背景
mlflow是Databrick开源的机器学习管理平台，它很好的解藕了算法训练和算法模型服务，使得算法工程师专注于模型的训练，而不需要过多的关注于服务的,
而且在我们公司已经有十多个服务稳定运行了两年多。
搭建
mlflow的搭建主要是mlflow tracking server的搭建，tracking server主要是用于模型的元数据以及模型的数据存储
我们这次以minio作为模型数据的存储后台，mysql作为模型元数据的存储，因为这种模式能满足线上的需求，不仅仅是用于测试
minio的搭建
参考我之前的文章MinIO的搭建使用，并且创建名为mlflow的bucket，便于后续操作
mlflow的搭建
conda的安装
参照install conda,根据自己的系统安装不同的conda环境
mlfow tracking server安装
# 创建conda环境 并安装 python 3.6 conda create -n mlflow-1.11.0 python==3.6 #激活conda环境 conda activate mlflow-1.11.0 # 安装mlfow tracking server python需要的依赖包 pip install mlflow==1.11.0 pip install mysqlclient pip install boto3
- mlflow tracking server的启动
暴露出minio url以及需要的ID和KEY，因为mlflow tracking server在上传模型文件时需要 export AWS_ACCESS_KEY_ID=AKIAIOSFODNN7EXAMPLE export AWS_SECRET_ACCESS_KEY=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY export MLFLOW_S3_ENDPOINT_URL=http://localhost:9001 mlflow server \ --backend-store-uri mysql://root:AO,h07ObIeH-@localhost/mlflow_test \ --host 0.0.0.0 -p 5002 \ --default-artifact-root s3://mlflow
访问localhost:5002, 就能看到如下界面:
使用
拷贝以下的wine.py文件
import os import warnings import sys import pandas as pd import numpy as np from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score from sklearn.model_selection import train_test_split from sklearn.linear_model import ElasticNet import mlflow.sklearn def eval_metrics(actual, pred): rmse = np.sqrt(mean_squared_error(actual, pred)) mae = mean_absolute_error(actual, pred) r2 = r2_score(actual, pred) return rmse, mae, r2 if __name__ == "__main__": warnings.filterwarnings("ignore") np.random.seed(40) # Read the wine-quality csv file (make sure you're running this from the root of MLflow!) wine_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "wine-quality.csv") data = pd.read_csv(wine_path) # Split the data into training and test sets. (0.75, 0.25) split. train, test = train_test_split(data) # The predicted column is "quality" which is a scalar from [3, 9] train_x = train.drop(["quality"], axis=1) test_x = test.drop(["quality"], axis=1) train_y = train[["quality"]] test_y = test[["quality"]] alpha = float(sys.argv[1]) if len(sys.argv) > 1 else 0.5 l1_ratio = float(sys.argv[2]) if len(sys.argv) > 2 else 0.5 mlflow.set_tracking_uri("http://localhost:5002") client = mlflow.tracking.MlflowClient() mlflow.set_experiment('http_metrics_test') with mlflow.start_run(): lr = ElasticNet(alpha=alpha, l1_ratio=l1_ratio, random_state=42) lr.fit(train_x, train_y) predicted_qualities = lr.predict(test_x) (rmse, mae, r2) = eval_metrics(test_y, predicted_qualities) print("Elasticnet model (alpha=%f, l1_ratio=%f):" % (alpha, l1_ratio)) print(" RMSE: %s" % rmse) print(" MAE: %s" % mae) print(" R2: %s" % r2) mlflow.log_param("alpha", alpha) mlflow.log_param("l1_ratio", l1_ratio) mlflow.log_metric("rmse", rmse) mlflow.log_metric("r2", r2) mlflow.log_metric("mae", mae) mlflow.sklearn.log_model(lr, "model")
注意：
1.mlflow.set_tracking_uri("http://localhost:5002") 设置为刚才启动的mlflow tracking server的地址
2.mlflow.set_experiment('http_metrics_test') 设置实验的名字
3.安装该程序所依赖的python包
4.如果不是在同一个conda环境中，还得执行
export AWS_ACCESS_KEY_ID=AKIAIOSFODNN7EXAMPLE export AWS_SECRET_ACCESS_KEY=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY export MLFLOW_S3_ENDPOINT_URL=http://localhost:9001
便于python客户端上传模型文件以及模型元数据
直接执行 python wine.py 如果成功，访问mlflow tracking server ui下有如下
点击 2020-10-30 10:34:38，如下：
启动mlflow 算法服务
在同一个conda环境中执行命令
export MLFLOW_TRACKING_URI=http://localhost:5002 mlflow models serve -m runs:/e69aed0b22fb45debd115dfc09dbc75a/model -p 1234 --no-conda
其中e69aed0b22fb45debd115dfc09dbc75a为mlflow tracking server ui中的run id
如遇到ModuleNotFoundError: No module named ‘sklearn’
执行 pip install scikit-learn==0.19.1
遇到ModuleNotFoundError: No module named ‘scipy’
执行pip install scipy
请求访问该model启动的服务:
curl -X POST -H "Content-Type:application/json; format=pandas-split" --data '{"columns":["alcohol", "chlorides", "citric acid", "density", "fixed acidity", "free sulfur dioxide", "pH", "residual sugar", "sulphates", "total sulfur dioxide", "volatile acidity"],"data":[[12.8, 0.029, 0.48, 0.98, 6.2, 29, 3.33, 1.2, 0.39, 75, 0.66]]}' http://127.0.0.1:1234/invocations
输出 [5.455573233630147] 则表明该模型服务成功部署
至此主要简单的mlflow使用就完成了，如果还有mlflow不支持的算法，可以参照自定义model
