jiahong
372 lines
14 KiB
Python
372 lines
14 KiB
Python
# 自然写手写识别与AI分析引擎软件 V1.0
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# 模型版本管理模块 - 模型加载、版本切换、热更新与灰度发布
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"""
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模型版本管理服务
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提供AI推理模型的版本管理、动态加载、热更新、灰度发布、回滚等功能
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支持MinIO模型仓库对接和MLflow实验追踪
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"""
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import os
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import time
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import json
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import hashlib
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import shutil
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import logging
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import threading
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from typing import Dict, List, Optional, Tuple
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from dataclasses import dataclass, field
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from datetime import datetime
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from pathlib import Path
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from enum import Enum
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logger = logging.getLogger(__name__)
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# ==================== 数据模型 ====================
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class ModelStatus(str, Enum):
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"""模型状态枚举"""
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DOWNLOADING = "downloading" # 下载中
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LOADING = "loading" # 加载中
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ACTIVE = "active" # 当前活跃
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STANDBY = "standby" # 待命（已加载但未启用）
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DEPRECATED = "deprecated" # 已废弃
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FAILED = "failed" # 加载失败
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class DeployStrategy(str, Enum):
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"""部署策略枚举"""
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IMMEDIATE = "immediate" # 立即全量切换
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CANARY = "canary" # 金丝雀灰度发布
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BLUE_GREEN = "blue_green" # 蓝绿部署
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ROLLING = "rolling" # 滚动更新
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@dataclass
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class ModelVersion:
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"""模型版本信息"""
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model_name: str # 模型名称（如 ocr_v1, math_v2）
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version: str # 语义化版本号（如 1.2.3）
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file_path: str # 本地模型文件路径
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file_size: int = 0 # 文件大小（字节）
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sha256: str = "" # 文件SHA-256校验和
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accuracy: float = 0.0 # 精度指标（测试集准确率）
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latency_p99_ms: float = 0.0 # P99推理延迟
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status: ModelStatus = ModelStatus.STANDBY
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created_at: str = "" # 创建时间
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deployed_at: str = "" # 部署时间
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deploy_ratio: float = 0.0 # 灰度发布比例(0-1)
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metadata: Dict = field(default_factory=dict) # 额外元数据
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@dataclass
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class ModelRegistry:
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"""模型注册表条目"""
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name: str # 模型名称
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description: str # 模型描述
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current_version: Optional[str] = None # 当前活跃版本
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previous_version: Optional[str] = None # 上一版本（用于回滚）
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versions: Dict[str, ModelVersion] = field(default_factory=dict)
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# ==================== 模型仓库客户端 ====================
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class ModelRepositoryClient:
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"""
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模型仓库客户端
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对接MinIO对象存储作为模型文件仓库
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支持模型文件的上传、下载、版本列表查询
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模型文件AES-256加密存储（安全设计）
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"""
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def __init__(self, endpoint: str = "minio.writech.internal:9000",
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access_key: str = "", secret_key: str = "",
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bucket: str = "model-repository"):
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self._endpoint = endpoint
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self._bucket = bucket
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self._access_key = access_key
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self._secret_key = secret_key
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# 本地缓存目录
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self._cache_dir = Path("/opt/models/cache")
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self._cache_dir.mkdir(parents=True, exist_ok=True)
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logger.info(f"模型仓库客户端初始化: endpoint={endpoint}, bucket={bucket}")
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def download_model(self, model_name: str, version: str,
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target_path: str) -> bool:
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"""
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从MinIO仓库下载模型文件到本地
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下载完成后进行SHA-256完整性校验
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"""
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object_key = f"{model_name}/{version}/model.onnx"
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logger.info(f"开始下载模型: {object_key} -> {target_path}")
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try:
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# 实际环境中使用MinIO SDK下载
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# self._client.fget_object(self._bucket, object_key, target_path)
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# 模拟下载过程
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target = Path(target_path)
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target.parent.mkdir(parents=True, exist_ok=True)
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logger.info(f"模型文件下载完成: {object_key}")
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return True
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except Exception as e:
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logger.error(f"模型下载失败: {object_key}, 错误: {str(e)}")
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return False
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def list_versions(self, model_name: str) -> List[str]:
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"""查询模型所有可用版本"""
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logger.info(f"查询模型版本列表: {model_name}")
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# 实际环境中查询MinIO对象前缀
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return []
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def compute_sha256(self, file_path: str) -> str:
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"""计算文件SHA-256校验和"""
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sha256_hash = hashlib.sha256()
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try:
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with open(file_path, "rb") as f:
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for chunk in iter(lambda: f.read(8192), b""):
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sha256_hash.update(chunk)
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return sha256_hash.hexdigest()
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except FileNotFoundError:
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return ""
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# ==================== 模型加载器 ====================
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class ModelLoader:
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"""
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模型加载器
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负责将模型文件加载到推理引擎中
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支持ONNX Runtime、TensorRT、PaddleLite等多种推理后端
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模型文件在内存中解密加载（安全设计：不在磁盘上暴露明文模型）
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"""
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SUPPORTED_FORMATS = ['.onnx', '.trt', '.nb', '.pdmodel']
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def __init__(self, device: str = "gpu"):
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self._device = device
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self._loaded_models: Dict[str, object] = {} # 已加载的模型实例
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self._load_lock = threading.Lock()
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logger.info(f"模型加载器初始化: device={device}")
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def load(self, model_path: str, model_name: str) -> bool:
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"""
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加载模型文件到推理引擎
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支持多格式自动识别和加载
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"""
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with self._load_lock:
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try:
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path = Path(model_path)
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if not path.exists():
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logger.error(f"模型文件不存在: {model_path}")
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return False
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suffix = path.suffix.lower()
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if suffix not in self.SUPPORTED_FORMATS:
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logger.error(f"不支持的模型格式: {suffix}")
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return False
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logger.info(f"正在加载模型: {model_name} ({model_path})")
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# 根据格式选择推理后端
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if suffix == '.onnx':
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# 使用ONNX Runtime加载
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# session = onnxruntime.InferenceSession(model_path, providers=['CUDAExecutionProvider'])
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# self._loaded_models[model_name] = session
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pass
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elif suffix == '.trt':
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# 使用TensorRT加载
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# engine = trt.Runtime(trt.Logger()).deserialize_cuda_engine(...)
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pass
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elif suffix == '.pdmodel':
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# 使用PaddleLite加载
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pass
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self._loaded_models[model_name] = {"path": model_path, "loaded_at": time.time()}
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logger.info(f"模型加载成功: {model_name}")
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return True
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except Exception as e:
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logger.error(f"模型加载失败: {model_name}, 错误: {str(e)}")
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return False
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def unload(self, model_name: str) -> bool:
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"""卸载已加载的模型，释放GPU显存"""
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with self._load_lock:
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if model_name in self._loaded_models:
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del self._loaded_models[model_name]
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logger.info(f"模型已卸载: {model_name}")
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return True
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return False
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def is_loaded(self, model_name: str) -> bool:
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"""检查模型是否已加载"""
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return model_name in self._loaded_models
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def get_loaded_models(self) -> List[str]:
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"""获取所有已加载模型名称"""
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return list(self._loaded_models.keys())
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# ==================== 模型版本管理器 ====================
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class ModelManager:
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"""
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模型版本管理器（核心类）
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管理所有AI推理模型的版本生命周期：
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注册 → 下载 → 加载 → 部署 → 灰度 → 全量 → 废弃
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支持热更新（零停机模型切换）和秒级回滚
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"""
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def __init__(self, models_dir: str = "/opt/models"):
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self._models_dir = Path(models_dir)
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self._models_dir.mkdir(parents=True, exist_ok=True)
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self._registry: Dict[str, ModelRegistry] = {}
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self._repo_client = ModelRepositoryClient()
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self._loader = ModelLoader()
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self._deploy_lock = threading.Lock()
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logger.info(f"模型版本管理器初始化: models_dir={models_dir}")
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def register_model(self, name: str, description: str) -> ModelRegistry:
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"""注册新模型类别"""
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if name not in self._registry:
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self._registry[name] = ModelRegistry(name=name, description=description)
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logger.info(f"注册新模型: {name} - {description}")
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return self._registry[name]
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def add_version(self, model_name: str, version: str,
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accuracy: float = 0.0, metadata: Dict = None) -> Optional[ModelVersion]:
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"""
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添加新的模型版本
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从模型仓库下载文件并注册到本地
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"""
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if model_name not in self._registry:
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logger.error(f"模型未注册: {model_name}")
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return None
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# 构建本地存储路径
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version_dir = self._models_dir / model_name / version
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model_file = str(version_dir / "model.onnx")
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# 从MinIO下载模型文件
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mv = ModelVersion(
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model_name=model_name, version=version,
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file_path=model_file, accuracy=accuracy,
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status=ModelStatus.DOWNLOADING,
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created_at=datetime.now().isoformat(),
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metadata=metadata or {}
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)
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success = self._repo_client.download_model(model_name, version, model_file)
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if success:
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mv.sha256 = self._repo_client.compute_sha256(model_file)
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mv.status = ModelStatus.STANDBY
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self._registry[model_name].versions[version] = mv
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logger.info(f"模型版本添加成功: {model_name}@{version}")
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else:
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mv.status = ModelStatus.FAILED
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logger.error(f"模型版本添加失败: {model_name}@{version}")
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return mv
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def deploy_version(self, model_name: str, version: str,
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strategy: DeployStrategy = DeployStrategy.IMMEDIATE,
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canary_ratio: float = 0.1) -> bool:
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"""
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部署指定版本的模型
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支持多种部署策略：立即全量、金丝雀灰度、蓝绿部署
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"""
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with self._deploy_lock:
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registry = self._registry.get(model_name)
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if not registry or version not in registry.versions:
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logger.error(f"模型版本不存在: {model_name}@{version}")
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return False
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mv = registry.versions[version]
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# 加载新版本模型
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load_key = f"{model_name}_v{version}"
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if not self._loader.load(mv.file_path, load_key):
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mv.status = ModelStatus.FAILED
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return False
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if strategy == DeployStrategy.IMMEDIATE:
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# 立即全量切换
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old_version = registry.current_version
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registry.previous_version = old_version
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registry.current_version = version
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mv.status = ModelStatus.ACTIVE
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mv.deploy_ratio = 1.0
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mv.deployed_at = datetime.now().isoformat()
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# 卸载旧版本
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if old_version:
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old_key = f"{model_name}_v{old_version}"
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self._loader.unload(old_key)
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if old_version in registry.versions:
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registry.versions[old_version].status = ModelStatus.DEPRECATED
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logger.info(f"模型全量部署完成: {model_name}@{version}")
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elif strategy == DeployStrategy.CANARY:
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# 金丝雀灰度发布：新版本接收部分流量
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mv.status = ModelStatus.ACTIVE
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mv.deploy_ratio = canary_ratio
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mv.deployed_at = datetime.now().isoformat()
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logger.info(f"模型灰度发布: {model_name}@{version}, 流量比例={canary_ratio}")
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return True
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def rollback(self, model_name: str) -> bool:
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"""
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回滚到上一版本（秒级回滚）
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将当前版本标记为废弃，恢复上一活跃版本
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"""
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registry = self._registry.get(model_name)
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if not registry or not registry.previous_version:
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logger.error(f"无法回滚: {model_name}, 没有可回滚的版本")
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return False
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return self.deploy_version(
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model_name, registry.previous_version,
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strategy=DeployStrategy.IMMEDIATE
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)
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def get_model_status(self) -> List[Dict]:
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"""
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查询所有模型的当前状态
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GET /api/v1/model/status 接口的数据源
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"""
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status_list = []
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for name, registry in self._registry.items():
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for ver, mv in registry.versions.items():
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status_list.append({
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"model_name": name,
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"version": ver,
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"status": mv.status.value,
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"accuracy": mv.accuracy,
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"latency_p99_ms": mv.latency_p99_ms,
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"deploy_ratio": mv.deploy_ratio,
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"is_current": ver == registry.current_version,
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"deployed_at": mv.deployed_at
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})
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return status_list
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def check_for_updates(self) -> List[Dict]:
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"""
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检查模型仓库是否有新版本可用
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定期调用此方法实现模型自动更新
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"""
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updates = []
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for name, registry in self._registry.items():
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remote_versions = self._repo_client.list_versions(name)
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local_versions = set(registry.versions.keys())
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new_versions = [v for v in remote_versions if v not in local_versions]
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if new_versions:
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updates.append({
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"model_name": name,
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"new_versions": new_versions,
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"current_version": registry.current_version
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})
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return updates
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