Скачать или смотреть Solving ModuleNotFoundError: Running Python Scripts in a DevOps Pipeline with AzureML

Learn how to resolve the `ModuleNotFoundError` when importing `azureml.core` in a DevOps pipeline by installing the required Python packages.
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How to Fix the ModuleNotFoundError in Your DevOps Pipeline with AzureML

When working in a DevOps environment, you may encounter various challenges while attempting to run Python scripts. One frequent issue is the ModuleNotFoundError, particularly when trying to import specific packages, such as azureml.core. This guide will help you troubleshoot this problem and set up your DevOps pipeline correctly.

Understanding the Problem

In your pipeline configuration, you found that while a simple 'hello world' Python script runs without issues, importing the Azure Machine Learning library throws an error:

[[See Video to Reveal this Text or Code Snippet]]

This error indicates that the Azure ML library is not available in the Python environment that your DevOps pipeline is using.

Why You Encounter This Error

The default Python environment does not come with all libraries pre-installed. In your case, azureml.core is missing, which is why your script fails when trying to use it. However, you can solve this problem by ensuring that the required packages are installed before executing your script.

Steps to Fix the Issue

Modify Your YAML Configuration

To correctly install azureml.core in your pipeline, you need to update your YAML configuration file. Here’s how to do it:

Specify the Python Version: Use the correct Python version that your script requires. As of now, it's recommended to use Python 3.8.

Upgrade Pip: Ensure that your package manager, pip, is up-to-date, so it can install other packages correctly.

Install Required Packages: Specifically install the azureml.core package.

Here’s how your updated YAML file should look:

[[See Video to Reveal this Text or Code Snippet]]

Understanding Each Section

UsePythonVersion: This task sets the Python version to be used in the pipeline.

Upgrade Pip: Upgrading pip ensures you have the latest version, which can avoid potential installation issues.

Install Packages: This step makes sure your Python environment has access to azureml.core, resolving your import issue.

Run Your Script: Finally, your script is executed as intended without any import errors.

Testing Your Setup

After updating your YAML configuration, commit the changes and push them to your repository. Monitor the pipeline execution and verify if the Python script runs successfully. You should see the message indicating that Azure ML is ready to use without encountering the ModuleNotFoundError.

Conclusion

By following the steps outlined in this guide, you’ve effectively resolved the import error in your DevOps pipeline associated with azureml.core. Remember, ensuring that all required Python packages are installed in your environment is crucial for smooth script execution. Happy coding!