What is PyTorch?

It’s a Python based scientific computing package targeted at two sets of audiences:

  • A replacement for numpy to use the power of GPUs
  • a deep learning research platform that provides maximum flexibility and speed

Getting Started


Tensors are similar to numpy’s ndarrays, with the addition being that Tensors can also be used on a GPU to accelerate computing.

from __future__ import print_function
import torch

Construct a 5x3 matrix, uninitialized:

x = torch.Tensor(5, 3)

Construct a randomly initialized matrix

x = torch.rand(5, 3)

Get its size



torch.Size is in fact a tuple, so it supports the same operations


There are multiple syntaxes for operations. Let’s see addition as an example

Addition: syntax 1

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

Addition: syntax 2

print(torch.add(x, y))

Addition: giving an output tensor

result = torch.Tensor(5, 3)
torch.add(x, y, out=result)

Addition: in-place

# adds x to y


Any operation that mutates a tensor in-place is post-fixed with an _ For example: x.copy_(y), x.t_(), will change x.

You can use standard numpy-like indexing with all bells and whistles!

print(x[:, 1])

Read later:

100+ Tensor operations, including transposing, indexing, slicing, mathematical operations, linear algebra, random numbers, etc are described here

Numpy Bridge

Converting a torch Tensor to a numpy array and vice versa is a breeze.

The torch Tensor and numpy array will share their underlying memory locations, and changing one will change the other.

Converting torch Tensor to numpy Array

a = torch.ones(5)
b = a.numpy()

See how the numpy array changed in value.


Converting numpy Array to torch Tensor

See how changing the np array changed the torch Tensor automatically

import numpy as np
a = np.ones(5)
b = torch.from_numpy(a)
np.add(a, 1, out=a)

All the Tensors on the CPU except a CharTensor support converting to NumPy and back.

CUDA Tensors

Tensors can be moved onto GPU using the .cuda function.

# let us run this cell only if CUDA is available
if torch.cuda.is_available():
    x = x.cuda()
    y = y.cuda()
    x + y

Total running time of the script: ( 0 minutes 0.000 seconds)

Generated by Sphinx-Gallery