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Which two code segments can you use to achieve this goal?
You use the following code to define the steps for a pipeline:
from azureml.core import Workspace, Experiment, Run from azureml.pipeline.core import Pipeline
from azureml.pipeline.steps import PythonScriptStep
ws = Workspace.from_config()
. . .
step1 = PythonScriptStep(name="step1", …)
step2 = PythonScriptsStep(name="step2", …)
pipeline_steps = [step1, step2]
You need to add code to run the steps.
Which two code segments can you use to achieve this goal? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.
A . experiment = Experiment(workspace=ws,
name=’pipeline-experiment’)
run = experiment.submit(config=pipeline_steps)
B . run = Run(pipeline_steps)
C . pipeline = Pipeline(workspace=ws, steps=pipeline_steps)
experiment = Experiment(workspace=ws,
name=’pipeline-experiment’)
run = experiment.submit(pipeline)
D . pipeline = Pipeline(workspace=ws, steps=pipeline_steps)
run = pipeline.submit(experiment_name=’pipeline-experiment’)
Answer: C,D
Explanation:
After you define your steps, you build the pipeline by using some or all of those steps.
# Build the pipeline. Example:
pipeline1 = Pipeline(workspace=ws, steps=[compare_models])
# Submit the pipeline to be run
pipeline_run1 = Experiment(ws, ‘Compare_Models_Exp’).submit(pipeline1)
Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-machine-learning-pipelines
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