python-pytorchHow can I use Python PyTorch without a GPU?

PyTorch is a deep learning library that can be used with or without a GPU. To use PyTorch without a GPU, you will need to install the CPU-only version of PyTorch. This can be done using the following commands:

pip install torch==1.5.0+cpu torchvision==0.6.0+cpu -f https://download.pytorch.org/whl/torch_stable.html

Once installed, you can use PyTorch just as you would with a GPU. For example, you can create a tensor, perform operations on it, and print the result:

import torch

x = torch.rand(5, 3)
y = torch.rand(5, 3)
z = x + y
print(z)

# Output:
# tensor([[1.4486, 0.6862, 1.2105],
#         [1.0808, 0.7302, 1.0615],
#         [1.0721, 0.7155, 0.7374],
#         [1.1788, 0.9099, 1.3288],
#         [1.1930, 0.7202, 0.8854]])

The above example code creates two tensors (x and y), adds them together, and prints the result.

Using PyTorch without a GPU also allows you to take advantage of other features such as data loading and preprocessing. For example, you can use the torchvision package to load and preprocess images:

import torchvision

transform = torchvision.transforms.Compose([
    torchvision.transforms.ToTensor(),
    torchvision.transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])

dataset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)

The above code downloads the CIFAR10 dataset and applies the transform defined in the Compose object. This transform normalizes the images and converts them to tensors.

In summary, PyTorch can be used without a GPU by installing the CPU-only version of PyTorch and using the same methods as when using a GPU. This includes creating tensors, performing operations on them, loading and preprocessing data, and so on.

Helpful links

• PyTorch Installation Guide
• PyTorch Tutorials
• torchvision Documentation

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