---
# AegisLM GPU Node Pool Configuration
# Kubernetes node pool configuration for GPU workloads
# Multi-Agent Adversarial LLM Evaluation Framework

# GPU Node Pool Configuration
# This defines the node selector and taints for GPU workloads
# Apply with: kubectl apply -f gpu-node-pool.yaml

---
# Node Pool Configuration for GPU workloads
apiVersion: v1
kind: ConfigMap
metadata:
  name: aegislm-gpu-config
  namespace: aegislm
  labels:
    app: aegislm
    component: gpu-pool
data:
  # Node selector labels
  node-selector.json: |
    {
      "workload": "benchmark",
      "accelerator": "nvidia"
    }
  
  # GPU tolerations
  gpu-tolerations.json: |
    [
      {
        "key": "nvidia.com/gpu",
        "operator": "Exists",
        "effect": "NoSchedule"
      }
    ]
  
  # Resource limits per pod
  resource-limits.json: |
    {
      "nvidia.com/gpu": 1,
      "cpu": "4",
      "memory": "16Gi"
    }

---
# Cluster Autoscaler Configuration
# Add this to your cluster autoscaler deployment
apiVersion: v1
kind: ConfigMap
metadata:
  name: cluster-autoscaler-status
  namespace: kube-system
  labels:
    app: cluster-autoscaler
data:
  # GPU node pool scaling configuration
  # Note: This is informational - actual autoscaler config depends on your cloud provider
  gpu-scaling-rules.yaml: |
    # GPU Node Pool Auto-Scaling Rules
    # 
    # Scale UP condition:
    #   pending_gpu_jobs > available_gpus
    #
    # Scale DOWN condition:
    #   gpu_utilization < threshold for N minutes
    #
    # Example configuration for GKE:
    # gcloud container clusters update CLUSTER_NAME \
    #   --enable-autoscaling \
    #   --min-nodes=0 \
    #   --max-nodes=10 \
    #   --node-pool=gpu-pool
    
    # Scaling formula:
    # T_scale_up = max(1, pending_jobs - available_gpus)
    # T_scale_down = max(0, idle_minutes - cool_down_period)
    
    # Parameters:
    # - min_nodes: 0 (scale to zero when idle)
    # - max_nodes: 10 (max GPU nodes)
    # - scale_up_delay: 1 minute
    # - scale_down_delay: 10 minutes (cool down period)
    # - gpu_utilization_threshold: 30% (scale down if below)
    
    # Spot/Preemptive instance configuration (future):
    # - Use spot instances for cost optimization
    # - Handle spot interruptions gracefully
    
---
# GPU Node Labels (for manual node labeling)
apiVersion: v1
kind: ConfigMap
metadata:
  name: node-labels
  namespace: aegislm
  labels:
    app: aegislm
data:
  # Label GPU nodes with these commands:
  # kubectl label nodes <node-name> workload=benchmark
  # kubectl label nodes <node-name> accelerator=nvidia
  # kubectl label nodes <node-name> gpu-type=<type>  # e.g., v100, a100, a10
  
  label-commands.sh: |
    #!/bin/bash
    # Label GPU nodes for AegisLM workloads
    
    # For each GPU node:
    # kubectl label nodes $NODE_NAME \
    #   workload=benchmark \
    #   accelerator=nvidia \
    #   gpu-type=a10 \
    #   gpu-count=1

---
# GPU Resource Quota (optional - for multi-tenant)
apiVersion: v1
kind: ResourceQuota
metadata:
  name: gpu-quota
  namespace: aegislm
  labels:
    app: aegislm
spec:
  hard:
    # Limit total GPUs across all pods
    nvidia.com/gpu: "10"
    # Limit CPU and memory
    requests.cpu: "40"
    requests.memory: "64Gi"
    limits.cpu: "80"
    limits.memory: "128Gi"
  scopeSelector:
    matchExpressions:
      - operator: In
        scopeName: PriorityClass
        values: ["high-priority", "normal-priority"]

---
# LimitRange for GPU Pods
apiVersion: v1
kind: LimitRange
metadata:
  name: gpu-limits
  namespace: aegislm
  labels:
    app: aegislm
spec:
  limits:
    # Default limits for GPU pods
    - type: Container
      default:
        nvidia.com/gpu: "1"
        cpu: "4"
        memory: "16Gi"
      defaultRequest:
        nvidia.com/gpu: "1"
        cpu: "2"
        memory: "8Gi"
      max:
        nvidia.com/gpu: "2"
        cpu: "8"
        memory: "32Gi"
      min:
        nvidia.com/gpu: "0"
        cpu: "100m"
        memory: "128Mi"
    
    # Pod-level limits
    - type: Pod
      max:
        nvidia.com/gpu: "2"
        cpu: "16"
        memory: "64Gi"

---
# PriorityClass for GPU workloads
apiVersion: scheduling.k8s.io/v1
kind: PriorityClass
metadata:
  name: high-priority
  labels:
    app: aegislm
value: 1000000
globalDefault: false
description: "High priority for GPU-accelerated benchmark jobs"

---
# PriorityClass for normal workloads
apiVersion: scheduling.k8s.io/v1
kind: PriorityClass
metadata:
  name: normal-priority
  labels:
    app: aegislm
value: 0
globalDefault: true
description: "Normal priority for standard workloads"

---
# Horizontal Pod Autoscaler for GPU node pool (if using metrics-server)
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: aegislm-gpu-hpa
  namespace: aegislm
  labels:
    app: aegislm
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: aegislm-worker
  minReplicas: 0
  maxReplicas: 10
  metrics:
    - type: Resource
      resource:
        name: nvidia.com/gpu
        target:
          type: Utilization
          averageUtilization: 70
  behavior:
    scaleDown:
      stabilizationWindowSeconds: 300
      policies:
        - type: Percent
          value: 50
          periodSeconds: 60
    scaleUp:
      stabilizationWindowSeconds: 60
      policies:
        - type: Percent
          value: 100
          periodSeconds: 15
        - type: Pods
          value: 2
          periodSeconds: 15
      selectPolicy: Max