Does the solution meet the goal?

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.

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You have a Python script named train.py in a local folder named scripts. The script trains a regression model by using scikit-learn. The script includes code to load a training data file which is also located in the scripts folder.

You must run the script as an Azure ML experiment on a compute cluster named aml-compute.

You need to configure the run to ensure that the environment includes the required packages for model training. You have instantiated a variable named aml-compute that references the target compute cluster.

Solution: Run the following code:

Does the solution meet the goal?
A . Yes
B . No

View Answer

Answer: B

Explanation:

There is a missing line: conda_packages=[‘scikit-learn’], which is needed.

Correct example:

sk_est = Estimator(source_directory=’./my-sklearn-proj’,

script_params=script_params,

compute_target=compute_target,

entry_script=’train.py’,

conda_packages=[‘scikit-learn’])

Note:

The Estimator class represents a generic estimator to train data using any supplied framework.

This class is designed for use with machine learning frameworks that do not already have an Azure Machine Learning pre-configured estimator. Pre-configured estimators exist for Chainer, PyTorch, TensorFlow, and SKLearn.

Example:

from azureml.train.estimator import Estimator

script_params = {

# to mount files referenced by mnist dataset

‘–data-folder’: ds.as_named_input(‘mnist’).as_mount(),

‘–regularization’: 0.8

}

Reference: https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.estimator.estimator