JetsonMCP

# JetsonMCP Examples Practical examples of using JetsonMCP for common edge AI and system management tasks. ## 🔋 Hardware Management ### Power Management **Check current power mode:** > "What power mode is my Jetson currently using?" *Expected response: Current power mode information with available options* **Switch to power-efficient mode:** > "Switch my Jetson to 5W power mode to save battery" *Expected response: Power mode changed confirmation* **Optimize for performance:** > "Set my Jetson to maximum performance mode for AI training" *Expected response: MAXN mode activation* ### Temperature Monitoring **Check current temperatures:** > "What's the temperature of my Jetson Nano right now?" *Expected response: CPU, GPU, and other thermal zone readings* **Monitor thermal conditions:** > "Monitor my Jetson's temperature for the next 2 minutes while I run this model" *Expected response: Time-series temperature data with analysis* **Set up thermal alerts:** > "Alert me if my Jetson gets too hot during this AI workload" *Expected response: Temperature monitoring with threshold warnings* ### GPU and Memory Management **Check GPU status:** > "Show me detailed information about my Jetson's GPU" *Expected response: GPU model, memory, utilization, and capabilities* **Monitor memory usage:** > "How much GPU memory is available for my next AI model?" *Expected response: Current memory usage and available space* **Check CUDA installation:** > "Verify that CUDA is properly installed and working" *Expected response: CUDA version, GPU accessibility, and framework compatibility* ## 🤖 AI/ML Workloads ### Framework Installation **Set up TensorFlow:** > "Install TensorFlow with GPU support on my Jetson Nano" *Expected response: Installation progress and verification* **Install PyTorch for computer vision:** > "I need PyTorch for a computer vision project. Can you install it with CUDA support?" *Expected response: PyTorch installation with GPU verification* **Check installed frameworks:** > "What AI frameworks are currently installed on my Jetson?" *Expected response: List of installed frameworks with versions* ### Model Deployment **Deploy a YOLOv5 model:** > "Download and deploy YOLOv5 model for object detection" *Command:* ```python { "action": "deploy_model", "model_name": "yolov5s", "model_url": "https://github.com/ultralytics/yolov5/releases/download/v6.0/yolov5s.pt" } ``` **Deploy custom ONNX model:** > "I have a custom ONNX model at ~/models/my_model.onnx. Can you deploy it for inference?" *Expected response: Model deployment with optimization suggestions* **List deployed models:** > "Show me all AI models currently deployed on my Jetson" *Expected response: Model inventory with sizes and types* ### Performance Optimization **Optimize model for inference:** > "Convert my PyTorch model to TensorRT for faster inference" *Expected response: Model conversion with performance benchmarks* **Benchmark model performance:** > "Run a performance benchmark on my deployed YOLOv5 model" *Expected response: Latency, throughput, and resource utilization metrics* **Memory optimization:** > "My model is running out of GPU memory. Can you help optimize it?" *Expected response: Memory analysis and optimization recommendations* ## 📦 Container Management ### Docker Setup **Install Docker with GPU support:** > "Set up Docker with NVIDIA GPU support for AI containers" *Expected response: Docker and NVIDIA Container Toolkit installation* **Run GPU-accelerated container:** > "Start a TensorFlow container with GPU access for development" *Command example:* ```bash docker run --gpus all -it tensorflow/tensorflow:latest-gpu-jupyter ``` **Deploy model serving container:** > "Deploy my trained model in a TensorFlow Serving container" *Expected response: Container deployment with API endpoints* ### Container Orchestration **Set up edge Kubernetes:** > "Install K3s lightweight Kubernetes for edge AI workloads" *Expected response: K3s installation and configuration* **Deploy distributed inference:** > "Deploy my object detection model across multiple Jetson devices" *Expected response: Multi-node deployment configuration* ## 🗺️ System Administration ### Package Management **System updates:** > "Update all packages on my Jetson Nano to the latest versions" *Expected response: Update process with package counts* **Install development tools:** > "Install essential development tools for AI projects" *Expected response: Installation of build tools, editors, and utilities* **Clean up disk space:** > "My Jetson is running low on storage. Help me clean up unnecessary files" *Expected response: Disk usage analysis and cleanup recommendations* ### Service Management **Check system services:** > "Show me the status of all important services on my Jetson" *Expected response: Service list with status indicators* **Restart networking:** > "I'm having network issues. Can you restart the networking service?" *Expected response: Service restart confirmation* **Set up auto-start service:** > "Configure my AI inference script to start automatically on boot" *Expected response: Systemd service creation and enablement* ### Network Configuration **Check network status:** > "Show me my Jetson's network configuration and connectivity" *Expected response: IP addresses, interfaces, and routing information* **Configure static IP:** > "Set up a static IP address for my Jetson Nano" *Expected response: Network configuration update* ## 🔍 Monitoring and Diagnostics ### System Health **Comprehensive health check:** > "Run a complete health check on my Jetson Nano system" *Expected response: CPU, memory, storage, temperature, and service status* **Performance monitoring:** > "Monitor system performance while I run my AI training job" *Expected response: Real-time resource utilization tracking* **Log analysis:** > "Check system logs for any errors or warnings" *Expected response: Log summary with important messages highlighted* ### AI Workload Monitoring **Model inference monitoring:** > "Monitor GPU utilization while my object detection model processes this video" *Expected response: Real-time GPU metrics during inference* **Training progress monitoring:** > "Track system resources during my neural network training session" *Expected response: CPU, GPU, memory, and thermal monitoring* ## 🔧 Development Workflows ### Jupyter Setup **Remote Jupyter access:** > "Set up Jupyter Notebook for remote AI development on my Jetson" *Expected response: Jupyter installation and remote access configuration* **Create AI development environment:** > "Prepare a complete Python environment for computer vision development" *Expected response: Package installation and environment setup* ### Model Development Pipeline **Complete ML pipeline setup:** > "Set up a complete machine learning pipeline from data preprocessing to model deployment" *Expected response: End-to-end pipeline configuration* **Automated model testing:** > "Create an automated testing setup for my AI models" *Expected response: Testing framework and CI/CD pipeline setup* ## 🚑 Emergency and Recovery ### System Recovery **Emergency cool-down:** > "My Jetson is overheating! Put it in emergency cool-down mode" *Expected response: Immediate power mode reduction and fan control* **Service recovery:** > "My AI service crashed. Can you restart it and check what went wrong?" *Expected response: Service restart with error analysis* **System reboot:** > "I need to reboot my Jetson safely. Can you do this properly?" *Expected response: Graceful system shutdown and reboot* ### Backup and Restore **Create system backup:** > "Create a backup of my current Jetson configuration and models" *Expected response: Backup creation with storage location* **Restore from backup:** > "Restore my Jetson from the backup I created last week" *Expected response: System restoration process* ## 📊 Advanced Use Cases ### Edge AI Fleet Management **Multi-device deployment:** > "Deploy the same AI model across 5 different Jetson devices" *Expected response: Coordinated deployment across multiple devices* **Load balancing:** > "Set up load balancing for inference requests across my Jetson cluster" *Expected response: Load balancer configuration and traffic distribution* ### IoT Integration **Sensor data processing:** > "Set up real-time processing for data from my IoT sensors" *Expected response: Data pipeline configuration with edge processing* **Edge-to-cloud sync:** > "Configure automatic synchronization of AI results to the cloud" *Expected response: Cloud integration and data sync setup* ### Custom Applications **Build custom AI application:** > "Help me build a custom real-time video analytics application" *Expected response: Application framework and component setup* **Optimize for specific use case:** > "Optimize my Jetson for autonomous drone applications" *Expected response: Hardware and software optimization for drone use case* --- ## 📝 Tips for Effective Usage ### 1. Be Specific - ✅ "Switch to 10W power mode for balanced performance" - ❌ "Change power settings" ### 2. Provide Context - ✅ "Install TensorFlow for my computer vision project" - ❌ "Install TensorFlow" ### 3. Ask for Explanations - ✅ "Explain why my GPU memory usage is high and how to optimize it" - ❌ "Fix GPU memory" ### 4. Request Monitoring - ✅ "Monitor temperatures while I run this intensive AI workload" - ❌ "Check temperature" ### 5. Combine Operations - ✅ "Install PyTorch, set up Jupyter, and create a sample computer vision notebook" - Multiple related tasks in one request These examples demonstrate the power and flexibility of JetsonMCP for managing edge AI systems through natural language interaction with Claude!