PyTorch

PyTorch is an open source machine learning library based on the Torch library,[1][2][3] used for applications such as computer vision and natural language processing,[4] primarily developed by Facebook's AI Research lab (FAIR).[5][6][7] 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.[8]

PyTorch
Original author(s)Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan
Developer(s)Facebook's AI Research lab (FAIR)
Initial releaseOctober 2016 (2016-10)
Stable release
1.6.0 / 28 July 2020 (2020-07-28)
Repositorygithub.com/pytorch/pytorch
Written inPython, C++, CUDA
Operating systemLinux, macOS, Windows
PlatformIA-32, x86-64
Available inEnglish
TypeLibrary for machine learning and deep learning
LicenseBSD
Websitepytorch.org

A number of pieces of Deep Learning software are built on top of PyTorch, including Tesla Autopilot[9], Uber's Pyro,[10] HuggingFace's Transformers,[11] PyTorch Lightning[12][13], and Catalyst.[14][15]

PyTorch provides two high-level features:[16]

History

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.[17]

PyTorch tensors

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.[18]

Modules

Autograd module

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

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

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.

gollark: Slices have length.
gollark: Oh, it also knows that because things actually have length on them in good languages.
gollark: It doesn't. It reads nbyte bytes and says how much it did. This is common.
gollark: I'm considering rewriting it in Rust again as it will never achieve completion anyway.
gollark: Distributed systems are hard.

See also

References
  1. Yegulalp, Serdar (19 January 2017). "Facebook brings GPU-powered machine learning to Python". InfoWorld. Retrieved 11 December 2017.
  2. Lorica, Ben (3 August 2017). "Why AI and machine learning researchers are beginning to embrace PyTorch". O'Reilly Media. Retrieved 11 December 2017.
  3. 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.
  4. "Natural Language Processing (NLP) with PyTorch – NLP with PyTorch documentation". dl4nlp.info. Retrieved 2017-12-18.
  5. Patel, Mo (2017-12-07). "When two trends fuse: PyTorch and recommender systems". O'Reilly Media. Retrieved 2017-12-18.
  6. 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.
  7. Arakelyan, Sophia (2017-11-29). "Tech giants are using open source frameworks to dominate the AI community". VentureBeat. Retrieved 2017-12-18.
  8. "The C++ Frontend". PyTorch Master Documentation. Retrieved 2019-07-29.
  9. Karpathy, Andrej. "PyTorch at Tesla - Andrej Karpathy, Tesla".
  10. "Uber AI Labs Open Sources Pyro, a Deep Probabilistic Programming Language". Uber Engineering Blog. 2017-11-03. Retrieved 2017-12-18.
  11. PYTORCH-TRANSFORMERS: PyTorch implementations of popular NLP Transformers, PyTorch Hub, 2019-12-01, retrieved 2019-12-01
  12. PYTORCH-Lightning: The lightweight PyTorch wrapper for ML researchers. Scale your models. Write less boilerplate, Lightning-Team, 2020-06-18, retrieved 2020-06-18
  13. "Ecosystem Tools". pytorch.org. Retrieved 2020-06-18.
  14. GitHub - catalyst-team/catalyst: Accelerated DL & RL, Catalyst-Team, 2019-12-05, retrieved 2019-12-05
  15. "Ecosystem Tools". pytorch.org. Retrieved 2020-04-04.
  16. "PyTorch – About". pytorch.org. Archived from the original on 2018-06-15. Retrieved 2018-06-11.
  17. "Caffe2 Merges With PyTorch". 2018-04-02.
  18. "An Introduction to PyTorch – A Simple yet Powerful Deep Learning Library". analyticsvidhya.com. 2018-02-22. Retrieved 2018-06-11.


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