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chrislu 814e0bb233 Phase 4: Revolutionary Recipe-Based ML Optimization Engine 
🚀 Transform SeaweedFS ML optimizations from hard-coded framework-specific code
to a flexible, configuration-driven system using YAML/JSON rules and templates.

## Key Innovations:
- Rule-based optimization engine with conditions and actions
- Plugin system for framework detection (PyTorch, TensorFlow)
- Configuration manager with YAML/JSON support
- Adaptive learning from usage patterns
- Template-based optimization recipes

## New Components:
- optimization_engine.go: Core rule evaluation and application
- config_manager.go: Configuration loading and validation
- plugins/pytorch_plugin.go: PyTorch-specific optimizations
- plugins/tensorflow_plugin.go: TensorFlow-specific optimizations
- examples/: Sample configuration files and documentation

## Benefits:
- Zero-code customization through configuration files
- Support for any ML framework via plugins
- Intelligent adaptation based on workload patterns
- Production-ready with comprehensive error handling
- Backward compatible with existing optimizations

This replaces hard-coded optimization logic with a flexible system that can
adapt to new frameworks and workload patterns without code changes.
2025-08-30 16:49:12 -07:00
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custom_ml_optimization.yaml Phase 4: Revolutionary Recipe-Based ML Optimization Engine 2025-08-30 16:49:12 -07:00
pytorch_optimized.yaml Phase 4: Revolutionary Recipe-Based ML Optimization Engine 2025-08-30 16:49:12 -07:00