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arXiv 2008.04175cs.LGcs.MSstat.ML
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EagerPy: Writing Code That Works Natively with PyTorch, TensorFlow, JAX, and NumPy

Tübingen AI Center, University of Tübingen(蒂宾根人工智能中心,蒂宾根大学);International Max Planck Research School for Intelligent Systems(国际马克斯·普朗克智能系统研究学院);Bernstein Center for Computational Neuroscience Tübingen(伯恩斯坦计算神经科学中心(蒂宾根))

Jonas Rauber, Matthias Bethge, Wieland Brendel

英文摘要:

EagerPy is a Python framework that lets you write code that automatically works natively with PyTorch, TensorFlow, JAX, and NumPy. Library developers no longer need to choose between supporting just one of these frameworks or reimplementing the library for each framework and dealing with code duplication. Users of such libraries can more easily switch frameworks without being locked in by a specific 3rd party library. Beyond multi-framework support, EagerPy also brings comprehensive type annotations and consistent support for method chaining to any framework. The latest documentation is available online at https://eagerpy.jonasrauber.de and the code can be found on GitHub at https://github.com/jonasrauber/eagerpy.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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