Azure Data Scientists Associate Practice Exam

To successfully run the script sample.py using the PyTorch estimator, it's necessary to specify GPU settings and install any needed libraries. PyTorch is a deep learning framework that often utilizes GPU acceleration for training models, as it significantly speeds up the computation for complex tasks. Therefore, configuring the GPU settings properly is essential; it ensures that the training process can leverage the power of the available hardware effectively.

Additionally, installing the necessary libraries is crucial to ensure that all dependencies that sample.py requires are met. This might include specific versions of PyTorch and any other relevant Python packages. Without addressing these two elements, the script may not run correctly or might fail altogether.

In contrast, using a scikit-learn model for initial training is not a requirement for running a PyTorch script, as scikit-learn is a different library designed for traditional machine learning tasks rather than deep learning with PyTorch. Setting up a TensorFlow environment beforehand is unnecessary since TensorFlow and PyTorch are separate frameworks; one does not require the other to function. Lastly, defining the output directory in the same script is not a strict requirement. While specifying an output location can be useful, it’s not essential for executing the script.