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- Export LightGBM and XGBoost models to ONNX format (332 features each) - Update Dockerfile with ONNX Runtime 1.19.2 for hot-path inference - Add ML_ENABLED and ONNX_MODEL_DIR env vars to render.yaml - Create export_onnx.py script for model conversion - Fix XGBoost feature naming for onnxmltools compatibility Models exported: - lightgbm_model.onnx (2.3MB) - xgboost_model.onnx (2.1MB) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- nsmi.rs: Use .clamp(2, 5) instead of .max(2).min(5) - nsmi.rs: Use iterator zip pattern instead of index loops - ml_inference.rs: Rename ModelType variants (LSTM->Lstm, CNN->Cnn, etc.) - Add #[allow(dead_code)] for modules not yet integrated - Add #[allow(unused_imports)] for conditional exports All fixes maintain identical runtime behavior while satisfying Clippy warnings-as-errors (-D warnings) in CI. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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Models exported: