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Answer: Run the following code in a notebook:, Run the following code and monitor the console output from the PipelineRun object:
The question asks for two ways to monitor a batch inference pipeline execution in Azure ML. Based on the community discussion and official documentation: - Option D (using `RunDetails` widget) is correct because it provides a visual widget in Jupyter notebooks that shows real-time pipeline progress, which is explicitly mentioned in the Azure ML documentation for monitoring parallel run jobs. - Option E (using `wait_for_completion(show_output=True)`) is correct because it streams console output from the PipelineRun object, showing detailed progress until completion, and is demonstrated in official Azure ML batch scoring tutorials. Option A is incomplete and lacks the specific monitoring method. Option B (Inference Clusters tab) is for managing compute resources, not monitoring pipeline execution progress. Option C (Activity log) is for auditing workspace-level events, not real-time pipeline monitoring. The community consensus, with high upvotes and references to official documentation, confirms D and E as the optimal solutions.
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You create a batch inference pipeline using the Azure ML SDK and run it with the following code:
from azureml.pipeline.core import Pipeline
from azureml.core.experiment import Experiment
pipeline = Pipeline(workspace=ws, steps=[parallelrun_step])
pipeline_run = Experiment(ws, 'batch_pipeline').submit(pipeline)
from azureml.pipeline.core import Pipeline
from azureml.core.experiment import Experiment
pipeline = Pipeline(workspace=ws, steps=[parallelrun_step])
pipeline_run = Experiment(ws, 'batch_pipeline').submit(pipeline)
You need to monitor the progress of the pipeline execution.
What are two possible ways to achieve this goal? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
A
Run the following code in a notebook:
B
Use the Inference Clusters tab in Machine Learning Studio.
C
Use the Activity log in the Azure portal for the Machine Learning workspace.
D
Run the following code in a notebook:
E
Run the following code and monitor the console output from the PipelineRun object: