Microsoft Certified: Azure AI Engineer Associate, Knowledge Mining Error on "Enrich a search index using Azure Machine Learning model"

Question

Hello, I am following the mslearn tutorial for Exam AZ-102: Microsoft Certified: Azure AI Engineer Associate. I am in the Knowledge Mining section, specifically the tutorial, Build an Azure Machine Learning custom skill for Azure AI Search. Under the section, Deploy the model with the updated scoring code, I am getting the error, ResourceOperationFailure: User container has crashed or terminated. Below is a screenshot of the error.

This question is related to the following Learning Module

Answer 1

Hi John Hancock,

Thank you for reaching out to Microsoft Q & A forum.

The error "ResourceOperationFailure: User container has crashed or terminated" indicates that the container running your model has encountered a problem and stopped working. This can be due to several reasons such as issues with the scoring script, environment dependencies, or configuration problems.

1.Please check the score.py script to ensure it is correctly formatted and free of syntax errors. Also, verify that all required libraries and dependencies are properly imported and available in the script.
2.Please confirm that the custom environment you selected contains all the necessary packages and dependencies. If any dependencies are missing or incorrectly specified, the container may fail to start. Additionally, please check the environment.yml or requirements.txt file used to create the custom environment to ensure all dependencies are listed correctly.
3.Please navigate to the Azure Machine Learning workspace, select the deployed endpoint, and check the logs for detailed error messages. These logs can provide insights into what caused the container to crash. Look for errors related to missing dependencies, import errors, or runtime exceptions in the logs.
4.Please verify that the selected VM size (e.g., Standard_D2as_v4) has sufficient resources (CPU, memory) to run the model and scoring script. If the model or script requires more resources, consider selecting a more powerful VM size. Additionally, check the resource quotas in your Azure subscription to ensure you have not exceeded the limits.
5.It would be helpful to make minor changes to the scoring script or environment and redeploy the model to resolve issues. For example, you could add additional logging to the score.py script to pinpoint where the error occurs. Please ensure the latest version of your model and script are used during deployment.
6.Please ensure that all required libraries are compatible with each other to avoid runtime failures caused by conflicting versions. Additionally, use virtual environments or Docker containers to manage dependencies effectively.
7.Sure, before deploying, it's a good idea to test your scoring script locally in an environment similar to what Azure will use. This helps to identify any potential issues before you go live.

By following these steps, you should be able to diagnose and resolve the "ResourceOperationFailure: User container has crashed or terminated" error during your model deployment process.

Additional Information,
Troubleshooting online endpoints deployment and scoring: https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-online-endpoints?view=azureml-api-2&tabs=cli

Please feel free to contact us if you have any additional questions.

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