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roanbrasil
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devops-gitops-corpus

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---
## 🛠️ Configuration Customization
- Update `model_name` and `model_base_path` in the deployment
- Replace `hostPath` with `PersistentVolumeClaim` bound to cloud storage
- Modify resource requests/limits for TensorFlow container
---
## 🧹 Cleanup
```bash
kubectl delete -f https://raw.githubusercontent.com/kubernetes/examples/refs/heads/master/ai/model-serving-tensorflow/ingress.yaml # Optional
kubectl delete -f https://raw.githubusercontent.com/kubernetes/examples/refs/heads/master/ai/model-serving-tensorflow/service.yaml
kubectl delete -f https://raw.githubusercontent.com/kubernetes/examples/refs/heads/master/ai/model-serving-tensorflow/deployment.yaml
kubectl delete -f https://raw.githubusercontent.com/kubernetes/examples/refs/heads/master/ai/model-serving-tensorflow/pvc.yaml
kubectl delete -f https://raw.githubusercontent.com/kubernetes/examples/refs/heads/master/ai/model-serving-tensorflow/pv.yaml
```
---
## 4 Further Reading / Next Steps
- [TensorFlow Serving](https://www.tensorflow.org/tfx/serving)
- [TF Serving REST API Reference](https://www.tensorflow.org/tfx/serving/api_rest)
- [Kubernetes Ingress Controller](https://kubernetes.io/docs/concepts/services-networking/ingress-controllers/)
- [Persistent Volumes](https://kubernetes.io/docs/concepts/storage/persistent-volumes/)
<|endoftext|>
# source: k8s_examples/AI/vllm-deployment/vllm-service.yaml type: yaml
apiVersion: v1
kind: Service
metadata:
name: vllm-service
spec:
selector:
app: gemma-server
type: ClusterIP
ports:
- protocol: TCP
port: 8080
targetPort: 8080
<|endoftext|>
# source: k8s_examples/AI/vllm-deployment/vllm-deployment.yaml type: yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: vllm-gemma-deployment
spec:
replicas: 1
selector:
matchLabels:
app: gemma-server
template:
metadata:
labels:
app: gemma-server
# Labels for better functionality within GKE.
# ai.gke.io/model: gemma-3-1b-it
# ai.gke.io/inference-server: vllm
# examples.ai.gke.io/source: user-guide
spec:
containers:
- name: inference-server
image: vllm/vllm-openai:v0.11.0
resources:
requests:
cpu: "2"
memory: "10Gi"
ephemeral-storage: "10Gi"
nvidia.com/gpu: "1"
limits:
cpu: "2"
memory: "10Gi"
ephemeral-storage: "10Gi"
nvidia.com/gpu: "1"
command: ["python3", "-m", "vllm.entrypoints.openai.api_server"]
args:
- --model=$(MODEL_ID)
- --tensor-parallel-size=1
- --host=0.0.0.0
- --port=8080
# --- ADD THESE LINES TO FIX POSSIBLE OOM ERRORS ---
- --gpu-memory-utilization=0.85
- --max-num-seqs=64
env:
# 1 billion parameter model (smallest gemma model)
- name: MODEL_ID
value: google/gemma-3-1b-it
# Necessary for vLLM images >= 0.8.5.
# Ref - https://github.com/vllm-project/vllm/issues/18859
- name: LD_LIBRARY_PATH
value: "/usr/local/nvidia/lib64:/usr/local/cuda/lib64"
- name: HUGGING_FACE_HUB_TOKEN
valueFrom:
secretKeyRef:
name: hf-secret