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feat: added test kubernetes files
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‎agentic_rag/k8s/deploy.sh‎

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#!/bin/bash
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# Deployment script for Agentic RAG
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# Function to display usage
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usage() {
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echo "Usage: $0 [--hf-token TOKEN] [--namespace NAMESPACE] [--cpu-only]"
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echo ""
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echo "Options:"
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echo " --hf-token TOKEN Hugging Face token (optional but recommended)"
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echo " --namespace NAMESPACE Kubernetes namespace to deploy to (default: default)"
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echo " --cpu-only Deploy without GPU support (not recommended for production)"
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exit 1
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}
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# Default values
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NAMESPACE="default"
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HF_TOKEN=""
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CPU_ONLY=false
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# Parse arguments
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while [[ $# -gt 0 ]]; do
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case $1 in
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--hf-token)
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HF_TOKEN="$2"
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shift 2
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;;
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--namespace)
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NAMESPACE="$2"
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shift 2
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;;
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--cpu-only)
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CPU_ONLY=true
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shift
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;;
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*)
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usage
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;;
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esac
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done
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# Create namespace if it doesn't exist
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kubectl get namespace $NAMESPACE > /dev/null 2>&1 || kubectl create namespace $NAMESPACE
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echo "Deploying Agentic RAG to namespace $NAMESPACE..."
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# Check for GPU availability if not in CPU-only mode
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if [[ "$CPU_ONLY" == "false" ]]; then
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echo "Checking for GPU availability..."
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GPU_COUNT=$(kubectl get nodes "-o=custom-columns=GPU:.status.allocatable.nvidia\.com/gpu" --no-headers | grep -v "<none>" | wc -l)
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if [[ "$GPU_COUNT" -eq 0 ]]; then
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echo "WARNING: No GPUs detected in the cluster!"
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echo "The deployment is configured to use GPUs, but none were found."
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echo "Options:"
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echo " 1. Install the NVIDIA device plugin: kubectl create -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/v0.14.0/nvidia-device-plugin.yml"
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echo " 2. Use --cpu-only flag to deploy without GPU support (not recommended for production)"
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echo " 3. Ensure your nodes have GPUs and proper drivers installed"
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read -p "Continue with deployment anyway? (y/n): " CONTINUE
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if [[ "$CONTINUE" != "y" && "$CONTINUE" != "Y" ]]; then
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echo "Deployment aborted."
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exit 1
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fi
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echo "Continuing with deployment despite no GPUs detected..."
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else
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echo "Found $GPU_COUNT nodes with GPUs available."
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fi
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fi
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# Create ConfigMap with Hugging Face token if provided
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if [[ -n "$HF_TOKEN" ]]; then
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echo "Using provided Hugging Face token..."
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cat <<EOF | kubectl apply -n $NAMESPACE -f -
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apiVersion: v1
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kind: ConfigMap
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metadata:
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name: agentic-rag-config
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data:
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config.yaml: |
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HUGGING_FACE_HUB_TOKEN: "$HF_TOKEN"
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EOF
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else
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echo "No Hugging Face token provided. Creating empty config..."
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cat <<EOF | kubectl apply -n $NAMESPACE -f -
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apiVersion: v1
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kind: ConfigMap
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metadata:
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name: agentic-rag-config
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data:
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config.yaml: |
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# No Hugging Face token provided
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# You can still use Ollama models
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EOF
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fi
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# Apply deployment and service
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if [[ "$CPU_ONLY" == "true" ]]; then
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echo "Deploying in CPU-only mode (not recommended for production)..."
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# Create a temporary CPU-only version of the deployment file
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sed '/nvidia.com\/gpu/d' local-deployment/deployment.yaml > local-deployment/deployment-cpu.yaml
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kubectl apply -n $NAMESPACE -f local-deployment/deployment-cpu.yaml
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rm local-deployment/deployment-cpu.yaml
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else
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kubectl apply -n $NAMESPACE -f local-deployment/deployment.yaml
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fi
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kubectl apply -n $NAMESPACE -f local-deployment/service.yaml
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echo "Deployment started. Check status with: kubectl get pods -n $NAMESPACE"
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echo "Access the application with: kubectl get service agentic-rag -n $NAMESPACE"
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echo "Note: Initial startup may take some time as models are downloaded."
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# Provide additional guidance for monitoring GPU usage
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if [[ "$CPU_ONLY" == "false" ]]; then
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echo ""
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echo "To monitor GPU usage:"
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echo " 1. Check pod status: kubectl get pods -n $NAMESPACE"
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echo " 2. View pod logs: kubectl logs -f deployment/agentic-rag -n $NAMESPACE"
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echo " 3. Check GPU allocation: kubectl describe pod -l app=agentic-rag -n $NAMESPACE | grep -A5 'Allocated resources'"
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fi

‎agentic_rag/k8s/local-deployment/configmap.yaml‎

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apiVersion: v1
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kind: ConfigMap
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metadata:
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name: agentic-rag-config
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data:
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config.yaml: |
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HUGGING_FACE_HUB_TOKEN: "your-huggingface-token"
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# Optional OpenAI configuration
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# .env: |
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# OPENAI_API_KEY=your-openai-api-key

‎agentic_rag/k8s/local-deployment/deployment.yaml‎

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apiVersion: apps/v1
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kind: Deployment
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metadata:
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name: agentic-rag
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labels:
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app: agentic-rag
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spec:
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replicas: 1
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selector:
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matchLabels:
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app: agentic-rag
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template:
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metadata:
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labels:
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app: agentic-rag
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spec:
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containers:
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- name: agentic-rag
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image: python:3.10-slim
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resources:
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requests:
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memory: "8Gi"
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cpu: "2"
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nvidia.com/gpu: "1"
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limits:
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memory: "16Gi"
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cpu: "4"
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nvidia.com/gpu: "1"
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ports:
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- containerPort: 7860
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name: gradio
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- containerPort: 11434
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name: ollama-api
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volumeMounts:
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- name: config-volume
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mountPath: /app/config.yaml
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subPath: config.yaml
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- name: data-volume
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mountPath: /app/embeddings
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- name: chroma-volume
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mountPath: /app/chroma_db
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- name: ollama-models
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mountPath: /root/.ollama
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command: ["/bin/bash", "-c"]
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args:
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- |
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apt-get update && apt-get install -y git curl gnupg
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# Install NVIDIA drivers and CUDA
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echo "Installing NVIDIA drivers and CUDA..."
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curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
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curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
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sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
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tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
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apt-get update && apt-get install -y nvidia-container-toolkit
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# Verify GPU is available
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echo "Verifying GPU availability..."
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nvidia-smi || echo "WARNING: nvidia-smi command failed. GPU might not be properly configured."
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# Install Ollama
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echo "Installing Ollama..."
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curl -fsSL https://ollama.com/install.sh | sh
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# Configure Ollama to use GPU
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echo "Configuring Ollama for GPU usage..."
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mkdir -p /root/.ollama
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echo '{"gpu": {"enable": true}}' > /root/.ollama/config.json
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# Start Ollama in the background with GPU support
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echo "Starting Ollama service with GPU support..."
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ollama serve &
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# Wait for Ollama to be ready
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echo "Waiting for Ollama to be ready..."
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until curl -s http://localhost:11434/api/tags >/dev/null; do
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sleep 5
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done
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# Verify models are using GPU
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echo "Verifying models are using GPU..."
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curl -s http://localhost:11434/api/tags | grep -q "llama3" && echo "llama3 model is available"
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# Clone and set up the application
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cd /app
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git clone https://github.com/devrel/devrel-labs.git .
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cd agentic_rag
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pip install -r requirements.txt
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# Start the Gradio app
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echo "Starting Gradio application..."
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python gradio_app.py
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env:
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- name: PYTHONUNBUFFERED
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value: "1"
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- name: OLLAMA_HOST
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value: "http://localhost:11434"
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- name: NVIDIA_VISIBLE_DEVICES
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value: "all"
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- name: NVIDIA_DRIVER_CAPABILITIES
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value: "compute,utility"
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- name: TORCH_CUDA_ARCH_LIST
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value: "7.0;7.5;8.0;8.6"
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volumes:
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- name: config-volume
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configMap:
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name: agentic-rag-config
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- name: data-volume
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emptyDir: {}
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- name: chroma-volume
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emptyDir: {}
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- name: ollama-models
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emptyDir: {}
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# For production, consider using persistent volumes instead of emptyDir
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# - name: data-volume
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# persistentVolumeClaim:
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# claimName: agentic-rag-data-pvc
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# - name: chroma-volume
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# persistentVolumeClaim:
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# claimName: agentic-rag-chroma-pvc
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# - name: ollama-models
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# persistentVolumeClaim:
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# claimName: ollama-models-pvc

‎agentic_rag/k8s/local-deployment/service.yaml‎

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apiVersion: v1
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kind: Service
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metadata:
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name: agentic-rag
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labels:
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app: agentic-rag
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spec:
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type: LoadBalancer # Use NodePort if LoadBalancer is not available
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ports:
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- port: 80
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targetPort: 7860
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protocol: TCP
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name: http
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selector:
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app: agentic-rag

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