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devops-gitops-corpus

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## 1 Purpose / What You'll Learn
This example demonstrates how to deploy a TensorFlow model for inference using [TensorFlow Serving](https://www.tensorflow.org/serving) on Kubernetes. You’ll learn how to:
- Set up TensorFlow Serving with a pre-trained model
- Use a PersistentVolume to mount your model directory
- Expose the inference endpoint using a Kubernetes `Service` and `Ingress`
- Send a sample prediction request to the model
---
## 📚 Table of Contents
- [Prerequisites](#prerequisites)
- [Quick Start / TL;DR](#quick-start--tldr)
- [Detailed Steps & Explanation](#detailed-steps--explanation)
- [Verification / Seeing it Work](#verification--seeing-it-work)
- [Configuration Customization](#configuration-customization)
- [Cleanup](#cleanup)
- [Further Reading / Next Steps](#further-reading--next-steps)
---
## ⚙️ Prerequisites
- Kubernetes cluster (tested with v1.29+)
- `kubectl` configured
- Optional: `ingress-nginx` for external access
- x86-based machine (for running TensorFlow Serving image)
- Local hostPath support (for demo) or a cloud-based PVC
---
## ⚡ Quick Start / TL;DR
```bash
# Apply manifests
kubectl apply -f https://raw.githubusercontent.com/kubernetes/examples/refs/heads/master/ai/model-serving-tensorflow/pv.yaml
kubectl apply -f https://raw.githubusercontent.com/kubernetes/examples/refs/heads/master/ai/model-serving-tensorflow/pvc.yaml
kubectl apply -f https://raw.githubusercontent.com/kubernetes/examples/refs/heads/master/ai/model-serving-tensorflow/deployment.yaml
kubectl apply -f https://raw.githubusercontent.com/kubernetes/examples/refs/heads/master/ai/model-serving-tensorflow/service.yaml
kubectl apply -f https://raw.githubusercontent.com/kubernetes/examples/refs/heads/master/ai/model-serving-tensorflow/ingress.yaml # Optional
```
---
## 2. Expose the Servic
### 1. PersistentVolume & PVC Setup
> ⚠️ Note: For local testing, `hostPath` is used to mount `/mnt/models/my_model`. In production, replace this with a cloud-native storage backend (e.g., AWS EBS, GCP PD, or NFS).
Model folder structure:
```
/mnt/models/my_model/
└── 1/
├── saved_model.pb
└── variables/
```
---
### 2. Expose the Service
- A `ClusterIP` service exposes gRPC (8500) and REST (8501).
- An optional `Ingress` exposes `/tf/v1/models/my_model:predict` to external clients.
Update the `host` value in `ingress.yaml` to match your domain.
---
## 3 Verification / Seeing it Work
If using ingress:
```bash
curl -X POST http://<ingress-host>/tf/v1/models/my_model:predict \
-H "Content-Type: application/json" \
-d '{ "instances": [[1.0, 2.0, 5.0]] }'
```
Expected output:
```json
{
"predictions": [...]
}
```
To verify the pod is running:
```bash
kubectl get pods
kubectl wait --for=condition=Available deployment/tf-serving --timeout=300s
kubectl logs deployment/tf-serving
```