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Edge deployment

Skill a5c-ai/babysitter/library/specializations/robotics-simulation/skills/edge-deployment

ML model optimization and deployment on robot edge devices (Jetson, embedded)From its SKILL.md

Install
npx -y skills add a5c-ai/babysitter --skill edge-deployment

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SKILL.md

1.6 KB, 239 tokens by cl100k_base, as published. Nobody here has run it

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

What ships with it: 1 file

581 B alongside SKILL.md

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