ml-optimization

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name: ml-optimization description: Optimizes TensorFlow.js WebGPU backend, WebLLM offline inference, and implements memory safety for heavy ML models license: MIT compatibility: opencode metadata: audience: developers workflow: development

What I do

I optimize the ML pipeline for performance and memory efficiency. I manage TF.js WebGPU backend configuration, WebLLM offline inference, and ensure proper memory cleanup to prevent leaks.

When to use me

Use this when:

• You're working with TF.js or WebLLM models
• You notice memory leaks or performance regressions
• You're updating model weights or configurations

Key Concepts

• TF.js WebGPU: GPU acceleration for browser ML
• WebLLM: Browser-based LLM for offline inference
• SmolLM2: Efficient LLM model variant
• Memory Safety: Proper tensor cleanup
• Manual Chunks: Code splitting for model loading

Source Files

• services/vision.ts: ML model integration
• vite.config.ts: Build configuration for chunks
• plans/02_edge_ml_implementation.md: ML optimization plan

Code Patterns

• Use tf.tidy() for automatic tensor cleanup
• Lazy load heavy models on user interaction
• Expose unload() method for component unmount
• Manual chunks in Vite for model code splitting

Operational Constraints

• All TF.js operations MUST use tf.tidy() or dispose()
• Heavy models must expose unload() method
• Clean up on component unmount
• Monitor memory usage in production