如何在蓝色机器学习的mlflow中更改Sklearn口味版本？

Question

当我记录经过训练的模型时，我需要将mlflow中的"sklearn_version“从"0.22.1”更改为"1.0.0“，这是因为这个模型将与我在推理期间用于部署的sklearn版本不兼容。我可以通过在conda.yml文件中设置"conda_env“

mlflow.sklearn.log_model(conda_env= 'my_env')

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这是requirements.txt的屏幕截图

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但是，在MLmodel文件中，sklearn下的版本保持不变，这是导致问题的文件：

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下面是我用来在蓝色机器学习笔记本中创建这个mlflow实验的脚本。

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)

您知道如何在我的脚本中将MLmodel文件中的版本从"0.22.1"更改为"1.0.0"吗？

提前表示感谢！

Answer 1

版本的修改必须来自"requirements.txt".。我们需要手动覆盖所需的版本，并将构建移动到管道中。

手动编辑以下代码

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)

下面的代码应该在conda.yml中

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

下面的代码块必须在python_env.yaml中

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

在requirements.txt中必须有以下内容

mlflow
scikit-learn==0.23.2
cloudpickle==1.6.0

Answer 2

我终于解决了这个问题。显然，mlflow MLfile中的口味使用的版本是安装的scikit-在工作区中学习。我所需要做的就是升级scikit--从工作区内的cli中学习。

notebooks

• upgarde

1. 在蓝色机器上打开cli学习你想要的风味包。在我的案例中，我使用了1.0.2:

 pip install --upgrade scikit-learn==1.0.2

通过再次运行培训脚本，

1. 将您的模型记录在mlflow中。

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