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# This label is critical. The ServiceMonitor uses this label to find this
# specific Service. If the labels don't match, Prometheus will not be
# able to discover the metrics endpoint.
# GKE-SPECIFIC: This label is used by GKE's managed service. For a custom
# deployment, you would use a more generic label like 'nvidia-dcgm-exporter'.
app.kubernetes.io/name: gke-managed-dcgm-exporter
spec:
selector:
# This selector tells the Service which pods to route traffic to.
# It must match the labels on the DCGM exporter pods.
# GKE-SPECIFIC: This selector matches the labels on GKE's managed DCGM pods.
app.kubernetes.io/name: gke-managed-dcgm-exporter
ports:
- # The 'name' of this port is important. The ServiceMonitor will specifically
# look for a port with this name to scrape metrics from.
name: metrics
port: 9400
targetPort: 9400
<|endoftext|>
# source: k8s_examples/AI/vllm-deployment/hpa/gpu-service-monitor-generic.yaml type: yaml
# This ServiceMonitor is for users who have MANUALLY installed the NVIDIA DCGM
# Exporter, for example, on EKS or AKS. It tells the Prometheus Operator
# how to discover and scrape metrics from the DCGM exporter's Service.
apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
name: nvidia-dcgm-exporter-servicemonitor
namespace: monitoring
labels:
# This label is used by the Prometheus Operator to discover this
# ServiceMonitor. It must match the 'serviceMonitorSelector' configured
# in the Prometheus custom resource.
release: prometheus
spec:
# This selector identifies the Service to scrape. It must match the labels
# on the 'gpu-dcgm-exporter-service' Service defined in the
# 'gpu-dcgm-exporter-service.yaml' file.
selector:
matchLabels:
app.kubernetes.io/name: gpu-dcgm-exporter
# This selector specifies which namespace to search for the target Service.
# The 'gpu-dcgm-exporter-service' is deployed in the 'monitoring'
# namespace by the Helm chart instructions.
namespaceSelector:
matchNames:
- monitoring
endpoints:
- port: metrics
interval: 15s
<|endoftext|>
# source: k8s_examples/AI/vllm-deployment/hpa/gpu-horizontal-pod-autoscaler.yaml type: yaml
# This HorizontalPodAutoscaler (HPA) targets the vLLM deployment and scales
# it based on the average GPU utilization across all pods. It uses the
# custom metric 'gpu_utilization_percent', which is provided by the
# Prometheus Adapter.
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
name: gemma-server-gpu-hpa
spec:
# scaleTargetRef points the HPA to the deployment it needs to scale.
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: vllm-gemma-deployment
minReplicas: 1
maxReplicas: 5
metrics:
- type: Pods
pods:
metric:
# This is the custom metric that the HPA will query.
# IMPORTANT: This name ('gpu_utilization_percent') is not the raw metric
# from the DCGM exporter. It is the clean, renamed metric that is
# exposed by the Prometheus Adapter. The names must match exactly.
name: gpu_utilization_percent
target:
type: AverageValue
# This is the target value for the metric. The HPA will add or remove
# pods to keep the average GPU utilization across all pods at 20%.
averageValue: 20
behavior:
scaleUp:
# The stabilizationWindowSeconds is set to 0 to allow for immediate
# scaling up. This is a trade-off:
# - For highly volatile workloads, immediate scaling is critical to
# maintain performance and responsiveness.
# - However, this also introduces a risk of over-scaling if the workload
# spikes are very brief. A non-zero value would make the scaling
# less sensitive to short-lived spikes, but could introduce latency
# if the load persists.
stabilizationWindowSeconds: 0
policies:
- type: Pods
value: 4
periodSeconds: 15
- type: Percent