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Capítulo 3: Escalado de cargas de trabajo de ML

Horizontal Pod Autoscaler​

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: churn-model-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: churn-model
  minReplicas: 2
  maxReplicas: 20
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: Pods
    pods:
      metric:
        name: requests_per_second
      target:
        type: AverageValue
        averageValue: "100"

Programación de GPU​

apiVersion: v1
kind: Pod
metadata:
  name: gpu-training
spec:
  containers:
  - name: trainer
    image: pytorch/pytorch:2.0-cuda12
    resources:
      limits:
        nvidia.com/gpu: 2  # Request 2 GPUs
  nodeSelector:
    accelerator: nvidia-tesla-a100
  tolerations:
  - key: "nvidia.com/gpu"
    operator: "Exists"
    effect: "NoSchedule"

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