Setting Up a Local Kubernetes Cluster with Kind for ML

Why Kind for ML Development?

Before deploying ML models to a production Kubernetes cluster, you need a local environment to test your deployments. Kind (Kubernetes in Docker) gives you a fully functional Kubernetes cluster running inside Docker containers.

Installing Kind

Prerequisites

• Docker installed and running
• kubectl installed

Install Kind

bash
# Linux/macOS
curl -Lo ./kind https://kind.sigs.k8s.io/dl/v0.20.0/kind-linux-amd64
chmod +x ./kind
sudo mv ./kind /usr/local/bin/kind

Create a Cluster

bash
kind create cluster --name ml-cluster

Verify it's running:

bash
kubectl cluster-info --context kind-ml-cluster
kubectl get nodes

Configuring for ML Workloads

ML models need more resources than typical web services. Create a cluster config:

yaml
# kind-config.yaml
kind: Cluster
apiVersion: kind.x-k8s.io/v1alpha4
nodes:
  - role: control-plane
  - role: worker
    extraMounts:
      - hostPath: /tmp/ml-models
        containerPath: /models

bash
kind create cluster --name ml-cluster --config kind-config.yaml

Installing the Kubernetes Dashboard

Visualize your cluster's state:

bash
kubectl apply -f https://raw.githubusercontent.com/kubernetes/dashboard/v2.7.0/aio/deploy/recommended.yaml

Create a service account for dashboard access:

bash
kubectl create serviceaccount dashboard-admin -n kubernetes-dashboard
kubectl create clusterrolebinding dashboard-admin \
  --clusterrole=cluster-admin \
  --serviceaccount=kubernetes-dashboard:dashboard-admin

Next Steps

With your local cluster running, you're ready to: - Install KServe for model serving - Deploy your first MLflow model - Test inference locally before going to production

Learn the complete workflow in our MLflow for Kubernetes course.