Files
OCR/backend/app/main.py
egg 7536f43513 feat: implement GPU acceleration support for OCR processing
實作 GPU 加速支援,自動偵測並啟用 CUDA GPU 加速 OCR 處理

主要變更:

1. 環境設置增強 (setup_dev_env.sh)
   - 新增 GPU 和 CUDA 版本偵測功能
   - 自動安裝對應的 PaddlePaddle GPU/CPU 版本
   - CUDA 11.2+ 安裝 GPU 版本,否則安裝 CPU 版本
   - 安裝後驗證 GPU 可用性並顯示設備資訊

2. 配置更新
   - .env.local: 加入 GPU 配置選項
     * FORCE_CPU_MODE: 強制 CPU 模式選項
     * GPU_MEMORY_FRACTION: GPU 記憶體使用比例
     * GPU_DEVICE_ID: GPU 裝置 ID
   - backend/app/core/config.py: 加入 GPU 配置欄位

3. OCR 服務 GPU 整合 (backend/app/services/ocr_service.py)
   - 新增 _detect_and_configure_gpu() 方法自動偵測 GPU
   - 新增 get_gpu_status() 方法回報 GPU 狀態和記憶體使用
   - 修改 get_ocr_engine() 支援 GPU 參數和錯誤降級
   - 修改 get_structure_engine() 支援 GPU 參數和錯誤降級
   - 自動 GPU/CPU 切換,GPU 失敗時自動降級到 CPU

4. 健康檢查與監控 (backend/app/main.py)
   - /health endpoint 加入 GPU 狀態資訊
   - 回報 GPU 可用性、裝置名稱、記憶體使用等資訊

5. 文檔更新 (README.md)
   - Features: 加入 GPU 加速功能說明
   - Prerequisites: 加入 GPU 硬體要求(可選)
   - Quick Start: 更新自動化設置說明包含 GPU 偵測
   - Configuration: 加入 GPU 配置選項和說明
   - Notes: 加入 GPU 支援注意事項

技術特性:
- 自動偵測 NVIDIA GPU 和 CUDA 版本
- 支援 CUDA 11.2-12.x
- GPU 初始化失敗時優雅降級到 CPU
- GPU 記憶體分配控制防止 OOM
- 即時 GPU 狀態監控和報告
- 完全向後相容 CPU-only 環境

預期效能:
- GPU 系統: 3-10x OCR 處理速度提升
- CPU 系統: 無影響,維持現有效能

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-14 07:42:13 +08:00

163 lines
4.4 KiB
Python

"""
Tool_OCR - FastAPI Application Entry Point
Main application setup with CORS, routes, and startup/shutdown events
"""
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from contextlib import asynccontextmanager
import logging
import asyncio
from pathlib import Path
from app.core.config import settings
from app.services.background_tasks import task_manager
# Ensure log directory exists before configuring logging
Path(settings.log_file).parent.mkdir(parents=True, exist_ok=True)
# Configure logging
logging.basicConfig(
level=getattr(logging, settings.log_level),
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
handlers=[
logging.FileHandler(settings.log_file),
logging.StreamHandler(),
],
)
logger = logging.getLogger(__name__)
@asynccontextmanager
async def lifespan(app: FastAPI):
"""Application lifespan events"""
# Startup
logger.info("Starting Tool_OCR application...")
# Ensure all directories exist
settings.ensure_directories()
logger.info("All directories created/verified")
# Start cleanup scheduler as background task
cleanup_task = asyncio.create_task(task_manager.start_cleanup_scheduler())
logger.info("Started cleanup scheduler for expired files")
# TODO: Initialize database connection pool
# TODO: Load PaddleOCR models
logger.info("Application startup complete")
yield
# Shutdown
logger.info("Shutting down Tool_OCR application...")
# Cancel cleanup task
cleanup_task.cancel()
try:
await cleanup_task
except asyncio.CancelledError:
logger.info("Cleanup scheduler stopped")
# TODO: Close database connections
# Create FastAPI application
app = FastAPI(
title="Tool_OCR",
description="OCR Batch Processing System with Structure Extraction",
version="0.1.0",
lifespan=lifespan,
)
# Configure CORS
app.add_middleware(
CORSMiddleware,
allow_origins=settings.cors_origins_list,
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# Health check endpoint
@app.get("/health")
async def health_check():
"""Health check endpoint with GPU status"""
from app.services.ocr_service import OCRService
response = {
"status": "healthy",
"service": "Tool_OCR",
"version": "0.1.0",
}
# Add GPU status information
try:
# Create temporary OCRService instance to get GPU status
# In production, this should be a singleton service
ocr_service = OCRService()
gpu_status = ocr_service.get_gpu_status()
response["gpu"] = {
"available": gpu_status.get("gpu_available", False),
"enabled": gpu_status.get("gpu_enabled", False),
"device_name": gpu_status.get("device_name", "N/A"),
"device_count": gpu_status.get("device_count", 0),
"compute_capability": gpu_status.get("compute_capability", "N/A"),
}
# Add memory info if available
if gpu_status.get("memory_total_mb"):
response["gpu"]["memory"] = {
"total_mb": round(gpu_status.get("memory_total_mb", 0), 2),
"allocated_mb": round(gpu_status.get("memory_allocated_mb", 0), 2),
"utilization_percent": round(gpu_status.get("memory_utilization", 0), 2),
}
# Add reason if GPU is not available
if not gpu_status.get("gpu_available") and gpu_status.get("reason"):
response["gpu"]["reason"] = gpu_status.get("reason")
except Exception as e:
logger.warning(f"Failed to get GPU status: {e}")
response["gpu"] = {
"available": False,
"error": str(e),
}
return response
# Root endpoint
@app.get("/")
async def root():
"""Root endpoint with API information"""
return {
"message": "Tool_OCR API",
"version": "0.1.0",
"docs_url": "/docs",
"health_check": "/health",
}
# Include API routers
from app.routers import auth, ocr, export, translation
app.include_router(auth.router)
app.include_router(ocr.router)
app.include_router(export.router)
app.include_router(translation.router) # RESERVED for Phase 5
if __name__ == "__main__":
import uvicorn
uvicorn.run(
"app.main:app",
host="0.0.0.0",
port=settings.backend_port,
reload=True,
log_level=settings.log_level.lower(),
)