PyTorch: Difference between revisions

Revision as of 14:58, 18 August 2025

Latest revision as of 20:35, 27 August 2025

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==History==

In 2001, Torch was written and released under a [[GNU General Public License|GPL license]]. It was a machine-learning library written in C++, supporting methods including neural networks, [[Support vector machine|SVMsupport vector machines]] (SVM), [[Hidden Markov model|hidden Markov models]], etc.<ref>[http://torch.ch/torch3/matos/tutorial.pdf "Torch Tutorial", Ronan Collobert, IDIAP, 2002-10-02]</ref><ref>R. Collobert, S. Bengio and J. Mariéthoz. [https://infoscience.epfl.ch/server/api/core/bitstreams/7513f344-91b6-427d-a020-7836b150a150/content Torch: a modular machine learning software library]. Technical Report IDIAP-RR 02-46, IDIAP, 2002. </ref><ref>https://web.archive.org/web/20011031104036/http://www.torch.ch/</ref> It was improved to Torch7 in 2012.<ref>{{Citation |last=Collobert |first=Ronan |title=Implementing Neural Networks Efficiently |date=2012 |work=Neural Networks: Tricks of the Trade: Second Edition |pages=537–557 |editor-last=Montavon |editor-first=Grégoire |url=https://doi.org/10.1007/978-3-642-35289-8_28 |access-date=2025-06-10 |place=Berlin, Heidelberg |publisher=Springer |language=en |doi=10.1007/978-3-642-35289-8_28 |isbn=978-3-642-35289-8 |last2=Kavukcuoglu |first2=Koray |last3=Farabet |first3=Clément |editor2-last=Orr |editor2-first=Geneviève B. |editor3-last=Müller |editor3-first=Klaus-Robert|url-access=subscription }}</ref> Development on Torch ceased in 2018 and was subsumed by the PyTorch project.<ref>[https://github.com/torch/torch7/commit/fd0ee3bbf7bfdd21ab10c5ee70b74afaef9409e1 torch/torch7, Commit fd0ee3b, 2018-07-02]</ref>

Meta (formerly known as Facebook) operates both PyTorch and Convolutional Architecture for Fast Feature Embedding ([[Caffe (software)|Caffe2]]), but models defined by the two frameworks were mutually incompatible. The Open Neural Network Exchange ([[Open Neural Network Exchange|ONNX]]) project was created by Meta and [[Microsoft]] in September 2017 for converting models between frameworks. Caffe2 was merged into PyTorch at the end of March 2018.<ref>{{cite web|url=https://medium.com/@Synced/caffe2-merges-with-pytorch-a89c70ad9eb7|title=Caffe2 Merges With PyTorch|date=2 April 2018|access-date=2 January 2019|archive-date=30 March 2019|archive-url=https://web.archive.org/web/20190330143500/https://medium.com/@Synced/caffe2-merges-with-pytorch-a89c70ad9eb7|url-status=live}}</ref> In September 2022, Meta announced that PyTorch would be governed by the independent PyTorch Foundation, a newly created subsidiary of the [[Linux Foundation]].<ref>{{cite web |url=https://arstechnica.com/information-technology/2022/09/meta-spins-off-pytorch-foundation-to-make-ai-framework-vendor-neutral/ |title=Meta spins off PyTorch Foundation to make AI framework vendor neutral |date=12 September 2022 |website=[[Ars Technica]] |last=Edwards |first=Benj |access-date=13 September 2022 |archive-date=13 September 2022 |archive-url=https://web.archive.org/web/20220913034850/https://arstechnica.com/information-technology/2022/09/meta-spins-off-pytorch-foundation-to-make-ai-framework-vendor-neutral/ |url-status=live }}</ref>

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==PyTorch tensors==

{{main|Tensor (machine learning)}}

PyTorch defines a class called Tensor (<code>torch.Tensor</code>) to store and operate on homogeneous multidimensional rectangular arrays of numbers. PyTorch Tensors are similar to [[NumPy]] Arrays, but can also be operated on by a [[CUDA]]-capable [[Nvidia|NVIDIA]] [[Graphics processing unit|GPU]]. PyTorch has also been developing support for other GPU platforms, for example, AMD's [[ROCm]]<ref>{{cite web|url=https://rocm.docs.amd.com/projects/install-on-linux/en/latest/how-to/3rd-party/pytorch-install.html|title=Installing PyTorch for ROCm|date=9 February 2024|website=rocm.docs.amd.com}}</ref> and Apple's [[Metal (API)|Metal Framework.]]<ref>{{Cite web |title=Introducing Accelerated PyTorch Training on Mac |url=https://pytorch.org/blog/introducing-accelerated-pytorch-training-on-mac/ |access-date=4 June 2022 |website=pytorch.org |language=en |archive-date=29 January 2024 |archive-url=https://web.archive.org/web/20240129141050/https://pytorch.org/blog/introducing-accelerated-pytorch-training-on-mac/ |url-status=live }}</ref>

PyTorch supports various sub-types of Tensors.<ref>{{cite web |url=https://www.analyticsvidhya.com/blog/2018/02/pytorch-tutorial/ |title=An Introduction to PyTorch – A Simple yet Powerful Deep Learning Library |website=analyticsvidhya.com |access-date=11 June 2018 |date=22 February 2018 |archive-date=22 October 2019 |archive-url=https://web.archive.org/web/20191022200858/https://www.analyticsvidhya.com/blog/2018/02/pytorch-tutorial/ |url-status=live }}</ref>