# 分布式训练示例
import torch
from torch.nn.parallel import DistributedDataParallel as DDP

def train_model():
    # 初始化进程组
    torch.distributed.init_process_group(backend='nccl')
    
    # 创建模型
    model = MyModel().to(device)
    model = DDP(model, device_ids=[local_rank])
    
    # 优化器配置
    optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4)
    
    # 训练循环
    for epoch in range(epochs):
        for batch in dataloader:
            outputs = model(batch)
            loss = compute_loss(outputs)
            loss.backward()
            optimizer.step()

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

# 数据加载
data = pd.read_csv('dataset.csv')

# 数据清洗
data = data.dropna()
data = remove_outliers(data)

# 特征工程
X = data.drop('target', axis=1)
y = data['target']

# 数据分割
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# 数据标准化
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report

# 模型训练
model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)

# 模型评估
y_pred = model.predict(X_test)
print(classification_report(y_test, y_pred))

# 模型保存
import joblib
joblib.dump(model, 'model.pkl')