You are a machine learning specialist working for a sports gambling company where you are responsible for building a machine learning model to predict the point spread and over/under of NCAA and NFL games. You have built your custom deep learning model using TensorFlow in SageMaker. You have attempted to train your model on a single GPU, but you have noticed that the amount of game data you need to train with exceeds the single GPU capacity.
How can you change your machine learning code to get it to use multiple GPUs with the least amount of effort on your part?

Answer options:

A.Rewrite your model to use the Factorization Machines algorithm.
B.Rewrite your code in PySpark and use spark to run your code across multiple GPUs.
C.Add Horovod to your code and use its distributed deep learning training framework for TensorFlow.
D.Rewrite your model to use the DeepAR Forecasting algorithm.

Answer: C
Option A is incorrect. The Factorization Machines algorithm is used for classification and regression problems, not deep learning predictions.
Option B is incorrect. Rewriting your model in PySpark would require more work compared to using the Horovod framework.
Option C is correct. Using the Horovod distributed deep learning training framework for TensorFlow allows you to distribute your training across many GPUs in parallel easily.
Option D is incorrect. Rewriting your code to use the DeepAR algorithm would require more work compared to using the Horovod framework.
Reference:
Please see the GitHub page titled Horovod (https://github.com/horovod/horovod), the Amazon SageMaker developer guide titled Use Amazon SageMaker built-in algorithms (https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html), and the Medium article titled 3 Methods for Parallelization in Spark (https://towardsdatascience.com/3-methods-for-parallelization-in-spark-6a1a4333b473)