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PyTorch logo black.svg
Original author(s)
  • Adam Paszke
  • Sam Gross
  • Soumith Chintala
  • Gregory Chanan
Developer(s)Facebook's AI Research lab (FAIR)
Initial releaseSeptember 2016; 5 years ago (2016-09)[1]
Stable release
1.10.0[2] Edit this on Wikidata / 21 October 2021; 2 months ago (21 October 2021)
Written in
Operating system
PlatformIA-32, x86-64
Available inEnglish
TypeLibrary for machine learning and deep learning

PyTorch is an open source machine learning library based on the Torch library,[3][4][5] used for applications such as computer vision and natural language processing,[6] primarily developed by Facebook's AI Research lab (FAIR).[7][8][9] It is free and open-source software released under the Modified BSD license. Although the Python interface is more polished and the primary focus of development, PyTorch also has a C++ interface.[10]

A number of pieces of deep learning software are built on top of PyTorch, including Tesla Autopilot,[11] Uber's Pyro,[12] Hugging Face's Transformers,[13] PyTorch Lightning,[14][15] and Catalyst.[16][17]

PyTorch provides two high-level features:[18]


Facebook operates both PyTorch and Convolutional Architecture for Fast Feature Embedding (Caffe2), but models defined by the two frameworks were mutually incompatible. The Open Neural Network Exchange (ONNX) project was created by Facebook and Microsoft in September 2017 for converting models between frameworks. Caffe2 was merged into PyTorch at the end of March 2018.[19]

PyTorch tensors[edit]

PyTorch defines a class called Tensor (torch.Tensor) to store and operate on homogeneous multidimensional rectangular arrays of numbers. PyTorch Tensors are similar to NumPy Arrays, but can also be operated on a CUDA-capable Nvidia GPU. PyTorch supports various sub-types of Tensors.[20]

Note that the term "tensor" here does not carry the same meaning as in mathematics or physics. The meaning of the word in those areas is only tangentially related to the one in Machine Learning. In mathematics, a tensor is a certain kind of object in linear algebra, while in physics the term "tensor" usually refers to what mathematicians call a tensor field.


Autograd module[edit]

PyTorch uses a method called automatic differentiation. A recorder records what operations have performed, and then it replays it backward to compute the gradients. This method is especially powerful when building neural networks to save time on one epoch by calculating differentiation of the parameters at the forward pass.

Optim module[edit]

torch.optim is a module that implements various optimization algorithms used for building neural networks. Most of the commonly used methods are already supported, so there is no need to build them from scratch.

nn module[edit]

PyTorch autograd makes it easy to define computational graphs and take gradients, but raw autograd can be a bit too low-level for defining complex neural networks. This is where the nn module can help.


The following program shows the functionality of the library with a simple example

import torch
dtype = torch.float
device = torch.device("cpu") # This executes all calculations on the CPU
# device = torch.device("cuda:0") # This executes all calculations on the GPU

# Creation of a tensor and filling of a tensor with random numbers
a = torch.randn(2, 3, device=device, dtype=dtype)
print(a) # Output of tensor A
# Output: tensor([[-1.1884,  0.8498, -1.7129],
#                  [-0.8816,  0.1944,  0.5847]])

# Creation of a tensor and filling of a tensor with random numbers
b = torch.randn(2, 3, device=device, dtype=dtype)
print(b) # Output of tensor B
# Output: tensor([[ 0.7178, -0.8453, -1.3403],
#                  [ 1.3262,  1.1512, -1.7070]])

print(a*b) # Output of a multiplication of the two tensors
# Output: tensor([[-0.8530, -0.7183,  2.58],
#                  [-1.1692,  0.2238, -0.9981]])

print(a.sum()) # Output of the sum of all elements in tensor A
# Output: tensor(-2.1540)

print(a[1,2]) # Output of the element in the third column of the second row
# Output: tensor(0.5847)

print(a.min()) # Output of the minimum value in tensor A
# Output: tensor(-1.7129)

See also[edit]


  1. ^ Chintala, Soumith (1 September 2016). "PyTorch Alpha-1 release".
  2. ^ "New Library Releases in PyTorch 1.10". 21 October 2021. Retrieved 10 November 2021.
  3. ^ Yegulalp, Serdar (19 January 2017). "Facebook brings GPU-powered machine learning to Python". InfoWorld. Retrieved 11 December 2017.
  4. ^ Lorica, Ben (3 August 2017). "Why AI and machine learning researchers are beginning to embrace PyTorch". O'Reilly Media. Retrieved 11 December 2017.
  5. ^ Ketkar, Nikhil (2017). "Introduction to PyTorch". Deep Learning with Python. Apress, Berkeley, CA. pp. 195–208. doi:10.1007/978-1-4842-2766-4_12. ISBN 9781484227657.
  6. ^ "Natural Language Processing (NLP) with PyTorch – NLP with PyTorch documentation". Retrieved 2017-12-18.
  7. ^ Patel, Mo (2017-12-07). "When two trends fuse: PyTorch and recommender systems". O'Reilly Media. Retrieved 2017-12-18.
  8. ^ Mannes, John. "Facebook and Microsoft collaborate to simplify conversions from PyTorch to Caffe2". TechCrunch. Retrieved 2017-12-18. FAIR is accustomed to working with PyTorch – a deep learning framework optimized for achieving state of the art results in research, regardless of resource constraints. Unfortunately in the real world, most of us are limited by the computational capabilities of our smartphones and computers.
  9. ^ Arakelyan, Sophia (2017-11-29). "Tech giants are using open source frameworks to dominate the AI community". VentureBeat. Retrieved 2017-12-18.
  10. ^ "The C++ Frontend". PyTorch Master Documentation. Retrieved 2019-07-29.
  11. ^ Karpathy, Andrej. "PyTorch at Tesla - Andrej Karpathy, Tesla".
  12. ^ "Uber AI Labs Open Sources Pyro, a Deep Probabilistic Programming Language". Uber Engineering Blog. 2017-11-03. Retrieved 2017-12-18.
  13. ^ PYTORCH-TRANSFORMERS: PyTorch implementations of popular NLP Transformers, PyTorch Hub, 2019-12-01, retrieved 2019-12-01
  14. ^ PYTORCH-Lightning: The lightweight PyTorch wrapper for ML researchers. Scale your models. Write less boilerplate, Lightning-Team, 2020-06-18, retrieved 2020-06-18
  15. ^ "Ecosystem Tools". Retrieved 2020-06-18.
  16. ^ GitHub - catalyst-team/catalyst: Accelerated DL & RL, Catalyst-Team, 2019-12-05, retrieved 2019-12-05
  17. ^ "Ecosystem Tools". Retrieved 2020-04-04.
  18. ^ "PyTorch – About". Archived from the original on 2018-06-15. Retrieved 2018-06-11.
  19. ^ "Caffe2 Merges With PyTorch". 2018-04-02.
  20. ^ "An Introduction to PyTorch – A Simple yet Powerful Deep Learning Library". 2018-02-22. Retrieved 2018-06-11.

External links[edit]