Getting Started with Kserve on GitHub

Getting Started with Kserve on GitHub

In the dynamic landscape of machine learning and artificial intelligence, deploying and serving models efficiently is crucial. Kubernetes, a powerful container orchestration platform, has become a go-to solution for managing containerized applications. Kserve, an open-source project built on top of Kubernetes, simplifies the deployment and serving of machine learning models. This article will guide you through the process of getting started with Kserve on GitHub, providing step-by-step instructions, essential commands, and additional examples.

Step 1: Set Up Your Environment
Before diving into Kserve, ensure you have a Kubernetes cluster up and running. You can use Minikube for local development or any other Kubernetes cluster provider of your choice. Make sure kubectl is installed and configured to connect to your cluster.

Step 2: Clone Kserve Repository
Begin by cloning the Kserve repository from GitHub. Open your terminal and run the following command:

git clone

Navigate to the Kserve directory:

cd kserve

Step 3: Install Kserve
Now, it's time to install Kserve on your Kubernetes cluster. Use the provided installation script:


This script installs Kserve along with the necessary dependencies on your cluster.

Step 4: Verify Installation
After the installation, verify that Kserve components are running correctly. Execute the following command:

kubectl get pods -n kserve

Ensure all the pods are in a 'Running' state.

Step 5: Deploy a Sample Model
Kserve allows you to deploy machine learning models effortlessly. Let's deploy a sample model using a pre-built container. Create a YAML file (e.g., sample-model.yaml) with the following content:

apiVersion: ""
kind: "InferenceService"
name: "sample-model"
pythonPath: ""
serviceAccountName: "kserve-predictor"
- image: "kserve/sklearnserver:latest"

Apply the configuration to deploy the model:

kubectl apply -f sample-model.yaml

Step 6: Access the Model
Once the model is deployed, you can access it through the Kserve ingress gateway. Find the ingress address:

kubectl get svc -n istio-system istio-ingressgateway

Access the model using the provided address.

More Examples:
Explore advanced configurations, such as autoscaling, custom metrics, and model explainability, by referring to the official Kserve documentation on GitHub:

Incorporate additional models into Kserve and experiment with different containers to serve diverse machine learning frameworks.

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