Quickstart Guide
Welcome to the KServe Quickstart Guide! This guide will help you set up a KServe Quickstart environment for testing and experimentation. KServe Quickstart is designed to provide a simple and quick way to get started with KServe, allowing you to deploy and test machine learning models on Kubernetes with minimal setup. This guide will walk you through the prerequisites, installation steps, and how to verify your KServe Quickstart environment is up and running. By the end of this guide, you will have a fully functional KServe Quickstart environment ready for experimentation.
Prerequisites
Before you can get started with a KServe Quickstart deployment, you will need to ensure you have the following prerequisites installed:
Tools
Make sure you have the following tools installed:
- kubectl - The Kubernetes command-line tool
- helm - for installing KServe and other Kubernetes operators
- curl - for the quickstart script and for testing API endpoints (installed by default on most systems)
Run the following commands to verify that you have the required tools installed:
To verify kubectl
installation, run:
kubectl version --client
To verify helm
installation, run:
helm version
To verify curl
installation, run:
curl --version
Kubernetes Cluster
KServe requires a Kubernetes version 1.29 or higher. Ensure your cluster meets this requirement before proceeding with the installation.
You will need a running Kubernetes cluster with properly configured kubeconfig to run KServe. You can use any Kubernetes cluster, but for local development and testing, we recommend using kind
(Kubernetes in Docker) or minikube
.
- Local Kind Cluster
- Local Minikube Cluster
- Existing Kubernetes Cluster
If you want to run a local Kubernetes cluster, you can use Kind (Kubernetes in Docker). It allows you to create a Kubernetes cluster using Docker container nodes. This is ideal for local development and testing.
First, ensure you have Docker installed on your machine. Install Kind by following the Kind Quick Start Guide if you haven't done so already.
Then, you can create a local Kubernetes cluster with the following command:
kind create cluster
If you prefer to use Minikube, you can follow the Minikube Quickstart Guide to set up a local Kubernetes cluster. Minikube is another popular tool for running Kubernetes clusters locally. First, ensure you have Minikube installed on your machine. Then, you can start a local Kubernetes cluster with the following command:
minikube start
If you have access to a existing Kubernetes cluster, you can use that as well. Ensure that your kubeconfig is properly configured to connect to the cluster. You can verify your current context with:
kubectl config current-context
Verify your cluster meets the version requirements by running:
kubectl version --output=json
The server version in the output should show version 1.29 or higher:
{
"serverVersion": {
"major": "1",
"minor": "29",
...
}
}
Install KServe Quickstart Environment
Once you have the prerequisites installed and a Kubernetes cluster running, you can proceed with the KServe Quickstart installation.
KServe Quickstart Environments are for experimentation use only. For production installation, see our Administrator's Guide.
- Raw Deployment
- Serverless
curl -s "https://raw.githubusercontent.com/kserve/kserve/release-0.15/hack/quick_install.sh" | bash -r
curl -s "https://raw.githubusercontent.com/kserve/kserve/release-0.15/hack/quick_install.sh" | bash
Verify the installation by checking the status of the KServe components:
kubectl get pods -n kserve
You should see the KServe controller up and running:
NAME READY STATUS RESTARTS AGE
kserve-controller-manager-7f5b6c4d8f-abcde 1/1 Running 0 2m
kserve-localmodel-controller-manager-5b8b6574c7-jz42m 1/1 Running 0 2m
Next Steps
Now that you have a KServe Quickstart environment set up, you can start deploying and testing machine learning models. Here are some recommended next steps:
- 📖 First GenAI InferenceService - Deploy your first GenAI model using InferenceService.
- 📖 First Predictive InferenceService - Deploy your first predictive model using InferenceService.