Here’s the thing about training deep learning models: you spend all this time getting your hands on a powerful GPU, expecting lightning-fast training, only to find your computer chugging along, fans spinning like crazy, & your CPU usage pegged at 100%. Meanwhile, your expensive GPU is just… sitting there. It’s one of the most frustrating experiences in machine learning.

Honestly, it happens to the best of us. You think you've set everything up right, but your model stubbornly decides to offload all the heavy lifting to the CPU. Why does this happen? And more importantly, how do you fix it & force that model to use the glorious parallel processing power of your GPU?

We're going to dive deep into this. This isn't just a surface-level checklist; we'll get into the nitty-gritty of why your code might be defaulting to the CPU & what you can do about it.

Before we get into troubleshooting, let's quickly touch on why this CPU vs. GPU thing is such a big deal.

CPUs (Central Processing Units) are the general-purpose brains of your computer. They have a few, very powerful cores designed to tackle sequential tasks one after another, REALLY fast. They're perfect for things like loading your data, preprocessing text, or managing the overall flow of your program.

GPUs (Graphics Processing Units), on the other hand, are specialists. They were originally designed for rendering graphics in video games, which involves performing the same calculation on millions of pixels at once. Turns out, this architecture—with thousands of simpler, slower cores—is PERFECT for the matrix multiplications that are at the heart of deep learning. They can perform thousands of operations in parallel, which is what makes training a neural network on a GPU orders of magnitude faster than on a CPU.

So, when your model offloads to the CPU, you're essentially asking a world-class sprinter to run a marathon. It can do it, but it's going to be slow & inefficient. The goal is to get the marathon runner (the GPU) to do the long-distance running (the training loop).

Let's start with the most common culprits & work our way down to the more obscure ones.

Before you start tearing your code apart, let's make sure the hardware & drivers are even ready to play.

This is the most fundamental step. Open up your terminal or command prompt & run this command: