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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.
When it was first introduced, the name was an acronym for Compute Unified Device Architecture, [4] but Nvidia later dropped the common use of the acronym and now rarely expands it. [5] CUDA is a software layer that gives direct access to the GPU's virtual instruction set and parallel computational elements for the execution of compute kernels. [6]
Wolfram Mathematica 10 [74] and later Wolfram Research: 2014 Proprietary: No Windows, macOS, Linux, Cloud computing: C++, Wolfram Language, CUDA: Wolfram Language: Yes No Yes No Yes Yes [75] Yes Yes Yes Yes [76] Yes Software Creator Initial release Software license [a] Open source Platform Written in Interface OpenMP support OpenCL support CUDA ...
Torch is used by the Facebook AI Research Group, [8] IBM, [9] Yandex [10] and the Idiap Research Institute. [11] Torch has been extended for use on Android [12] [better source needed] and iOS. [13] [better source needed] It has been used to build hardware implementations for data flows like those found in neural networks. [14]
[5] [6] It is free and open-source software released under the Apache License 2.0. It was developed by the Google Brain team for Google's internal use in research and production. [7] [8] [9] The initial version was released under the Apache License 2.0 in 2015. [1] [10] Google released an updated version, TensorFlow 2.0, in September 2019. [11]
Tegra is a system on a chip (SoC) series developed by Nvidia for mobile devices such as smartphones, personal digital assistants, and mobile Internet devices. The Tegra integrates an ARM architecture central processing unit (CPU), graphics processing unit (GPU), northbridge , southbridge , and memory controller onto one package.
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PyTorch Lightning is an open-source Python library that provides a high-level interface for PyTorch, a popular deep learning framework. [1] It is a lightweight and high-performance framework that organizes PyTorch code to decouple research from engineering, thus making deep learning experiments easier to read and reproduce.