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