feat: add scripts for the multi-cluster AI with KAITO demo

.gitignore

@@ -0,0 +1,12 @@
+# Ignore Python virtual environment directories
+venv/
+
+# Ignore cloned repositories for specific projects
+multi-cluster-ai-with-kaito/kubefleet/
+multi-cluster-ai-with-kaito/istio/
+multi-cluster-ai-with-kaito/semantic-router/
+
+# Ignore downloaded files for specific projects
+multi-cluster-ai-with-kaito/configure-helm-values.sh
+multi-cluster-ai-with-kaito/gpu-provisioner-values-template.yaml
+multi-cluster-ai-with-kaito/gpu-provisioner-values.yaml

README.md

@@ -1,2 +1,2 @@
 # KubeFleet Cookbook
-Examples and guides on using KubeFleet to manage multicluster scenarios.
+A collection of various demos, tutorials, and labs for using the KubeFleet project.

multi-cluster-ai-with-kaito/SETUP.md

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+# How to run the scripts in this tutorial
+
+The scripts in this tutorial will help you:
+
+* Create a fleet of 3 AKS (Azure Kubernetes Service) clusters for running LLM inference workloads and routing LLM queries.
+* Put the 3 clusters under the management of KubeFleet, a CNCF sandbox project for multi-cluster management, with an
+additional KubeFleet hub cluster (also an AKS cluster) as the management portal.
+* Set up KAITO, a CNCF sandbox project for easy LLM usage, on the clusters for facilitating LLM workloads with ease.
+* Connect the 3 clusters with an Istio service mesh.
+* Use Kubernetes Gateway API with Inference Extension for serving LLM queries.
+
+> Note that even though the scripts are set to use AKS clusters and related resources for simplicity reasons; the tutorial itself is not necessarily Azure specific. It can run on any Kubernetes environment, as long as inter-cluster connectivity can be established.
+
+## Before you begin
+
+* This tutorial assumes that you are familiar with basic Azure/AKS usage and Kubernetes usage.
+* If you don't have an Azure account, [create a free account](https://azure.microsoft.com/pricing/purchase-options/azure-account) before you begin.
+* Make sure that you have the following tools installed in your environment:
+    * The Azure CLI (`az`).
+    * The Kubernetes CLI (`kubectl`).
+    * Helm
+    * Docker
+    * The Istio CLI (istioctl)
+    * Go runtime (>=1.24)
+    * `git`
+    * `base64`
+    * `make`
+    * `curl`
+* The setup in the tutorial requires usage of GPU-enabled nodes (with NVIDIA A100 GPUs or similar specs).
+
+## Run the scripts
+
+Switch to the current directory and follow the steps below to run the scripts:
+
+```sh
+chmod +x setup.sh
+./setup.sh
+```
+
+It may take a while for the setup to complete.
+
+The script includes some configurable parameters; in most cases though, you should be able to just use
+the default values. See the list of parameters at the file `setup.sh`, and, if needed, set up
+environment variables accordingly to override the default values.
+
+## Verify the setup
+
+After the setup script completes, follow the steps below to verify the setup:
+
+* Switch to one of the clusters that is running the inference workload:
+
+    ```sh
+    MEMBER_1="${MEMBER_1:-model-serving-cluster-1}"
+    MEMBER_2="${MEMBER_2:-model-serving-cluster-2}"
+    MEMBER_3="${MEMBER_3:-query-routing-cluster}"
+    MEMBER_1_CTX=$MEMBER_1-admin
+    MEMBER_2_CTX=$MEMBER_2-admin
+    MEMBER_3_CTX=$MEMBER_3-admin
+
+    kubectl config use-context $MEMBER_1_CTX
+    kubectl get workspace
+    ```
+
+    You should see that the KAITO workspace with the DeepSeek model is up and running. Note that it may take 
+    a while for a GPU node to get ready and have the model downloaded/set up.
+
+* Similarly, switch to the other cluster that is running the inference workload and make sure that the Phi model
+is up and running:
+
+    ```sh
+    kubectl config use-context $MEMBER_2_CTX
+    kubectl get workspace
+    ```
+
+* Now, switch to the query routing cluster and send some queries to the inference gateway:
+
+    ```sh
+    kubectl config use-context $MEMBER_3_CTX
+
+    # Open another shell window.
+    kubectl port-forward svc/inference-gateway-istio 10000:80
+
+    curl -X POST http://localhost:10000/v1/chat/completions \
+    -H "Content-Type: application/json" \
+    -d '{
+        "model": "auto",
+        "messages": [{"role": "user", "content": "Prove the Pythagorean theorem step by step"}],
+        "max_tokens": 100    
+    }'
+    ```
+
+    You should see from the response that the query is being served by the DeepSeek model.
+
+    ```sh
+    curl -X POST -i localhost:10000/v1/chat/completions \
+    -H "Content-Type: application/json" \
+    -d '{
+        "model": "auto",
+        "messages": [{"role": "user", "content": "What is the color of the sky?"}],
+        "max_tokens": 100
+    }'
+    ```
+
+    You should see from the response that the query is being served by the Phi model.
+
+    > Note: the tutorial features a semantic router that classifies queries based on their categories and sends queries to a LLM that is best equipped to process the category. The process is partly non-deterministic due to the nature of LLM. If you believe that a query belongs to a specific category but is not served by the expected LLM; tweak the query text a bit and give it another try.
+
+## Additional steps
+
+You can set up the LiteLLM proxy to interact with the models using a web UI. Follow the steps in the [LiteLLM setup README](./litellm/README.md) to complete the setup.
+
+## Clean things up
+
+To clean things up, delete the Azure resource group that contains all the resources:
+
+```sh
+export RG="${RG:-kubefleet-kaito-demo-2025}"
+az group delete -n $RG
+```
+
+## Questions or comments?
+
+If you have any questions or comments please using our [Q&A Discussions](https://github.com/kubefleet-dev/kubefleet/discussions/categories/q-a). 
+
+If you find a bug or the solution doesn't work, please open an [Issue](https://github.com/kubefleet-dev/kubefleet/issues/new) so we can take a look. We welcome submissions too, so if you find a fix please open a PR!
+
+Also, consider coming to a [Community Meeting](https://bit.ly/kubefleet-cm-meeting) too!
+
+

multi-cluster-ai-with-kaito/azresources.sh

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+function create_azure_vnet() {
+    echo "Creating an Azure virtual network..."
+    az network vnet create \
+        --name $VNET \
+        -g $RG \
+        --location $LOCATION \
+        --address-prefix $VNET_ADDR_PREFIX \
+        --subnet-name $SUBNET_1 \
+        --subnet-prefixes $SUBNET_1_ADDR_PREFIX
+}
+
+function create_azure_vnet_subnet() {
+    az network vnet subnet create \
+        -g $RG \
+        --vnet-name $VNET \
+        -n $1 \
+        --address-prefixes $2
+}
+
+function create_azure_vnet_subnets() {
+    echo "Creating additional subnets in the virtual network..."
+    create_azure_vnet_subnet $SUBNET_2 $SUBNET_2_ADDR_PREFIX
+    create_azure_vnet_subnet $SUBNET_3 $SUBNET_3_ADDR_PREFIX
+}
+
+function create_aks_cluster() {
+    echo "Creating AKS cluster $1..."
+    az aks create \
+        --name $1 \
+        --resource-group $RG \
+        --location $LOCATION \
+        --vnet-subnet-id $2 \
+        --network-plugin azure \
+        --enable-oidc-issuer \
+        --enable-workload-identity \
+        --enable-managed-identity \
+        --generate-ssh-keys \
+        --node-vm-size $VM_SIZE \
+        --node-count 1 \
+        --service-cidr $3 \
+        --dns-service-ip $4
+}
+
+function create_kubefleet_hub_cluster() {
+    echo "Creating KubeFleet hub cluster $FLEET_HUB..."
+    az aks create \
+        --name $FLEET_HUB \
+        --resource-group $RG \
+        --location $LOCATION \
+        --network-plugin azure \
+        --enable-oidc-issuer \
+        --enable-workload-identity \
+        --enable-managed-identity \
+        --generate-ssh-keys \
+        --node-vm-size $VM_SIZE \
+        --node-count 1
+}
+
+function create_aks_clusters() {
+    SUBNET_1_ID=$(az network vnet subnet show --resource-group $RG --vnet-name $VNET --name $SUBNET_1 --query "id" --output tsv)
+    SUBNET_2_ID=$(az network vnet subnet show --resource-group $RG --vnet-name $VNET --name $SUBNET_2 --query "id" --output tsv)
+    SUBNET_3_ID=$(az network vnet subnet show --resource-group $RG --vnet-name $VNET --name $SUBNET_3 --query "id" --output tsv)
+
+    echo "Creating AKS clusters..."
+    create_aks_cluster $MEMBER_1 $SUBNET_1_ID 172.16.0.0/16 172.16.0.10
+    create_aks_cluster $MEMBER_2 $SUBNET_2_ID 172.17.0.0/16 172.17.0.10
+    create_aks_cluster $MEMBER_3 $SUBNET_3_ID 172.18.0.0/16 172.18.0.10
+    create_kubefleet_hub_cluster
+
+    echo "Retrieving admin credentials for AKS clusters..."
+    az aks get-credentials -n $MEMBER_1 -g $RG --admin
+    az aks get-credentials -n $MEMBER_2 -g $RG --admin
+    az aks get-credentials -n $MEMBER_3 -g $RG --admin
+    az aks get-credentials -n $FLEET_HUB -g $RG --admin
+}
+
+function create_acr() {
+    echo "Creating Azure Container Registry $ACR..."
+    az acr create \
+        --resource-group $RG \
+        --name $ACR \
+        --sku Standard \
+        --admin-enabled true
+
+    echo "Connecting the ACR to the AKS clusters..."
+    az aks update -n $MEMBER_1 -g $RG --attach-acr $ACR
+    az aks update -n $MEMBER_2 -g $RG --attach-acr $ACR
+    az aks update -n $MEMBER_3 -g $RG --attach-acr $ACR
+    az aks update -n $FLEET_HUB -g $RG --attach-acr $ACR
+
+    echo "Logging into the ACR..."
+    az acr login --name $ACR
+}

multi-cluster-ai-with-kaito/istio.sh

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+function prep_istio_setup() {
+    echo "Cloning the Istio source code repository..."
+    git clone https://github.com/istio/istio.git
+    pushd istio
+
+    git fetch --all
+    git checkout $ISTIO_TAG
+}
+
+function connect_to_multi_cluster_service_mesh() {
+    echo "Connecting AKS cluster $1 to the multi-cluster Istio service mesh..."
+    kubectl config use-context $2
+    go run ./istioctl/cmd/istioctl install \
+        --context $2 \
+        --set tag=$ISTIO_TAG \
+        --set hub=gcr.io/istio-release \
+        --set values.global.meshID=simplemesh \
+        --set values.global.multiCluster.clusterName=$1 \
+        --set values.global.network=simplenet \
+        --set values.pilot.env.ENABLE_GATEWAY_API_INFERENCE_EXTENSION=true
+
+    istioctl create-remote-secret --context=$3 --name=$4 --server $5 | kubectl apply --context=$2 -f -
+    istioctl create-remote-secret --context=$6 --name=$7 --server $8 | kubectl apply --context=$2 -f -
+}
+
+function set_up_istio() {
+    echo "Performing some preparatory steps before setting Istio up..."
+    prep_istio_setup
+
+    echo "Setting up the Istio multi-cluster service mesh on the KubeFleet member clusters..."
+    MEMBER_1_ADDR=https://$(az aks show --resource-group $RG --name $MEMBER_1 --query "fqdn" -o tsv):443
+    MEMBER_2_ADDR=https://$(az aks show --resource-group $RG --name $MEMBER_2 --query "fqdn" -o tsv):443
+    MEMBER_3_ADDR=https://$(az aks show --resource-group $RG --name $MEMBER_3 --query "fqdn" -o tsv):443
+
+    connect_to_multi_cluster_service_mesh $MEMBER_1 $MEMBER_1_CTX $MEMBER_2_CTX $MEMBER_2 $MEMBER_2_ADDR $MEMBER_3_CTX $MEMBER_3 $MEMBER_3_ADDR
+    connect_to_multi_cluster_service_mesh $MEMBER_2 $MEMBER_2_CTX $MEMBER_1_CTX $MEMBER_1 $MEMBER_1_ADDR $MEMBER_3_CTX $MEMBER_3 $MEMBER_3_ADDR
+    connect_to_multi_cluster_service_mesh $MEMBER_3 $MEMBER_3_CTX $MEMBER_1_CTX $MEMBER_1 $MEMBER_1_ADDR $MEMBER_2_CTX $MEMBER_2 $MEMBER_2_ADDR
+
+    popd
+}

multi-cluster-ai-with-kaito/kaito.sh

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+function prep_kaito_setup() {
+    echo "Adding the KAITO Helm charts..."
+    helm repo add kaito https://kaito-project.github.io/kaito/charts/kaito
+    helm repo update
+
+    echo "Retrieving the KAITO GPU Provisioner setup script..."
+    GPU_PROVISIONER_VERSION=0.3.7
+    curl -sO https://raw.githubusercontent.com/Azure/gpu-provisioner/main/hack/deploy/configure-helm-values.sh
+}
+
+function install_kaito_core() {
+    echo "Installing KAITO core components in member cluster $1..."
+    kubectl config use-context $2
+    helm upgrade --install kaito-workspace kaito/workspace \
+        --namespace kaito-workspace \
+        --create-namespace \
+        --set clusterName="$1" \
+        --set featureGates.gatewayAPIInferenceExtension=true \
+        --wait
+}
+
+function install_kaito_gpu_provisioner() {
+    echo "Installing KAITO GPU provisioner in member cluster $1..."
+    kubectl config use-context $2
+
+    echo "Creating managed identity..."
+    local IDENTITY_NAME="kaitogpuprovisioner-$1"
+    az identity create --name $IDENTITY_NAME -g $RG
+    local IDENTITY_PRINCIPAL_ID=$(az identity show --name $IDENTITY_NAME -g $RG --query 'principalId' -o tsv)
+    az role assignment create \
+        --assignee $IDENTITY_PRINCIPAL_ID \
+        --scope /subscriptions/$SUBSCRIPTION/resourceGroups/$RG/providers/Microsoft.ContainerService/managedClusters/$1 \
+        --role "Contributor"
+
+    echo "Configuring Helm values..."
+    chmod +x ./configure-helm-values.sh && ./configure-helm-values.sh $1 $RG $IDENTITY_NAME
+
+    echo "Installing Helm chart..."
+    helm upgrade --install gpu-provisioner \
+        --values gpu-provisioner-values.yaml \
+        --set settings.azure.clusterName=$1 \
+        --wait \
+        https://github.com/Azure/gpu-provisioner/raw/gh-pages/charts/gpu-provisioner-$GPU_PROVISIONER_VERSION.tgz \
+        --namespace gpu-provisioner \
+        --create-namespace
+
+    echo "Enabling federated authentication..."
+    local AKS_OIDC_ISSUER=$(az aks show -n $1 -g $RG --query "oidcIssuerProfile.issuerUrl" -o tsv)
+    az identity federated-credential create \
+        --name kaito-federated-credential-$1 \
+        --identity-name $IDENTITY_NAME \
+        -g $RG \
+        --issuer $AKS_OIDC_ISSUER \
+        --subject system:serviceaccount:"gpu-provisioner:gpu-provisioner" \
+        --audience api://AzureADTokenExchange
+}
+
+function set_up_kaito() {
+    echo "Performing some preparatory steps before setting KAITO up..."
+    prep_kaito_setup
+
+    echo "Installing KAITO in member cluster $MEMBER_1..."
+    install_kaito_core $MEMBER_1 $MEMBER_1_CTX
+    install_kaito_gpu_provisioner $MEMBER_1 $MEMBER_1_CTX
+
+    echo "Installing KAITO in member cluster $MEMBER_2..."
+    install_kaito_core $MEMBER_2 $MEMBER_2_CTX
+    install_kaito_gpu_provisioner $MEMBER_2 $MEMBER_2_CTX
+}