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.
Answer:
Explanation:
Step 1: Configure a DataTransferStep() to fetch new image data…
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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