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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 a [[CUDA]]-capable [[Nvidia|NVIDIA]] 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=2024-02-09|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=2022-06-04 |website=pytorch.org |language=en}}</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=2018-06-11|date=2018-02-22 }}</ref>
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