Exam DP-100 topic 2 question 83 discussion

# Define the script run configuration src = ScriptRunConfig(source_directory='.', script='train.py', compute_target=compute_target, environment=env)

from azureml.core import ScriptRunConfig # Create a ScriptRunConfig object run_config = ScriptRunConfig( name="my-training-run", script="train.py", compute_target="my-compute-target", environment="my-environment", ) # Submit the run run = run_config.submit() # Wait for the run to complete run.wait_for_completion()

ScriptRunConfig()

The correct class to use for preparing information to submit a training run in the given scenario is B. ScriptRunConfig. from azureml.core import ScriptRunConfig, Environment, Workspace # Create a ScriptRunConfig script_run_config = ScriptRunConfig(source_directory='path/to/scripts', script='train.py', compute_target='my_compute_target', environment='my_environment') # Submit the training run run = Experiment(workspace=Workspace.get('my_workspace'), name='my_experiment').submit(script_run_config)

To submit a training run in Azure Machine Learning using Python code, you should create a ScriptRunConfig object. This class defines the configuration information needed for running a training script on a compute target. The ScriptRunConfig class allows you to specify the compute target, the environment, the training script, and any script arguments or data references needed by the script.