edge-deployment-skill

# Edge Deployment Skill ## Overview Expert skill for optimizing and deploying machine learning models on robot edge devices including NVIDIA Jetson and embedded systems. ## Capabilities - Configure TensorRT optimization for NVIDIA Jetson - Set up ONNX model conversion and optimization - Implement INT8 and FP16 quantization - Configure DeepStream for video analytics - Set up CUDA graph optimization - Implement model pruning and distillation - Configure DLA (Deep Learning Accelerator) deployment - Set up multi-stream inference - Implement ROS2 inference nodes - Profile and benchmark on target hardware ## Target Processes - nn-model-optimization.js - object-detection-pipeline.js - rl-robot-control.js - field-testing-validation.js ## Dependencies - TensorRT - ONNX Runtime - NVIDIA Jetson SDK - DeepStream ## Usage Context This skill is invoked when processes require deploying ML models on edge devices with optimized inference performance. ## Output Artifacts - TensorRT engine files - ONNX optimized models - Quantization configurations - DeepStream pipeline configs - Inference benchmark reports - ROS2 inference node implementations