernetes cluster.
TensorFlow Serving: A popular tool for serving machine learning models.
Deployment: We will create a Kubernetes deployment for TensorFlow Serving.
Service: We will expose the deployment using a Kubernetes service.

import * as pulumi from "@pulumi/pulumi";
import * as k8s from "@pulumi/kubernetes";

// Define the namespace
const namespace = new k8s.core.v1.Namespace("inference-namespace", {
    metadata: { name: "inference" },
});

// Define the TensorFlow Serving deployment
const tfServingDeployment = new k8s.apps.v1.Deployment("tf-serving-deployment", {
    metadata: {
        namespace: namespace.metadata.name,
        name: "tf-serving",
    },
    spec: {
        replicas: 2,
        selector: {
            matchLabels: { app: "tf-serving" },
        },
        template: {
            metadata: {
                labels: { app: "tf-serving" },
            },
            spec: {
                containers: [
                    {
                        name: "tensorflow-serving",
                        image: "tensorflow/serving:latest",
                        args: ["--port=8500", "--rest_api_port=8501"],
                        ports: [
                            { containerPort: 8500 },
                            { containerPort: 8501 },
                        ],
                        volumeMounts: [
                            {
                                name: "model-volume",
                                mountPath: "/models/my_model",
                                subPath: "my_model",
                            },
                        ],
                    },
                ],
                volumes: [
                    {
                        name: "model-volume",
                        hostPath: {
                            path: "/path/to/your/model",
                        },
                    },
                ],
            },
        },
    },
});

// Define the service to expose TensorFlow Serving
const tfServingService = new k8s.core.v1.Service("tf-serving-service", {
    metadata: {
        namespace: namespace.metadata.name,
        name: "tf-serving",
    },
    spec: {
        type: "LoadBalancer",
        selector: { app: "tf-serving" },
        ports: [
            { port: 8500, targetPort: 8500, protocol: "TCP" },
            { port: 8501, targetPort: 8501, protocol: "TCP" },
        ],
    },
});

// Export the service URL
export const tfServingServiceUrl = tfServingService.status.loadBalancer.ingress[0].hostname;

Summary:

In this guide, we deployed a TensorFlow Serving model on a Kubernetes cluster using Pulumi. We created a Kubernetes deployment for TensorFlow Serving and exposed it using a Kubernetes service. This setup allows us to serve machine learning models and perform inference in a scalable and reliable manner.

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