feat: enhance train_model.py to dynamically determine CPU count for parallel processing

- Added a new function to accurately retrieve the number of allocated CPUs in containerized environments, improving parallel processing efficiency.
- Updated the dataset generation function to utilize the new CPU count function, ensuring optimal resource usage during model training.

Made-with: Cursor

scripts/train_model.py

@@ -8,7 +8,7 @@ sys.path.insert(0, str(Path(__file__).parent.parent))
 
 import argparse
 import json
-import os
+import math
 from datetime import datetime
 from multiprocessing import Pool, cpu_count
 from pathlib import Path
@@ -23,6 +23,35 @@ from src.indicators import Indicators
 from src.ml_features import build_features, FEATURE_COLS
 from src.label_builder import build_labels
 
+def _cgroup_cpu_count() -> int:
+    """cgroup v1/v2 쿼터를 읽어 실제 할당된 CPU 수를 반환한다.
+    LXC/컨테이너 환경에서 cpu_count()가 호스트 전체 코어를 반환하는 문제를 방지한다.
+    쿼터를 읽을 수 없으면 cpu_count()를 그대로 사용한다.
+    """
+    # cgroup v2
+    try:
+        quota_path = Path("/sys/fs/cgroup/cpu.max")
+        if quota_path.exists():
+            parts = quota_path.read_text().split()
+            if parts[0] != "max":
+                quota = int(parts[0])
+                period = int(parts[1])
+                return max(1, math.floor(quota / period))
+    except Exception:
+        pass
+
+    # cgroup v1
+    try:
+        quota = int(Path("/sys/fs/cgroup/cpu/cpu.cfs_quota_us").read_text())
+        period = int(Path("/sys/fs/cgroup/cpu/cpu.cfs_period_us").read_text())
+        if quota > 0:
+            return max(1, math.floor(quota / period))
+    except Exception:
+        pass
+
+    return cpu_count()
+
+
 LOOKAHEAD = 60
 ATR_SL_MULT = 1.5
 ATR_TP_MULT = 3.0
@@ -78,7 +107,7 @@ def generate_dataset(df: pd.DataFrame, n_jobs: int | None = None) -> pd.DataFram
     total = len(df)
     indices = range(60, total - LOOKAHEAD)
 
-    workers = n_jobs or max(1, cpu_count() - 1)
+    workers = n_jobs or max(1, _cgroup_cpu_count() - 1)
     print(f"  병렬 처리: {workers}코어 사용 (총 {len(indices):,}개 인덱스)")
 
     # DataFrame을 numpy로 변환해서 worker 간 전달 비용 최소화