azure ML 管道內的環境沖突

Question

到目前為止，我一直在 azure 上使用我的 model 培訓管道，沒有任何問題。 上周我啟動它並收到有關環境版本沖突的錯誤。 我什么都沒改變，但它不再起作用了。 我該如何解決這個問題？

from azureml.train.automl import automl\nImportError: cannot import name 'automl' from 'azureml.train.automl'

我也同時開始收到此警告。

WARNING:azureml.pipeline.core.run:Expected a StepRun object but received <class 'azureml.core.run.Run'> instead.
This usually indicates a package conflict with one of the dependencies of azureml-core or azureml-pipeline-core.
Please check for package conflicts in your python environment

這是我的環境筆記本塊：

from azureml.core.runconfig import RunConfiguration
from azureml.core.conda_dependencies import CondaDependencies

aml_run_config = RunConfiguration()
# Use just-specified compute target ("cpu-cluster")
aml_run_config.target = compute_target

# Specify CondaDependencies obj, add necessary packages
aml_run_config.environment.python.conda_dependencies = CondaDependencies.create(
    conda_packages=['pandas','scikit-learn','pyodbc'], 
    pip_packages=['azureml-sdk[automl]','pyarrow', 'azureml-core>=1.42.0', 'msrest==0.6.21', 'xgboost'])

我嘗試更改 azureml-core、sdk、mrest 等的版本控制，但它仍然給我同樣的錯誤。

Answer 1

from azureml.train.automl import automl

上面的語法用於導入 automl. 但由於版本更新，無法使用Automl特性的程序。 以下兩個過程是可用於替換現有工作的代碼塊。

from azure.ai.ml import automl, Input

上面的代碼塊可用於檢索執行 autoML 訓練所需的所有功能。 使用上述語法后

# training data from your local directory
my_training_data_input = Input(
    type=AssetTypes.MLTABLE, path="./data/training-mltable-folder"
)

# Remote MLTable definition
my_training_data_input  = Input(type=AssetTypes.MLTABLE, path="azureml://datastores/workspaceblobstore/paths/Classification/Train")
# note that the below is a code snippet -- you might have to modify the variable values to run it successfully
classification_job = automl.classification(
    compute=my_compute_name,
    experiment_name=my_exp_name,
    training_data=my_training_data_input,
    target_column_name="y",
    primary_metric="accuracy",
    n_cross_validations=5,
    enable_model_explainability=True,
    tags={"my_custom_tag": "My custom value"}
)

# Limits are all optional

classification_job.set_limits(
    timeout_minutes=600, 
    trial_timeout_minutes=20, 
    max_trials=5,
    enable_early_termination=True,
)

# Training properties are optional
classification_job.set_training(
    blocked_training_algorithms=["LogisticRegression"], 
    enable_onnx_compatible_models=True
)

以上是在訓練 model 中使用的屬性。

這是實現類似工作的另一種方法。

Using AutoMLRun class can help to run the model.
from azureml.train.automl.run import AutoMLRun
   ws = Workspace.from_config()
   experiment = ws.experiments['my-experiment-name']
   automl_run = AutoMLRun(experiment, run_id = your run ID')