RunPod Windows 11 Compute Offloader

This repository contains a RunPod Serverless worker and Windows 11 client for intelligent compute offloading.

🚀 Features

• Smart Offloading: Automatically offloads tasks when system RAM > 80% or CPU > 85%
• Multiple Task Types: Text processing, data analysis, Python execution, file operations
• Cost Efficient: Only pay for compute time when RunPod is actually processing
• Easy Integration: Simple Python client for Windows 11

📁 Repository Structure

├── worker.py          # RunPod Serverless worker
├── requirements.txt   # Python dependencies
├── Dockerfile         # Container configuration
├── runpod_client.py   # Windows 11 client script
└── README.md         # This file

🛠️ Supported Tasks

• system_info: Get system information
• python_execution: Execute Python code remotely
• text_processing: Analyze and transform text
• data_analysis: Process CSV data
• file_processing: Handle file operations
• compression: Compress data
• batch_operations: Run multiple tasks

🔧 Setup Instructions

1. RunPod Configuration

• Queue vs Load Balancer: Choose Load Balancer
• CPU vs GPU: Choose CPU for general compute tasks
• CPU Configuration:
  • Compute-Optimized: 4-8 vCPUs for CPU-intensive tasks
  • General Purpose: 2-4 vCPUs for balanced workloads

2. Deploy to RunPod

1. Create new Serverless endpoint
2. Select "GitHub" as source
3. Connect to FunkyChicken420/RunPod repository
4. Choose CPU configuration based on your needs
5. Deploy and get your Endpoint ID

3. Windows 11 Setup

1. Install Python dependencies: pip install requests psutil
2. Get your RunPod API key from Account Settings
3. Update runpod_client.py with your credentials
4. Run: python runpod_client.py

💻 Usage Examples

from runpod_client import RunPodOffloader

# Initialize client
client = RunPodOffloader("your-endpoint-id", "your-api-key")

# Test connection
client.test_system_info()

# Execute heavy Python code
result = client.execute_python_code("""
# Your intensive computation here
import numpy as np
data = np.random.rand(1000000)
result = np.fft.fft(data)
print("FFT computation completed")
""")

# Process text file
result = client.process_text_file("large_document.txt", operation="analyze")

# Analyze CSV data
result = client.analyze_csv_data("large_dataset.csv")

# Start automatic monitoring
client.start_monitoring(interval=10)

💰 Cost Optimization

• CPU-only tasks: ~$0.01-0.05 per minute
• Smart thresholds: Only offloads when system is stressed
• Per-second billing: Pay only for actual processing time
• Automatic scaling: Workers shut down when idle

🔍 Monitoring

The client automatically monitors:

• RAM usage percentage
• CPU usage percentage
• Automatic offloading triggers
• Real-time system status

🚨 Troubleshooting

Connection Issues:

• Verify Endpoint ID and API key
• Check RunPod dashboard for endpoint status
• Ensure endpoint is deployed and running

High Costs:

• Check monitoring thresholds
• Review task complexity
• Use CPU workers for non-GPU tasks

Slow Performance:

• Choose appropriate CPU configuration
• Optimize task data size
• Consider active workers for frequent use

📊 Performance Tips

1. Choose Right Configuration:

   • Light tasks: 2 vCPU General Purpose
   • Heavy computation: 8 vCPU Compute-Optimized

2. Optimize Task Size:

   • Break large tasks into smaller chunks
   • Use batch operations for multiple small tasks

3. Monitor Costs:

   • Set up cost alerts in RunPod dashboard
   • Review usage patterns regularly

🔧 Advanced Configuration

Custom Task Types

Add new task types to worker.py:

elif task_type == "custom_task":
    return handle_custom_task(input_data)

Adjust Thresholds

Modify monitoring thresholds in client:

client.ram_threshold = 90  # Higher threshold
client.cpu_threshold = 95  # Less sensitive

📝 License

MIT License - Feel free to modify and use for your projects.