⚡ Bolt: ML予測のパフォーマンス改善 (LruCache & オーバーヘッド削減)

.jules/bolt.md

@@ -9,3 +9,7 @@
 ## 2026-06-10 - [Lazy Loading Scikit-learn & NumPy]
 **Learning:** Top-level imports of `numpy` and `joblib` plus loading a serialized model file (`.joblib`) during FastAPI startup adds significant overhead (e.g., ~3 seconds). Refactoring this into a service with `cached_property` and local imports deferred the cost until the first request.
 **Action:** Move all heavy ML model loading and their dependencies into lazy-loaded properties to ensure near-instant application startup.
+
+## 2026-06-11 - [ML Service Hot-Path Optimization]
+**Learning:** Applying `lru_cache` to an instance method can lead to memory leaks as the cache holds a strong reference to `self`. Using a module-level function for caching instead is safer. Additionally, for single-sample predictions, passing a list containing a tuple (e.g., `model.predict([features_tuple])`) to Scikit-learn is faster than creating and reshaping a Numpy array, especially when combined with removing local `numpy` imports.
+**Action:** Use module-level functions for caching to avoid instance leaks, and minimize object creation/conversion (like Numpy array initialization) in high-frequency prediction loops.

ml_service.py

@@ -1,8 +1,20 @@
-from functools import cached_property
+from functools import cached_property, lru_cache
 import logging
 
 logger = logging.getLogger(__name__)
 
+@lru_cache(maxsize=128)
+def _get_cached_prediction(model, features_tuple):
+    """
+    Internal cached prediction function.
+    Taking model as an argument and being top-level avoids the lru_cache
+    memory leak associated with instance methods.
+    """
+    # Scikit-learn can accept a list containing a tuple.
+    # This avoids converting the tuple back to a list in the hot path.
+    prediction = model.predict([features_tuple])
+    return int(prediction[0])
+
 class MLService:
     @cached_property
     def model(self):
@@ -16,11 +28,10 @@ def model(self):
             return None
 
     def predict(self, features_list):
-        """Perform prediction using the lazy-loaded model."""
+        """Perform prediction with caching and low overhead."""
         if self.model is None:
             raise RuntimeError("ML model could not be loaded")
 
-        import numpy as np
-        features = np.array(features_list).reshape(1, -1)
-        prediction = self.model.predict(features)
-        return int(prediction[0])
+        # Convert list to tuple for hashability in lru_cache
+        features_tuple = tuple(features_list)
+        return _get_cached_prediction(self.model, features_tuple)