Child job failed in a pipeline - Could not resolve URIS of type data for assets

Paul, Avishek 5

I tried to create a pipeline based on the examples provided in the notebook 'Run a pipeline job.ipynb' under Labs/09/ of the repo https://github.com/MicrosoftLearning/mslearn-azure-ml.git as mentioned in https://microsoftlearning.github.io/mslearn-azure-ml/Instructions/09-Run-pipelines.html.

However I am getting the following error message with one of the child jobs 'clean_data' :

" Could not resolve uris of type data for assets azureml://locations/eastus/workspaces/{guid}/data/diabetes-data/versions/1 for run with runId {runid}" Kindly assist how to resolve it.

Thanks!

romungi-MSFT 43,696 Reputation points Microsoft Employee

2023-05-03T07:56:48.9733333+00:00
@Paul, Avishek The error indicates that the step to setup compute and data where the setup script setup.sh needs to be run did not complete successfully to register the diabetes.csv file located in the repo. You can confirm this by going to ML portal and looking up the Data tab for all data assets. These are the steps in setup.sh which creates the same. Ensure the path that is used in the below step is correctly setup relative to your current path.

# Create data assets echo "Create training data asset:" az ml data create --type uri_file --name "diabetes-data" --path ./data/diabetes.csv -w mlw-dp100-labs -g rg-dp100-labs

The other steps in the script create compute instance and compute clusters. If these are also not available then you need to re-run the setup script and then run the notebook.
romungi-MSFT 43,696 Reputation points Microsoft Employee

2023-05-19T17:17:01.3133333+00:00

@Paul, Avishek Did you get a chance to follow the above steps and re-run successfully?
Michell Easmus 0 Reputation points

2023-05-19T18:29:28.52+00:00

The error message suggests that there is an issue with resolving the URIs (Uniform Resource Identifiers) for the data assets in your Azure Machine Learning workspace. Here are a few steps you can take to resolve this issue:

Verify Data Asset URIs: Double-check the URIs of the data assets used in your pipeline, specifically the URIs related to the 'diabetes-data' asset. Ensure that the URIs are correctly formatted and match the actual location of the data in your Azure Machine Learning workspace.

Check Data Asset Availability: Confirm that the 'diabetes-data' asset exists in your Azure Machine Learning workspace and that it has the correct version specified in the pipeline. If the data asset is missing or has a different version, you may need to upload the data or update the pipeline code accordingly.

Check Workspace and Datastore Configuration: Ensure that your Azure Machine Learning workspace and associated datastores are properly set up. Verify that the workspace and datastore configurations in your pipeline code match the actual configurations in your Azure environment.

Validate Datastore Connectivity: Check the connectivity and access permissions for the datastore associated with the 'diabetes-data' asset. Make sure that the datastore is accessible from your Azure Machine Learning workspace and that the necessary credentials or authentication methods are correctly configured.

Review Pipeline Code: Double-check your pipeline code for any typos, incorrect references, or missing steps related to the 'diabetes-data' asset. Ensure that the pipeline is correctly configured to use the data asset in the expected locations.

If you have followed the instructions in the provided notebook and documentation, the issue may be related to specific configurations or data asset availability in your Azure environment. I recommend carefully reviewing the documentation and comparing it with your own setup to identify any discrepancies or missing steps.

If the issue persists, you can consider reaching out to Microsoft Learning support or posting a question in the appropriate support channels related to the Azure Machine Learning service. They will have access to more specific information and can provide guidance based on your particular scenario.

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Child job failed in a pipeline - Could not resolve URIS of type data for assets