You create a batch inference pipeline by using the Azure ML SDK. You run the pipeline by using 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)
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.

Question

You create a batch inference pipeline by using the Azure ML SDK. You run the pipeline by using 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)

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.

ESSIS MAXIME-ALPHONSE SELLY · Accepted Answer

Run the following code in a notebook:

ESSIS MAXIME-ALPHONSE SELLY · Accepted Answer

Run the following code and monitor the console output from the PipelineRun object:

ESSIS MAXIME-ALPHONSE SELLY · Answer

Use the Inference Clusters tab in Machine Learning Studio.

ESSIS MAXIME-ALPHONSE SELLY · Answer

Use the Activity log in the Azure portal for the Machine Learning workspace.

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