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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












 

Suggested Answer: B

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

This question is in DP-100 Exam
For getting Microsoft Azure Data Scientist Associate Certificate


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