edge-ml
CommunityDeploy ML to edge devices with ease.
Software Engineering#quantization#pruning#on-device#distillation#edge-ml#onnx-runtime-mobile#core-ml
Authorinfantesromeroadrian
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Edge ML deployment requires systematic optimization and suitable runtimes for on-device inference across mobile, IoT, and embedded systems.
Core Features & Use Cases
- End-to-end edge deployment guidance covering model optimization (quantization, pruning, distillation), and runtimes (TF Lite, ONNX Runtime Mobile, Core ML).
- Platform-aware conversions and profiling for mobile and embedded devices.
- Use Case: Prepare a model for on-device inference with a 4x size reduction and latency improvements.
Quick Start
Guide me through converting a PyTorch model to a mobile-optimized edge-ML pipeline.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: edge-ml Download link: https://github.com/infantesromeroadrian/arca-claude-code/archive/main.zip#edge-ml Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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