PyTorch 101 Memory Management and Using Multiple GPUs

Introduction

When working with deep learning models that use PyTorch, efficiently managing GPUs can make a huge difference in performance. Whether you’re training large models or running complex computations, using multiple GPUs can significantly speed up the process. However, handling multiple GPUs properly requires understanding different parallelism techniques, automating GPU selection, and troubleshooting memory issues.

In this article, we’ll explore:

• How do you use multiple GPUs for your network, whether through data parallelism (splitting data across GPUs) or model parallelism (distributing model layers across GPUs)?
• How to automate GPU selection so PyTorch assigns available GPUs to new objects.
• How to diagnose and fix memory issues, ensuring smooth training and inference without running into out-of-memory errors.

By the end of this guide, you will understand how to optimize GPU usage in PyTorch.