PyTorch GPU Documentation

If you are looking for information on how to use PyTorch with GPUs, you've come to the right place. Below, you will find essential information to get started with PyTorch on GPUs.

Prerequisites

Before diving into the GPU documentation, ensure you have the following prerequisites:

PyTorch Installation (with GPU support)
A CUDA-capable NVIDIA GPU
The CUDA Toolkit installed
cuDNN library

What is PyTorch with GPU?

PyTorch with GPU allows you to leverage the power of NVIDIA GPUs to accelerate your deep learning computations. By using GPUs, you can significantly speed up the training of your models, especially for large datasets and complex neural networks.

Getting Started

To use PyTorch with GPU, you need to ensure that your PyTorch installation has been configured to use the GPU. You can check this by running the following code:

import torch

print(torch.cuda.is_available())

If the output is True, it means your PyTorch is configured to use the GPU.

Basic Usage

Here's a simple example of how to use PyTorch with GPU:

import torch

# Create a tensor on the CPU
x = torch.tensor([1, 2, 3], dtype=torch.float32)

# Transfer the tensor to the GPU
x_gpu = x.cuda()

# Perform some computation on the GPU
y = x_gpu * 2

# Transfer the result back to the CPU
y_cpu = y.cpu()

print(y_cpu)

In the example above, we create a tensor on the CPU, transfer it to the GPU, perform some computation, and then transfer the result back to the CPU.

Performance Tips

When using PyTorch with GPU, here are a few tips to optimize your performance:

Use in-place operations to reduce memory usage.
Use the torch.no_grad() context manager for inference to speed up computations.
Optimize your batch size and learning rate for your specific hardware.