Azure Data Scientists Associate Practice Exam

What is the primary reason to set the 'use_gpu' parameter to True in the PyTorch estimator?

1. To improve model accuracy during training

2. To enable the use of GPU resources for faster computation

3. To allow for variable batch sizes

4. To reduce the overall cost of the model training

The correct answer is: To enable the use of GPU resources for faster computation

Setting the 'use_gpu' parameter to True in the PyTorch estimator is primarily done to enable the use of GPU resources for faster computation. GPUs are specifically designed to handle parallel processing, which is essential for the intensive calculations required in training neural networks. By leveraging the power of a GPU, data scientists can significantly reduce the time it takes to train their models, allowing for more experiments and iterations in a shorter timeframe. Using a GPU can lead to faster feedforward passes and backpropagation, as the computations for the various operations such as matrix multiplications and gradients can be executed concurrently. This is particularly beneficial for large datasets or complex models, where the amount of computation needed can be substantial. While model accuracy can be influenced by various factors, simply utilizing a GPU does not inherently alter the model's accuracy. It may help converge to a better-performing model more quickly due to the opportunity for extensive training iterations. However, the fundamental purpose of setting 'use_gpu' to True is to enhance computation speeds, thus making the training process more efficient.