Add Scenario for AI conformance

Related to:
 - #17830

Signed-off-by: Arnaud Meukam <ameukam@gmail.com>

Author: ameukam

tests/e2e/scenarios/ai-conformance/run-test.sh

@@ -0,0 +1,158 @@
+#!/usr/bin/env bash
+
+# Copyright 2026 The Kubernetes Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+set -o errexit
+set -o nounset
+set -o pipefail
+
+REPO_ROOT=$(git rev-parse --show-toplevel)
+source "${REPO_ROOT}"/tests/e2e/scenarios/lib/common.sh
+
+# AI Conformance requirements:
+# - Kubernetes 1.35
+# - NVIDIA L4 Instances (g6.xlarge on AWS)
+# - Gateway API
+# - Gang Scheduling (Kueue)
+# - Robust Controller (KubeRay)
+
+export K8S_VERSION=$(curl -L -s https://dl.k8s.io/release/stable.txt)
+export CLOUD_PROVIDER=aws
+# Ensure region with L4 (g6) availability
+export AWS_REGION="${AWS_REGION:-us-east-2}"
+
+# Check for g6.xlarge availability in the region
+echo "Checking availability of g6.xlarge in ${AWS_REGION}..."
+AVAILABILITY=$(aws ec2 describe-instance-type-offerings --location-type availability-zone --filters Name=instance-type,Values=g6.xlarge --region "${AWS_REGION}" --query 'InstanceTypeOfferings' --output text)
+if [[ -z "${AVAILABILITY}" ]]; then
+  echo "Error: g6.xlarge instances are not available in ${AWS_REGION}. Please choose a region with L4 GPU support."
+  exit 1
+fi
+
+kops-acquire-latest
+
+# Cluster Configuration
+# - Networking: Cilium with Gateway API enabled
+# - Nodes: g6.xlarge (L4 GPU)
+# - Runtime: NVIDIA enabled
+OVERRIDES="${OVERRIDES-} --networking=cilium"
+OVERRIDES="${OVERRIDES} --set=cluster.spec.networking.cilium.enableGatewayAPI=true"
+OVERRIDES="${OVERRIDES} --node-size=g6.xlarge"
+OVERRIDES="${OVERRIDES} --node-count=2"
+OVERRIDES="${OVERRIDES} --set=cluster.spec.containerd.nvidia.enabled=true"
+
+kops-up
+
+echo "----------------------------------------------------------------"
+echo "Deploying AI Conformance Components"
+echo "----------------------------------------------------------------"
+
+# 0. Gateway API CRDs (Required for Cilium)
+echo "Installing Gateway API CRDs v1.2.0..."
+kubectl apply -f https://github.com/kubernetes-sigs/gateway-api/releases/download/v1.2.0/standard-install.yaml
+
+# 1. NVIDIA Device Plugin
+echo "Installing NVIDIA Device Plugin..."
+kubectl apply -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/v0.17.0/nvidia-device-plugin.yml
+
+# 1.5 NVIDIA DRA Driver
+echo "Installing Helm..."
+curl -fsSL -o get_helm.sh https://raw.githubusercontent.com/helm/helm/main/scripts/get-helm-3
+chmod 700 get_helm.sh
+USE_SUDO=false HELM_INSTALL_DIR=. ./get_helm.sh
+export PATH="$(pwd):$PATH"
+
+echo "Installing NVIDIA DRA Driver..."
+helm repo add nvidia https://helm.ngc.nvidia.com/nvidia
+helm repo update
+helm install nvidia-dra-driver-gpu nvidia/nvidia-dra-driver-gpu \
+  --create-namespace \
+  --namespace nvidia-dra-driver-gpu \
+  --version 25.8.1 \
+  --set resources.gpus.enabled=true \
+  --wait
+
+# 2. Gang Scheduling (Kueue)
+echo "Installing Kueue..."
+kubectl apply --server-side -f https://github.com/kubernetes-sigs/kueue/releases/download/v0.14.8/manifests.yaml
+
+# 3. Robust Controller (KubeRay)
+echo "Installing KubeRay Operator..."
+# KubeRay 1.3.0
+kubectl apply -k "github.com/ray-project/kuberay/ray-operator/config/default?ref=v1.5.0"
+
+echo "----------------------------------------------------------------"
+echo "Verifying Cluster and Components"
+echo "----------------------------------------------------------------"
+
+# Wait for kOps validation
+"${KOPS}" validate cluster --wait=15m
+
+# Verify Components
+echo "Verifying NVIDIA Device Plugin..."
+kubectl rollout status daemonset -n kube-system nvidia-device-plugin-daemonset --timeout=5m || echo "Warning: NVIDIA Device Plugin not ready yet"
+
+echo "Verifying Kueue..."
+kubectl rollout status deployment -n kueue-system kueue-controller-manager --timeout=5m || echo "Warning: Kueue not ready yet"
+
+echo "Verifying KubeRay..."
+kubectl rollout status deployment -n kuberay-system kuberay-operator --timeout=5m || echo "Warning: KubeRay not ready yet"
+
+echo "Verifying Gateway API..."
+kubectl get gatewayclass || echo "Warning: GatewayClass not found"
+
+echo "Verifying Allocatable GPUs..."
+# Wait a bit for nodes to report resources
+sleep 30
+kubectl get nodes -o jsonpath='{range .items[*]}{.metadata.name}: {.status.allocatable.nvidia\.com/gpu} GPUs{"\n"}{end}'
+
+echo "Running Sample DRA Workload..."
+# Create a ResourceClaim and Pod to test DRA
+kubectl apply -f - <<EOF
+apiVersion: resource.k8s.io/v1
+kind: ResourceClaim
+metadata:
+  name: test-gpu-claim
+spec:
+  resourceClassName: nvidia-gpu
+---
+apiVersion: v1
+kind: Pod
+metadata:
+  name: test-gpu-pod
+spec:
+  restartPolicy: Never
+  containers:
+  - name: test
+    image: nvcr.io/nvidia/k8s/cuda-sample:vectoradd-cuda12.5.0-ubuntu22.04
+    command: ["/bin/sh", "-c"]
+    args: ["/cuda-samples/vectorAdd"]
+    resources:
+      claims:
+      - name: gpu
+  resourceClaims:
+  - name: gpu
+    resourceClaimName: test-gpu-claim
+EOF
+
+echo "Waiting for Sample Workload to Complete..."
+# Wait for the pod to succeed
+kubectl wait --for=condition=Ready pod/test-gpu-pod --timeout=5m || true
+kubectl logs test-gpu-pod || echo "Failed to get logs"
+
+# Note: The actual AI conformance test suite (e.g., k8s-ai-conformance binary)
+# would be executed here. For this scenario, we establish the compliant environment.
+
+echo "AI Conformance Environment Setup Complete."