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Supply chain | Supply Chain Management, Commerce
Answered

Azure machine learning service pipeline failed

Posted on by 192
Hi all, As we know Azure Machine Learning Studio (classic) has been deprecated. We can no longer create new workspaces for it in Azure. It has been replaced by the Azure Machine Learning Service, which provides similar functionality and more. But I am facing pipeline run issue, Can somebody please give insights?
 
I have setup Azure machine learning service for demand forecasting as per the MS document: https://learn.microsoft.com/en-us/dynamics365/supply-chain/master-planning/demand-forecasting-setup#setup-amls 
I chose Option 2: Manually set up your machine learning workspace
When I run the pipeline from Machine learning studio manually with sampleInput.csv file, it runs successfully.
But, when the pipeline is triggered through D365 F&O application through Generate statistical baseline forecasting, pipeline fails with the below error message:
Execution failed. User process '/azureml-envs/azureml_f3f7e6c5fb83d94df23933000bf02da3/bin/python' exited with status code 1. Please check log file 'user_logs/std_log.txt' for error details. Error:     dataflow = _transform_and_validate(  File //azureml-envs/azureml_f3f7e6c5fb83d94df23933000bf02da3/lib/python3.8/site-packages/azureml/data/dataset_factory.py/, line 1225, in _transform_and_validate    _validate_has_data(dataflow, 'Failed to validate the data.')  File //azureml-envs/azureml_f3f7e6c5fb83d94df23933000bf02da3/lib/python3.8/site-packages/azureml/data/dataset_error_handling.py/, line 69, in _validate_has_data    raise DatasetValidationError(error_message + '/' + e.compliant_message, exception=e)azureml.data.dataset_error_handling.DatasetValidationError: DatasetValidationError:  Message: Failed to validate the data.ScriptExecutionException was caused by StreamAccessException.  StreamAccessException was caused by NotFoundException.    Found no resources for the input provided: '[REDACTED]'| session_id=ed1dc30e-1477-4445-8f7f-d34bc9daec6e  InnerException None  ErrorResponse {    /error/: {        /code/: /UserError/,        /message/: /Failed to validate the data./ScriptExecutionException was caused by StreamAccessException./  StreamAccessException was caused by NotFoundException./    Found no resources for the input provided: '[REDACTED]'/| session_id=ed1dc30e-1477-4445-8f7f-d34bc9daec6e/    }}
  • JasdeepSingh Profile Picture
    JasdeepSingh 192 on at
    Azure machine learning service pipeline failed
    Hi,
     
    Are you getting the same error as specified above?
    Did you check logs? Log file at: user_logs/std_log.txt
  • CU16071039-0 Profile Picture
    CU16071039-0 2 on at
    Azure machine learning service pipeline failed
    Hello Jasdeep Singh,
     
    I have tried the same steps as you did, but it keeps giving me the same error even when I use the correct name for the container. I even created a new workspace with all the resources from scratch, but I keep receiving the same error. Any help would be appreciated.
     
    Thank you
  • Verified answer
    JasdeepSingh Profile Picture
    JasdeepSingh 192 on at
    Azure machine learning service pipeline failed
    I got the solution:
     
    The issue was in the container name and Data store pointing to a different container which doesn't point to container storing input files.
    While creating container under storage account in Azure portal, make sure to use default name demplan-azureml and don't change the name. Because if you create a custom named container and point the data store to that container, while running the pipeline it would find input file in the custom named container and not in demplan-azureml container which is created by default and stores the input files from D365F&O.

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