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GPU Acceleration in PyTorch

Introduction​

Harnessing the power of GPU acceleration is one of the key aspects of deep learning. In this article, we will dive deep into understanding how to use GPU acceleration in PyTorch. PyTorch seamlessly allows for operations on both CPUs and GPUs with a focus on tensor computations and deep neural networks.

Setting up PyTorch for GPU​

Before we start, make sure you have a working CUDA-compatible GPU. You can check this by using nvidia-smi command in your terminal. Also, when installing PyTorch, ensure that you install the version compatible with your CUDA version.

GPU vs CPU​

Before we jump into the coding, it's important to understand why we use GPUs over CPUs for deep learning tasks. The primary reason is the architecture of a GPU. A GPU is composed of hundreds of cores that can handle thousands of threads simultaneously. This makes it ideal for the many parallelizable tasks in deep learning.

Moving Tensors to the GPU​

The basic data structure in PyTorch is a tensor. To use GPU acceleration, we need to move the tensors from the CPU to the GPU. Here's how to do this:

# Import the PyTorch library
import torch

# Create a tensor
tensor_cpu = torch.zeros(100, 100)

# Move the tensor to GPU
tensor_gpu = tensor_cpu.to('cuda')

Here, cuda refers to the GPU. If you have more than one GPU, you can specify which one to use like so: cuda:0 for the first GPU, cuda:1 for the second, and so on.

Verifying the Device​

To verify if a tensor is on the GPU, use the .device attribute of the tensor.

print(tensor_gpu.device)

Moving Models to the GPU​

Just like tensors, models can also be moved to the GPU.

# Assuming 'model' is your neural network model
model = model.to('cuda')

After this, all the model's parameters and buffers are moved to the GPU.

Training on GPU​

Finally, during your training loop, ensure both your model and data are on the same device. Here's a simplified example:

# Assuming 'data' is your input and 'target' is your label
data, target = data.to('cuda'), target.to('cuda')

# Forward pass
output = model(data)

# Compute loss
loss = criterion(output, target)

# Backward pass and optimization
loss.backward()
optimizer.step()

Multiple GPUs​

If you have more than one GPU, you can leverage them by using DataParallel. It wraps around any module and uses multiple GPUs to parallelize the forward pass.

model = nn.DataParallel(model)

Conclusion​

In this article, we covered how to use GPU acceleration in PyTorch. We looked at how to move tensors and models to the GPU and how to ensure your training loop runs on the GPU. With this knowledge, you can now harness the full power of your GPU(s) for your deep learning tasks!