Ivy：用于框架间可移植性的模板化深度学习
Ivy: Templated Deep Learning for Inter-Framework Portability
摘要：
我们介绍Ivy，一个模板化的深度学习（DL）框架，它抽象了现有的DL框架。Ivy统一了这些框架的核心功能，以展示一致的调用签名、语法和输入输出行为。新的与框架无关的高级函数和类，可以与特定于框架的代码一起使用，然后可以实现为统一的低级Ivy函数的组合。Ivy目前支持TensorFlow、PyTorch、MXNet、Jax和NumPy。我们还发布了四个pure-Ivy库，用于机械、3D视觉、机器人和可区分环境。通过我们的评估，我们表明Ivy可以显着减少代码行，在大多数情况下运行时开销小于1%。我们欢迎开发人员加入Ivy社区，在Ivy中编写自己的函数、层和库，最大限度地扩大他们的受众，并通过框架间代码库帮助加速DL研究。更多信息可以在https://ivy-dl.org上找到。
英文摘要：
We introduce Ivy, a templated Deep Learning (DL) framework which abstracts existing DL frameworks. Ivy unifies the core functions of these frameworks to exhibit consistent call signatures, syntax and input-output behaviour. New high-level framework-agnostic functions and classes, which are usable alongside framework-specific code, can then be implemented as compositions of the unified low-level Ivy functions. Ivy currently supports TensorFlow, PyTorch, MXNet, Jax and NumPy. We also release four pure-Ivy libraries for mechanics, 3D vision, robotics, and differentiable environments. Through our evaluations, we show that Ivy can significantly reduce lines of code with a runtime overhead of less than 1% in most cases. We welcome developers to join the Ivy community by writing their own functions, layers and libraries in Ivy, maximizing their audience and helping to accelerate DL research through inter-framework codebases. More information can be found at https://ivy-dl.org.
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。
