Assistance Needed: "numpy.core.multiarray failed to import" Error During the Monitoring job at Model Performance Metrics Computation step in Azure ML Workspace

Question

I am encountering an issue while running a monitoring pipeline job in Azure Machine Learning. During the "Model Performance - Compute Metrics" step, I receive the following error:

"numpy.core.multiarray failed to import"

I do not have control over the packages that it uses while running this job

I am using this schema for creating the yaml file for setting up the monitoring job

$schema: https://azuremlschemas.azureedge.net/latest/monitorSchedule.schema.json

In documentation also i am not able to find if we can set up a custom environment there. The monitoring job automatically does the steps i only provide what metrices it needs to calculate and the data it requires. Has anyone else experienced a similar problem, and if so, how did you address it?

Answer 1

Hi Vivek Kumar!

Sorry for the delay in response. I checked with our team internally.

We can create custom signal component to include in the model monitoring yaml to update the environment for spark computes.

# custom-monitoring.yaml
$schema:  http://azureml/sdk-2-0/Schedule.json
name: my-custom-signal
trigger:
  type: recurrence
  frequency: day # can be minute, hour, day, week, month
  interval: 7 # #every day
create_monitor:
  compute:
    instance_type: "standard_e4s_v3"
    runtime_version: "3.3"
  monitoring_signals:
    customSignal:
      type: custom
      component_id: azureml:my_custom_signal:1.0.0 #your custom component 
      input_data:
        production_data:
          input_data:
            type: uri_folder
            path: azureml:my_production_data:1
          data_context: test
          data_window:
            lookback_window_size: P30D
            lookback_window_offset: P7D
          pre_processing_component: azureml:custom_preprocessor:1.0.0
      metric_thresholds:
        - metric_name: std_deviation
          threshold: 2
  alert_notification:
    emails:
      - abc@example.com

az ml schedule create -f ./custom-monitoring.yaml

Reference - Custom signal to update spark computes with dependencies. Hope it fixes your issue now.

Thank you.