如何在Azure机器学习中更改MLFlow中的Sklearn Flavors版本？

Question

当我记录训练有素的模型时在推理期间。 中设置conda.yml文件中的sklearn版本

我可以通过在mlflow.sklearn.log_model（conda_env ='my_env'）

。 net/urygm.png“ rel =“ nofollow noreferrer”>

这是屏幕拍摄需求.txt

但是，mlModel文件中的口味下的Sklearn版本保持不变，这是引起问题的文件：

，我用这是我用来在Azure中创建此mlflow实验的脚本机器学习笔记本。

import mlflow
from sklearn.tree import DecisionTreeRegressor

from azureml.core import Workspace
from azureml.core.model import Model
from azureml.mlflow import register_model


def run_model(ws, experiment_name, run_name, x_train, y_train):
    
    # set up MLflow to track the metrics
    mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())
    mlflow.set_experiment(experiment_name)  
    
    with mlflow.start_run(run_name=run_name) as run:
        
        # fit model
        regression_model = DecisionTreeRegressor()
        regression_model.fit(x_train, y_train)
    
        # log training score 
        training_score = regression_model.score(x_train, y_train)
        mlflow.log_metric("Training score", training_score)

        my_conda_env = {
                    "name": "mlflow-env",
                    "channels": ["conda-forge"],
                    "dependencies": [
                        "python=3.8.5",
                        {
                            "pip": [
                                "pip",
                                "scikit-learn~=1.0.0",
                                "uuid==1.30",
                                "lz4==4.0.0",
                                "psutil==5.9.0",
                                "cloudpickle==1.6.0",
                                "mlflow",
                            ],
                        },
                    ],
                }

        
        # register the model
        mlflow.sklearn.log_model(regression_model, "model", conda_env=my_conda_env)

    model_uri = f"runs:/{run.info.run_id}/model"
    model = mlflow.register_model(model_uri, "sklearn_regression_model")

if __name__ == '__main__':

    # connect to your workspace
    ws = Workspace.from_config()

    # create experiment and start logging to a new run in the experiment
    experiment_name = "exp_name"

    # mlflow run name
    run_name= '1234'

  
    # get train data
    x_train, y_train  = get_train_data()
    
    run_model(ws, experiment_name, run_name, x_train, y_train)

任何想法如何将mlmodel文件中的风味sklearn版本从“ 0.22.1” “ ”更改为“ 1.0.0” 在我的脚本中？

预先感谢！

Answer 1

版本的修改必须来自 “ unignts.txt” 。我们需要手动覆盖所需的版本，并将构建移至管道。

手动编辑以下

conda_env = {
    'channels': ['conda-forge'],
    'dependencies': [
        'python=3.8.8',
        'pip'],
    'pip': [
        'mlflow',
        'scikit-learn==0.23.2',
        'cloudpickle==1.6.0'
    ],
    'name': 'mlflow-env'
}
mlflow.sklearn.log_model(model, "my_model", conda_env=conda_env)

应在conda中

name: mlflow-env
channels:
  - conda-forge
dependencies:
- python=3.8.8
- pip
- pip:
  - mlflow
  - scikit-learn==0.23.2
  - cloudpickle==1.6.0

。

python: 3.8.8
build_dependencies:
  - pip==21.1.3
  - setuptools==57.4.0
  - wheel==0.37.0
dependencies:
  - -r requirements.txt

代码

mlflow
scikit-learn==0.23.2
cloudpickle==1.6.0

Answer 2

我终于能够解决这个问题。显然，MLFLOW MLFILE中的口味使用工作空间中安装的Scikit-Learn版本。我需要做的就是从工作区内的CLI升级Scikit-Learn。

1. 在Azure Machine Learning Notebook中打开CLI
2. 贴上您预定的风味包装。我的案例是Scikit-Learn，就我而言，我使用1.0.2：

 pip install --upgrade scikit-learn==1.0.2

3. 在MLFlow中登录模型，通过再运行训练脚本一次。