mlflow将模型数据保存至外部存储

安装：

mlflow
boto3

设置环境变量：

AWS_ACCESS_KEY_ID=admin
AWS_SECRET_ACCESS_KEY=admin
MLFLOW_S3_ENDPOINT_URL=http://192.168.5.108:9000
MLFLOW_TRACKING_URI=http://192.168.5.108:15000/

例子：

import mlflow.sklearn
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

# 加载数据集
iris = load_iris()
X, y = iris.data, iris.target

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 训练模型
lr = LogisticRegression(max_iter=1000)
lr.fit(X_train, y_train)

# 设置 MLflow 运行
with mlflow.start_run():
    # 记录模型参数和性能指标（这里省略了具体指标的计算）
    mlflow.log_param("solver", lr.solver)
    mlflow.log_param("max_iter", lr.max_iter)

    # 保存模型到 MinIO
    mlflow.sklearn.log_model(sk_model=lr, artifact_path="iris_model", registered_model_name="iris-logistic-regression")

便可以在minio和mlflow里面看到对应的数据。

import mlflow.sklearn
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

iris = load_iris()
X, y = iris.data, iris.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

loaded_model = mlflow.pyfunc.load_model("runs:/<run-id>/iris_model")

predictions = loaded_model.predict(X_test)
print(predictions)

在mlflow里面找到对应的run id，进行加载即可使用对应的模型。

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