PyTorch

PyTorch
Original author(s)
  • Adam Paszke
  • Sam Gross
  • Soumith Chintala
  • Gregory Chanan
Developer(s)Meta AI
Initial releaseSeptember 2016 (2016-09)[1]
Stable release
2.7.0[2] Edit this on Wikidata / 23 April 2025 (23 April 2025)
Repositorygithub.com/pytorch/pytorch
Written in
Operating system
PlatformIA-32, x86-64, ARM64
Available inEnglish
TypeLibrary for machine learning and deep learning
LicenseBSD-3[3]
Websitepytorch.org

PyTorch is a machine learning library based on the Torch library,[4][5][6] used for applications such as computer vision and natural language processing,[7] originally developed by Meta AI and now part of the Linux Foundation umbrella.[8][9][10][11] It is one of the most popular deep learning frameworks, alongside others such as TensorFlow,[12] offering 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.[13]

A number of pieces of deep learning software are built on top of PyTorch, including Tesla Autopilot,[14] Uber's Pyro,[15] Hugging Face's Transformers,[16][17] and Catalyst.[18][19]

PyTorch provides two high-level features:[20]

  1. ^ Chintala, Soumith (1 September 2016). "PyTorch Alpha-1 release". GitHub. Archived from the original on 29 August 2021. Retrieved 19 August 2020.
  2. ^ "Release 2.7.0". 23 April 2025. Retrieved 27 April 2025.
  3. ^ Claburn, Thomas (12 September 2022). "PyTorch gets lit under The Linux Foundation". The Register. Archived from the original on 18 October 2022. Retrieved 18 October 2022.
  4. ^ Yegulalp, Serdar (19 January 2017). "Facebook brings GPU-powered machine learning to Python". InfoWorld. Archived from the original on 12 July 2018. Retrieved 11 December 2017.
  5. ^ Lorica, Ben (3 August 2017). "Why AI and machine learning researchers are beginning to embrace PyTorch". O'Reilly Media. Archived from the original on 17 May 2019. Retrieved 11 December 2017.
  6. ^ 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.
  7. ^ Moez Ali (June 2023). "NLP with PyTorch: A Comprehensive Guide". datacamp.com. Archived from the original on 1 April 2024. Retrieved 1 April 2024.
  8. ^ Patel, Mo (7 December 2017). "When two trends fuse: PyTorch and recommender systems". O'Reilly Media. Archived from the original on 30 March 2019. Retrieved 18 December 2017.
  9. ^ Mannes, John. "Facebook and Microsoft collaborate to simplify conversions from PyTorch to Caffe2". TechCrunch. Archived from the original on 6 July 2020. Retrieved 18 December 2017. 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.
  10. ^ Arakelyan, Sophia (29 November 2017). "Tech giants are using open source frameworks to dominate the AI community". VentureBeat. Archived from the original on 30 March 2019. Retrieved 18 December 2017.
  11. ^ "PyTorch strengthens its governance by joining the Linux Foundation". pytorch.org. Retrieved 13 September 2022.
  12. ^ "Top 30 Open Source Projects". Open Source Project Velocity by CNCF. Archived from the original on 3 September 2023. Retrieved 12 October 2023.
  13. ^ "The C++ Frontend". PyTorch Master Documentation. Archived from the original on 29 July 2019. Retrieved 29 July 2019.
  14. ^ Karpathy, Andrej (6 November 2019). "PyTorch at Tesla - Andrej Karpathy, Tesla". YouTube. Archived from the original on 24 March 2023. Retrieved 2 June 2020.
  15. ^ "Uber AI Labs Open Sources Pyro, a Deep Probabilistic Programming Language". Uber Engineering Blog. 3 November 2017. Archived from the original on 25 December 2017. Retrieved 18 December 2017.
  16. ^ PYTORCH-TRANSFORMERS: PyTorch implementations of popular NLP Transformers, PyTorch Hub, 1 December 2019, archived from the original on 11 June 2023, retrieved 1 December 2019
  17. ^ "Ecosystem Tools". pytorch.org. Archived from the original on 18 July 2023. Retrieved 18 June 2020.
  18. ^ GitHub - catalyst-team/catalyst: Accelerated DL & RL, Catalyst-Team, 5 December 2019, archived from the original on 22 December 2019, retrieved 5 December 2019
  19. ^ "Ecosystem Tools". pytorch.org. Archived from the original on 18 July 2023. Retrieved 4 April 2020.
  20. ^ "PyTorch – About". pytorch.org. Archived from the original on 15 June 2018. Retrieved 11 June 2018.

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