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Description
Operating System
Windows
Version Information
Python version: 3.11.1
azure-ai-ml package version: 1.3.0
Steps to reproduce
Alternatively to the steps below, run the notebook automl-forecasting-task-energy-demand-advanced directly in Azure Machine Learning Studio. Python 3.10 - SDK V2.
- Clone repo and open the automl-forecasting-task-energy-demand-advance jupyter notebook in VS Code.
- Update the credential to be used to
credential = InteractiveBrowserCredential(tenant_id="<MY TENANT ID")
. - Setup a compute resource
compute_config = AmlCompute(
name=compute_name,
type="amlcompute",
size="STANDARD_DS12_V2",
idle_time_before_scale_down=120,
min_instances=0,
max_instances=6,
)
ml_client.begin_create_or_update(compute_config).result()
- Run the code cells of the notebook .
Expected behavior
Expected behavior is to be able to run the job without exceptions.
Actual behavior
The created job fails while fetching data from the MLTable.
Encountered user error while fetching data from Dataset. Error: UserErrorException:
Message: MLTable yaml schema is invalid:
Error Code: Validation
Validation Error Code: Invalid MLTable
Validation Target: MLTableToDataflow
Error Message: Failed to convert a MLTable to dataflow
uri path is not a valid datastore uri path
| session_id=e2cb5fb1-f330-4b5d-a2f2-7784642128f9
InnerException None
ErrorResponse
{
"error": {
"code": "UserError",
"message": "MLTable yaml schema is invalid: \nError Code: Validation\nValidation Error Code: Invalid MLTable\nValidation Target: MLTableToDataflow\nError Message: Failed to convert a MLTable to dataflow\nuri path is not a valid datastore uri path\n| session_id=e2cb5fb1-f330-4b5d-a2f2-7784642128f9"
}
}
Addition information
I've also tried to run the notebook from Azure Machine Learning Studio. This time with Python 3.10 - SDK V2. I get the same error there.
No response