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.

Last Updated on October 21, 2021 by Admin

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?

  • environment_definition = {"training_data":training_ds}
  • inputs = [training_ds.as_named_input('training_ds')]
  • script_params = {"--training_ds":training_ds}
  • source_directory = training_ds
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’)

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