AI 与存储结合：智能存储实践与挑战 | 极客日志

AI 与存储结合：智能存储实践与挑战 | 极客日志

# I/O 预测模型训练
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error

# 加载数据
data = pd.read_csv('storage_io_data.csv')

# 特征和标签
X = data[['time', 'io_size', 'io_type', 'file_size', 'file_type']]
y = data['response_time']

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

# 模型训练
model = RandomForestRegressor(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# 模型评估
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print(f'Mean Squared Error: {mse}')

# 模型部署
import joblib
joblib.dump(model, 'io_prediction_model.pkl')

# 实时预测
def predict_io_response_time(model, io_data):
    # 预处理输入数据
    # ...
    # 预测响应时间
    prediction = model.predict(io_data)
    return prediction