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Exam DP-100 topic 3 question 8 discussion

Actual exam question from Microsoft's DP-100
Question #: 8
Topic #: 3
[All DP-100 Questions]

DRAG DROP -
You create a multi-class image classification deep learning experiment by using the PyTorch framework. You plan to run the experiment on an Azure Compute cluster that has nodes with GPU's.
You need to define an Azure Machine Learning service pipeline to perform the monthly retraining of the image classification model. The pipeline must run with minimal cost and minimize the time required to train the model.
Which three pipeline steps should you run in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
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Suggested Answer:
Step 1: Configure a DataTransferStep() to fetch new image dataג€¦
Step 2: Configure a PythonScriptStep() to run image_resize.y on the cpu-compute compute target.
Step 3: Configure the EstimatorStep() to run training script on the gpu_compute computer target.
The PyTorch estimator provides a simple way of launching a PyTorch training job on a compute target.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-pytorch

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chaudha4
Highly Voted 3 years, 7 months ago
Hope we don't see this question in exam since it is covering topics that are all deprecated.
upvoted 17 times
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ning
Highly Voted 2 years, 6 months ago
I think the answer is correct, though it might not in the scope of test any more 1. Fetch whole / big image 2. Break into small / tile images 3. Run training models
upvoted 6 times
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haby
Most Recent 11 months, 2 weeks ago
EstimatorStep is deprecated, it's recommended to use PythonSriptStep or CommandStep. For now(2023-12), I will take : 1. Configure a PythonScriptStep() to run image_fetcher.py on the cpu-compute compute target. 2. Configure a PythonScriptStep() to run image_resize.py on the cpu-compute compute target. 3. Configure an PythonScriptStep() to run an estimator that runs the bird_classifier_train.py model training script on the gpu_compute compute target.
upvoted 4 times
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NullVoider_0
11 months, 3 weeks ago
Based on the requirements to minimize cost and training time for the image classification retraining pipeline, the three steps that should be configured are: 1. Configure a PythonScriptStep() to run image_fetcher.py on the cpu-compute compute target. 2. Configure a PythonScriptStep() to run image_resize.py on the cpu-compute compute target. 3. Configure an EstimatorStep() to run an estimator that runs the bird_classifier_train.py model training script on the gpu_compute compute target.
upvoted 1 times
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giusecozza
2 years, 3 months ago
I'm afraid the first step is wrong, since DataTransferStep is used only to move data between datastores, not fetching data from the web. https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.data_transfer_step.datatransferstep?view=azure-ml-py
upvoted 3 times
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chevyli
2 years, 3 months ago
Look correct
upvoted 3 times
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luisbenitez_14
2 years, 5 months ago
Estimator is deprecated. PythonScript is the correct 3rd step
upvoted 1 times
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