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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.
The torch package also simplifies object-oriented programming and serialization by providing various convenience functions which are used throughout its packages. The torch.class(classname, parentclass) function can be used to create object factories ().
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.
Format name Design goal Compatible with other formats Self-contained DNN Model Pre-processing and Post-processing Run-time configuration for tuning & calibration
The Open Neural Network Exchange (ONNX) [ˈɒnɪks] [2] is an open-source artificial intelligence ecosystem [3] of technology companies and research organizations that establish open standards for representing machine learning algorithms and software tools to promote innovation and collaboration in the AI sector.
JAX is a Python library that provides a machine learning framework for transforming numerical functions developed by Google with some contributions from Nvidia. [2] [3] [4] It is described as bringing together a modified version of autograd (automatic obtaining of the gradient function through differentiation of a function) and OpenXLA's XLA (Accelerated Linear Algebra).
The paper was accompanied by a software package written in TensorFlow release on GitHub. [10] It was reimplemented in PyTorch by lucidrains. [11] [12] On December 20, 2021, the LDM paper was published on arXiv, [13] and both Stable Diffusion [14] and LDM [15] repositories were published on GitHub. However, they remained roughly the same.
The source code is licensed under Apache License and available on GitHub. [6] InfoWorld magazine awarded the library "The best machine learning tools" in 2017. [11] along with TensorFlow, Pytorch, XGBoost and 8 other libraries. Kaggle listed CatBoost as one of the most frequently used machine learning (ML) frameworks in the world.