Home » Microsoft » DP-100 v.2 » You use the Azure Machine Learning service to create a tabular dataset named training_data. You plan to use this dataset in a training script.
You use the Azure Machine Learning service to create a tabular dataset named training_data. You plan to use this dataset in a training script.
You create a variable that references the dataset using the following code:
training_ds = workspace.datasets.get("training_data") You define an estimator to run the script.
You need to set the correct property of the estimator to ensure that your script can access the training_data dataset.
Which property should you set?
A. environment_definition = {"training_data":training_ds}
B. inputs = [training_ds.as_named_input(‘training_ds’)]
C. script_params = {"–training_ds":training_ds}
D. source_directory = training_ds
Correct Answer: B
Explanation/Reference:
Explanation:
Example:
# Get the training dataset diabetes_ds = ws.datasets.get("Diabetes Dataset") # Create an estimator that uses the remote compute hyper_estimator = SKLearn(source_directory=experiment_folder, inputs=[diabetes_ds.as_named_input(‘diabetes’)], # Pass the dataset as an input compute_target = cpu_cluster, conda_packages=[‘pandas’,’ipykernel’,’matplotlib’], pip_packages=[‘azureml-sdk’,’argparse’,’pyarrow’], entry_script=’diabetes_training.py’) Reference:
https://notebooks.azure.com/GraemeMalcolm/projects/azureml-primers/html/04%20-%20Optimizing%20Model%20Training.ipynb