Neural Networks with PyTorch and Lightning AI Part 5: Final Results and GPU Acceleration

A developer using PyTorch and Lightning AI demonstrated automated neural network training with GPU acceleration. By setting the trainer's accelerator and devices to 'auto', Lightning automatically detects and utilizes available GPUs, simplifying hardware acceleration. The final bias value was optimized to -16.0098, and the model's output was visualized.

In the previous article https://dev.to/rijultp/neural-networks-with-pytorch-and-lightning-ai-part-4-from-manual-training-to-automated-training-2nkf , we saw how we automated several manual pieces when training a neural network. In this article, we will check the final results and also see how we can do hardware acceleration. For verifying that the final bias is correctly optimized, we can print out the new value: trainer.fit model, train dataloaders=dataloader print model.final bias.data This gives us -16.0098 . Let’s also visualize it: output values = model input doses sns.set style="whitegrid" sns.lineplot x=input doses, y=output values.detach , color="green", linewidth=2.5 plt.ylabel "Effectiveness" plt.xlabel "Dose" Now there are some practical questions, like: if we want to use GPU acceleration, how is that possible? On most basic laptops, the CPU does all the work . In that case, both: are stored and processed on the CPU. All computations happen there. But CPUs have limited cores, so when we run something like neural network training, it can become very slow. To speed things up, we use Graphics Processing Units GPUs . A GPU has 10x to 100x more compute cores compared to a CPU. However, without tools like Lightning, using GPUs requires manually assigning tensors and operations to the correct device, which becomes complicated. With Lightning, this process is automated. We can simply write: trainer = L.Trainer max epochs=34, accelerator="auto", devices="auto" Here: accelerator="auto" allows Lightning to automatically detect whether a GPU is available devices="auto" lets Lightning decide how many GPUs to useThis means the same code can: So Lightning makes training not only simpler, but also hardware-flexible without extra effort . In the coming series, we will explore more such applications of lightning. AI agents write code fast. They also silently remove logic, change behavior, and introduce bugs -- without telling you. You often find out in production. git-lrc https://github.com/HexmosTech/git-lrc fixes this. It hooks into git commit and reviews every diff before it lands. 60-second setup. Completely free. Any feedback or contributors are welcome It's online, source-available, and ready for anyone to use. Give it a ⭐ star on Github https://github.com/HexmosTech/git-lrc