[SLO Routing] Add Latency Predictor sidecars and EPP tools

.gitignore

@@ -7,6 +7,7 @@
 bin/*
 Dockerfile.cross
 artifacts
+latencypredictor/__pycache__
 
 # Test binary, built with `go test -c`
 *.test

latencypredictor/Dockerfile-prediction

@@ -0,0 +1,26 @@
+# Use an official Python runtime as a parent image
+FROM python:3.11-slim
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Copy the requirements file and install dependencies
+# (It's good practice to manage dependencies in a requirements.txt file)
+
+
+RUN apt-get update && apt-get install -y \
+    libgomp1 \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the rest of the application code
+COPY . .
+
+# Expose the port the app runs on
+EXPOSE 8001
+
+# Command to run the application using uvicorn
+# We use 0.0.0.0 to bind to all network interfaces inside the container
+CMD ["uvicorn", "prediction_server:app", "--host", "0.0.0.0", "--port", "8001"]

latencypredictor/Dockerfile-test

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+# Dockerfile-test
+FROM python:3.9-slim
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    curl \
+    wget \
+    jq \
+    && rm -rf /var/lib/apt/lists/*
+
+# Set working directory
+WORKDIR /app
+
+# Copy requirements and install Python dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Install additional testing dependencies
+RUN pip install --no-cache-dir \
+    pytest \
+    pytest-asyncio \
+    requests \
+    httpx \
+    aiohttp
+
+# Copy test files
+COPY test_dual_server_client.py .
+
+
+# Create test results directory
+RUN mkdir -p /test-results
+
+# Set environment variables
+ENV PYTHONPATH=/app
+ENV PYTHONUNBUFFERED=1
+
+# Default command runs the specific test
+CMD ["pytest", "-v", "-s", "test_dual_server_client.py"]

latencypredictor/Dockerfile-training

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+# Use an official Python runtime as a parent image
+FROM python:3.11-slim
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Copy the requirements file and install dependencies
+# (It's good practice to manage dependencies in a requirements.txt file)
+
+
+RUN apt-get update && apt-get install -y \
+    libgomp1 \
+    && rm -rf /var/lib/apt/lists/*
+
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the rest of the application code
+COPY . .
+
+# Expose the port the app runs on
+EXPOSE 8000
+
+# Command to run the application using uvicorn
+# We use 0.0.0.0 to bind to all network interfaces inside the container
+CMD ["uvicorn", "training_server:app", "--host", "0.0.0.0", "--port", "8000"]

latencypredictor/README.md

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+# Latency Predictor - Build Guide
+
+This directory contains the Latency Predictor component with dual server architecture (training and prediction servers). Use the provided `build-deploy.sh` script to build and deploy container images to Google Cloud Platform.
+
+## Prerequisites
+
+- Docker (latest version)
+- Google Cloud SDK (`gcloud`) configured and authenticated
+- Required files in directory:
+  - `training_server.py`
+  - `prediction_server.py` 
+  - `requirements.txt`
+  - `Dockerfile-training`
+  - `Dockerfile-prediction`
+  - `dual-server-deployment.yaml`
+
+**Optional (for deployment and testing):**
+- kubectl configured for GKE cluster access
+
+## Configuration
+
+Before running the script, update the configuration variables in `build-deploy.sh`:
+
+```bash
+# Edit these values in the script
+PROJECT_ID="your-gcp-project-id"
+REGION="your-gcp-region"
+REPOSITORY="your-artifact-registry-repo"
+TRAINING_IMAGE="latencypredictor-training-server"
+PREDICTION_IMAGE="latencypredictor-prediction-server"
+TAG="latest"
+```
+
+## Usage
+
+### Build Images Only
+
+```bash
+# Make script executable
+chmod +x build-deploy.sh
+
+# Build and push images to registry
+./build-deploy.sh build
+./build-deploy.sh push
+```
+
+### Complete Build and Deploy (Optional)
+
+```bash
+# Run complete process (build, push, deploy, test)
+# Note: This requires GKE cluster access
+./build-deploy.sh all
+```
+
+### Individual Commands
+
+```bash
+# Check if all required files exist
+./build-deploy.sh check
+
+# Build Docker images only
+./build-deploy.sh build
+
+# Push images to Google Artifact Registry
+./build-deploy.sh push
+
+# Optional: Deploy to GKE cluster (requires cluster access)
+./build-deploy.sh deploy
+
+# Optional: Get service information and IPs
+./build-deploy.sh info
+
+# Optional: Test the deployed services
+./build-deploy.sh test
+```
+
+## What the Script Does
+
+### Build Phase (`./build-deploy.sh build`)
+- Builds training server image from `Dockerfile-training`
+- Builds prediction server image from `Dockerfile-prediction` 
+- Tags images for Google Artifact Registry
+- Images created:
+  - `latencypredictor-training-server:latest`
+  - `latencypredictor-prediction-server:latest`
+
+### Push Phase (`./build-deploy.sh push`)
+- Configures Docker for Artifact Registry authentication
+- Pushes both images to:
+  - `us-docker.pkg.dev/PROJECT_ID/REPOSITORY/latencypredictor-training-server:latest`
+  - `us-docker.pkg.dev/PROJECT_ID/REPOSITORY/latencypredictor-prediction-server:latest`
+
+### Deploy Phase (`./build-deploy.sh deploy`) - Optional
+- Applies Kubernetes manifests from `dual-server-deployment.yaml`
+- Waits for deployments to be ready (5-minute timeout)
+- Creates services:
+  - `training-service-external` (LoadBalancer)
+  - `prediction-service` (LoadBalancer)
+
+### Test Phase (`./build-deploy.sh test`) - Optional
+- Tests health endpoint: `/healthz`
+- Tests prediction endpoint: `/predict` with sample data
+- Sample prediction request:
+  ```json
+  {
+    "kv_cache_percentage": 0.3,
+    "input_token_length": 100,
+    "num_request_waiting": 2,
+    "num_request_running": 1,
+    "num_tokens_generated": 50
+  }
+  ```
+
+## Setup Instructions
+
+1. **Configure GCP Authentication**:
+   ```bash
+   gcloud auth login
+   gcloud config set project YOUR_PROJECT_ID
+   ```
+
+2. **Configure kubectl for GKE (Optional - only needed for deployment)**:
+   ```bash
+   gcloud container clusters get-credentials CLUSTER_NAME --zone ZONE
+   ```
+
+3. **Update Script Configuration**:
+   ```bash
+   # Edit build-deploy.sh with your project details
+   nano build-deploy.sh
+   ```
+
+4. **Build Images**:
+   ```bash
+   ./build-deploy.sh build
+   ./build-deploy.sh push
+   ```
+
+5. **Optional: Deploy and Test**:
+   ```bash
+   ./build-deploy.sh deploy
+   ./build-deploy.sh test
+   # Or run everything at once
+   ./build-deploy.sh all
+   ```
+
+## Troubleshooting
+
+### Permission Issues
+```bash
+chmod +x build-deploy.sh
+```
+
+### GCP Authentication
+```bash
+gcloud auth configure-docker us-docker.pkg.dev
+```
+
+### Check Cluster Access
+```bash
+kubectl cluster-info
+kubectl get nodes
+```
+
+### View Service Status
+```bash
+./build-deploy.sh info
+kubectl get services
+kubectl get pods
+```
+
+### Check Logs
+```bash
+# Training server logs
+kubectl logs -l app=training-server
+
+# Prediction server logs  
+kubectl logs -l app=prediction-server
+```
+
+## Development Workflow
+
+1. **Make code changes** to `training_server.py` or `prediction_server.py`
+2. **Test locally** (optional):
+   ```bash
+   python training_server.py
+   python prediction_server.py
+   ```
+3. **Build and push images**:
+   ```bash
+   ./build-deploy.sh build
+   ./build-deploy.sh push
+   ```
+
+4. **Optional: Deploy and test**:
+   ```bash
+   ./build-deploy.sh deploy
+   ./build-deploy.sh test
+   ```
+
+## Service Endpoints
+
+After successful deployment:
+
+- **Training Service**: External LoadBalancer IP (check with `./build-deploy.sh info`)
+- **Prediction Service**: External LoadBalancer IP (check with `./build-deploy.sh info`)
+- **Health Check**: `http://PREDICTION_IP/healthz`
+- **Prediction API**: `http://PREDICTION_IP/predict` (POST)
+
+## Manual Build (Alternative)
+
+If you need to build manually:
+
+```bash
+# Build training server
+docker build -f Dockerfile-training -t training-server .
+
+# Build prediction server  
+docker build -f Dockerfile-prediction -t prediction-server .
+```