背景

mlflow是Databrick开源的机器学习治理平台，它很好的解藕了算法训练和算法模型服务，使得算法工程师专一于模型的训练，而不须要过多的关注于服务的,
而且在咱们公司曾经有十多个服务稳固运行了两年多。

搭建

mlflow的搭建次要是mlflow tracking server的搭建，tracking server次要是用于模型的元数据以及模型的数据存储
咱们这次以minio作为模型数据的存储后盾，mysql作为模型元数据的存储，因为这种模式能满足线上的需要，不仅仅是用于测试

  • minio的搭建
    参考我之前的文章MinIO的搭建应用，并且创立名为mlflow的bucket，便于后续操作

  • mlflow的搭建

    • conda的装置
      参照install conda,依据本人的零碎装置不同的conda环境

    • mlfow tracking server装置

      # 创立conda环境 并装置 python 3.6  conda create -n mlflow-1.11.0 python==3.6#激活conda环境conda activate mlflow-1.11.0# 装置mlfow tracking server python须要的依赖包pip install mlflow==1.11.0 pip install mysqlclientpip install boto3

    • mlflow tracking server的启动

      暴露出minio url以及须要的ID和KEY，因为mlflow tracking server在上传模型文件时须要   export AWS_ACCESS_KEY_ID=AKIAIOSFODNN7EXAMPLEexport AWS_SECRET_ACCESS_KEY=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEYexport MLFLOW_S3_ENDPOINT_URL=http://localhost:9001mlflow server \   --backend-store-uri mysql://root:AO,h07ObIeH-@localhost/mlflow_test \   --host 0.0.0.0 -p 5002 \   --default-artifact-root s3://mlflow

      拜访localhost:5002, 就能看到如下界面:

应用

拷贝以下的wine.py文件

import osimport warningsimport sysimport pandas as pdimport numpy as npfrom sklearn.metrics import mean_squared_error, mean_absolute_error, r2_scorefrom sklearn.model_selection import train_test_splitfrom sklearn.linear_model import ElasticNetimport mlflow.sklearndef 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, r2if __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")

留神：

  1.`mlflow.set_tracking_uri("http://localhost:5002")` 设置为方才启动的mlflow tracking server的地址      2.`mlflow.set_experiment('http_metrics_test')` 设置试验的名字      3.装置该程序所依赖的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

间接执行 python wine.py 如果胜利，拜访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

申请拜访该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

输入 [5.455573233630147] 则表明该模型服务胜利部署

至此次要简略的mlflow应用就实现了，如果还有mlflow不反对的算法，能够参照自定义model