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

In the previous article, 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_datas=data)
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 availabledevices="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.

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