What are the best practices for accelerating Machine Learning models with GPUs?
Machine learning (ML) models can be very computationally intensive, especially when dealing with large and complex data sets. One way to speed up the training and inference of ML models is to use graphics processing units (GPUs), which are specialized hardware devices that can perform parallel operations on large arrays of data. GPUs can offer significant advantages over central processing units (CPUs) for ML tasks, such as faster execution, lower power consumption, and better scalability. However, to make the most of GPUs, you need to follow some best practices that can help you optimize your ML models for GPU acceleration. In this article, we will discuss some of these best practices, such as choosing the right framework, tuning the hyperparameters, using mixed precision, and leveraging cloud services.