添加系统设计
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# 机器学习平台系统设计
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## 0. 依赖库总览
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```python
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# 基础依赖
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import numpy as np
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import pandas as pd
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from typing import Dict, List
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from dataclasses import dataclass
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import uuid
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import json
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from datetime import datetime
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# 数据处理
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from sklearn.preprocessing import StandardScaler, OneHotEncoder
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from sklearn.impute import SimpleImputer
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from sklearn.feature_selection import SelectKBest, VarianceThreshold
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from sklearn.pipeline import Pipeline
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from sklearn.model_selection import train_test_split
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from sklearn.ensemble import IsolationForest
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# 机器学习框架
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import torch
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import torch.nn as nn
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from torch.utils.data import DataLoader
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import xgboost as xgb
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import lightgbm as lgb
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from catboost import CatBoostClassifier, CatBoostRegressor
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# 深度学习
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import pytorch_lightning as pl
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from transformers import AutoModel, AutoTokenizer
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from diffusers import StableDiffusionPipeline
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# 超参数优化
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import optuna
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from optuna.pruners import MedianPruner
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# 模型管理
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import mlflow
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from mlflow.tracking import MlflowClient
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# API框架
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from fastapi import FastAPI, BackgroundTasks, Depends
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from pydantic import BaseModel
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# 分布式训练
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from torch.nn.parallel import DistributedDataParallel
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from torch.distributed import init_process_group
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# 系统监控
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import psutil
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import GPUtil
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from prometheus_client import Counter, Gauge
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# 安全相关
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from cryptography.fernet import Fernet
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from passlib.hash import bcrypt
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# 任务队列
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from celery import Celery
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from redis import Redis
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# 日志管理
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import logging
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from tensorboard import SummaryWriter
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```
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## 1. 数据处理模块
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### 使用的库
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```python
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# 数据读取与处理
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import pandas as pd
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import numpy as np
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from sklearn.preprocessing import StandardScaler, OneHotEncoder, LabelEncoder
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from sklearn.impute import SimpleImputer
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from sklearn.feature_selection import SelectKBest, VarianceThreshold
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from sklearn.pipeline import Pipeline
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from sklearn.model_selection import train_test_split
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from sklearn.ensemble import IsolationForest
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# 数据版本控制
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import hashlib
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import json
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from datetime import datetime
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import uuid
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```
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### 1.1 数据读取
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1. 支持多种数据格式读取:
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```python
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SUPPORTED_FORMATS = {
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'csv': pd.read_csv,
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'parquet': pd.read_parquet,
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'hdf5': pd.read_hdf
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}
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```
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2. 数据读取参数配置:
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```python
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READ_CONFIG = {
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'csv': {
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'encoding': 'utf-8',
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'sep': ',',
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'na_values': ['', 'NULL', 'null']
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}
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}
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```
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### 1.2 数据预处理
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1. 数据清洗流水线:
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```python
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class DataCleaner:
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def __init__(self):
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self.pipeline = [
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('missing_handler', SimpleImputer(strategy='mean')),
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('outlier_detector', IsolationForest()),
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('duplicates_remover', DuplicatesRemover())
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]
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def clean(self, data):
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for name, processor in self.pipeline:
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data = processor.transform(data)
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return data
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```
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2. 数据转换:
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```python
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class DataTransformer:
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def __init__(self):
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self.numerical_pipeline = Pipeline([
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('scaler', StandardScaler()),
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('selector', VarianceThreshold(threshold=0.01))
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])
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self.categorical_pipeline = Pipeline([
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('encoder', OneHotEncoder(sparse=False)),
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('selector', SelectKBest(k=20))
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])
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```
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3. 特征工程:
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```python
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class FeatureEngineer:
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def __init__(self):
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self.feature_generators = {
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'polynomial': PolynomialFeatures(),
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'interaction': InteractionFeatures(),
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'time': TimeFeatures()
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}
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```
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4. 数据集划分:
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```python
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class DataSplitter:
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def split(self, data, test_size=0.2, val_size=0.1):
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train, test = train_test_split(data, test_size=test_size)
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train, val = train_test_split(train, test_size=val_size)
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return {
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'train': train,
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'val': val,
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'test': test
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}
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```
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### 1.3 数据版本控制
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```python
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class DataVersionControl:
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def __init__(self):
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self.version_db = {}
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def save_version(self, data, metadata):
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version_id = str(uuid.uuid4())
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hash_value = self._calculate_hash(data)
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self.version_db[version_id] = {
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'hash': hash_value,
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'timestamp': datetime.now(),
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'metadata': metadata
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}
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return version_id
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```
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## 2. 模型训练模块
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### 使用的库
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```python
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# 机器学习框架
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import torch
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import torch.nn as nn
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import torch.optim as optim
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from torch.utils.data import DataLoader
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import pytorch_lightning as pl
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# 传统机器学习算法
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import xgboost as xgb
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import lightgbm as lgb
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from catboost import CatBoostClassifier
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from sklearn.ensemble import RandomForestClassifier
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# 深度学习模型
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from transformers import AutoModel, AutoTokenizer
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from diffusers import StableDiffusionPipeline
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# 超参数优化
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import optuna
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from optuna.pruners import MedianPruner
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```
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### 2.1 算法管理
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1. 算法注册表:
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```python
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ALGORITHMS = {
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'classification': {
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'xgboost': {
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'class': XGBClassifier,
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'params': {
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'max_depth': {'type': 'int', 'range': [3,10]},
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'learning_rate': {'type': 'float', 'range': [0.001,0.1]},
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'n_estimators': {'type': 'int', 'range': [100,1000]}
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}
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},
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'lightgbm': {...},
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'catboost': {...}
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},
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'regression': {
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'xgboost_regressor': {...},
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'lightgbm_regressor': {...}
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},
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'clustering': {
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'kmeans': {...},
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'dbscan': {...}
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}
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}
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```
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2. 模型工厂:
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```python
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class ModelFactory:
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@staticmethod
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def create_model(algorithm_name, params=None):
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if algorithm_name not in ALGORITHMS:
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raise ValueError(f"Algorithm {algorithm_name} not supported")
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algorithm = ALGORITHMS[algorithm_name]
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return algorithm['class'](**params if params else {})
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```
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### 2.2 训练管理
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1. 训练配置:
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```python
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@dataclass
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class TrainingConfig:
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algorithm: str
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params: Dict
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data_version: str
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device: str = 'cuda'
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batch_size: int = 32
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num_epochs: int = 100
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early_stopping: bool = True
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```
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2. 训练器:
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```python
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class ModelTrainer:
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def __init__(self, config: TrainingConfig):
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self.config = config
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self.model = ModelFactory.create_model(config.algorithm, config.params)
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self.device = torch.device(config.device)
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def train(self, train_data, val_data=None):
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self.model.to(self.device)
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optimizer = self._configure_optimizer()
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scheduler = self._configure_scheduler()
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for epoch in range(self.config.num_epochs):
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train_loss = self._train_epoch(train_data)
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if val_data is not None:
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val_loss = self._validate(val_data)
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if self.early_stopping.should_stop():
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break
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```
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### 2.3 超参数优化
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```python
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class HPOptimizer:
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def __init__(self, study_name, n_trials=100):
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self.study = optuna.create_study(
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study_name=study_name,
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direction='minimize'
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)
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self.n_trials = n_trials
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def optimize(self, objective_fn):
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self.study.optimize(
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objective_fn,
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n_trials=self.n_trials,
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callbacks=[self._pruning_callback]
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)
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return self.study.best_params
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```
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## 3. 模型管理模块
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### 使用的库
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```python
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# 模型管理与追踪
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import mlflow
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from mlflow.tracking import MlflowClient
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from mlflow.models import Model
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# 模型序列化
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import pickle
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import torch.serialization
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import json
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# 模型版本控制
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from datetime import datetime
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import hashlib
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```
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### 3.1 模型注册
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```python
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class ModelRegistry:
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def __init__(self, mlflow_uri):
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self.client = MlflowClient(mlflow_uri)
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def register_model(self, model_path, name, metadata=None):
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model_version = self.client.create_model_version(
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name=name,
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source=model_path,
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run_id=mlflow.active_run().info.run_id
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)
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if metadata:
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self.client.set_model_version_tag(
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name=name,
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version=model_version.version,
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key='metadata',
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value=json.dumps(metadata)
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)
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```
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### 3.2 模型加载
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```python
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class ModelLoader:
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@staticmethod
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def load_model(model_id):
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model_path = f"models/{model_id}"
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return torch.load(model_path)
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@staticmethod
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def load_for_inference(model_id):
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model = ModelLoader.load_model(model_id)
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model.eval()
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return model
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```
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### 3.3 模型服务
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```python
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class ModelService:
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def __init__(self):
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self.loaded_models = {}
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async def predict(self, model_id, data):
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if model_id not in self.loaded_models:
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self.loaded_models[model_id] = ModelLoader.load_for_inference(model_id)
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model = self.loaded_models[model_id]
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with torch.no_grad():
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predictions = model(torch.tensor(data))
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return predictions.numpy()
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```
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## 4. 系统监控模块
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### 使用的库
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```python
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# 系统监控
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import psutil
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import GPUtil
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from prometheus_client import Counter, Gauge
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# 日志管理
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import logging
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from tensorboard import SummaryWriter
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# 性能分析
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import cProfile
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import line_profiler
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import memory_profiler
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```
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### 4.1 资源监控
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```python
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class ResourceMonitor:
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def __init__(self):
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self.gpu_util = GPUUtilization()
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self.memory_util = MemoryUtilization()
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def get_metrics(self):
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return {
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'gpu': self.gpu_util.get_usage(),
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'memory': self.memory_util.get_usage(),
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'cpu': psutil.cpu_percent()
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}
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```
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### 4.2 训练监控
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```python
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class TrainingMonitor:
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def __init__(self):
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self.metrics_history = defaultdict(list)
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def log_metrics(self, metrics):
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for name, value in metrics.items():
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self.metrics_history[name].append(value)
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def get_summary(self):
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return {
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name: {
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'current': values[-1],
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'mean': np.mean(values),
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'std': np.std(values)
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}
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for name, values in self.metrics_history.items()
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}
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```
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## 5. API接口设计
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### 使用的库
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```python
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# Web框架
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from fastapi import FastAPI, BackgroundTasks, Depends, HTTPException
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from fastapi.security import OAuth2PasswordBearer
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from pydantic import BaseModel, validator
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# 异步支持
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import asyncio
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from concurrent.futures import ThreadPoolExecutor
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# API文档
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from fastapi.openapi.utils import get_openapi
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```
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### 5.1 训练接口
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```python
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@router.post("/train")
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async def start_training(
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request: TrainingRequest,
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background_tasks: BackgroundTasks
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):
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task_id = str(uuid.uuid4())
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background_tasks.add_task(
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train_model,
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task_id=task_id,
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config=request.dict()
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)
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return {"task_id": task_id}
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```
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### 5.2 预测接口
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```python
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@router.post("/predict")
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async def predict(
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request: PredictionRequest,
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model_service: ModelService = Depends()
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):
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predictions = await model_service.predict(
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model_id=request.model_id,
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data=request.data
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)
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return {"predictions": predictions.tolist()}
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```
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## 6. 安全模块
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### 使用的库
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```python
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# 加密
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from cryptography.fernet import Fernet
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from passlib.hash import bcrypt
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from jwt import encode, decode
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# 访问控制
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from fastapi.security import OAuth2PasswordBearer
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from jose import JWTError, jwt
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# 数据脱敏
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import re
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from typing import Pattern
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```
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### 6.1 数据安全
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```python
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class DataSecurity:
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def __init__(self):
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self.encryptor = DataEncryptor()
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self.sanitizer = DataSanitizer()
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def secure_save(self, data, path):
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sanitized_data = self.sanitizer.clean(data)
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encrypted_data = self.encryptor.encrypt(sanitized_data)
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self._save_to_disk(encrypted_data, path)
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```
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### 6.2 模型安全
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```python
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class ModelSecurity:
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def __init__(self):
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self.access_control = AccessControl()
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def check_permission(self, user_id, model_id, operation):
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return self.access_control.has_permission(user_id, model_id, operation)
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def encrypt_model(self, model, key):
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return AESEncryption.encrypt(model.state_dict(), key)
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```
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## 7. 部署配置
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### 使用的库
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```python
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# 容器化
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import docker
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from docker.types import Mount
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# 任务队列
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from celery import Celery
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from redis import Redis
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# 配置管理
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import yaml
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from dynaconf import Dynaconf
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# 环境管理
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import os
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from dotenv import load_dotenv
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```
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### 7.1 Docker配置
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```yaml
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version: '3'
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services:
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api:
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build: .
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ports:
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- "8000:8000"
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environment:
|
||||
- CUDA_VISIBLE_DEVICES=0
|
||||
volumes:
|
||||
- ./models:/app/models
|
||||
|
||||
worker:
|
||||
build: .
|
||||
command: celery -A tasks worker
|
||||
environment:
|
||||
- CUDA_VISIBLE_DEVICES=1
|
||||
```
|
||||
|
||||
### 7.2 资源限制
|
||||
```python
|
||||
class ResourceLimiter:
|
||||
def __init__(self):
|
||||
self.limits = {
|
||||
'max_data_size': 1024 * 1024 * 1024, # 1GB
|
||||
'max_training_time': 3600, # 1 hour
|
||||
'max_batch_size': 1024
|
||||
}
|
||||
|
||||
def check_limits(self, config):
|
||||
if config.data_size > self.limits['max_data_size']:
|
||||
raise ValueError("Data size exceeds limit")
|
||||
```
|
||||
|
||||
## 8. 版本信息
|
||||
```python
|
||||
DEPENDENCIES = {
|
||||
# 基础依赖
|
||||
'numpy': '>=1.21.0',
|
||||
'pandas': '>=1.3.0',
|
||||
'scikit-learn': '>=1.0.0',
|
||||
|
||||
# 机器学习框架
|
||||
'torch': '>=2.0.0',
|
||||
'xgboost': '>=1.5.0',
|
||||
'lightgbm': '>=3.3.0',
|
||||
'catboost': '>=1.0.0',
|
||||
|
||||
# 深度学习
|
||||
'pytorch-lightning': '>=2.0.0',
|
||||
'transformers': '>=4.30.0',
|
||||
'diffusers': '>=0.19.0',
|
||||
|
||||
# 模型管理
|
||||
'mlflow': '>=2.4.0',
|
||||
'optuna': '>=3.2.0',
|
||||
|
||||
# Web框架
|
||||
'fastapi': '>=0.95.0',
|
||||
'celery': '>=5.3.0',
|
||||
|
||||
# 监控与日志
|
||||
'prometheus-client': '>=0.16.0',
|
||||
'tensorboard': '>=2.12.0',
|
||||
|
||||
# 安全
|
||||
'cryptography': '>=40.0.0',
|
||||
'passlib': '>=1.7.4',
|
||||
|
||||
# 部署
|
||||
'docker': '>=6.1.0',
|
||||
'redis': '>=4.5.0'
|
||||
}
|
||||
```
|
||||
Loading…
Reference in New Issue
Block a user