什么是pytorch

Pytorch is an open-source, Python-based machine and deep learning framework, which is being widely used for several natural language processing and computer vision applications. PyTorch was developed by Facebook’s AI Research and is adapted by several industries like Uber, Twitter, Salesforce, and NVIDIA.

Pytorch是一个基于Python的开源机器和深度学习框架，已被广泛用于多种自然语言处理和计算机视觉应用程序。 PyTorch由Facebook的AI Research开发，并被Uber，Twitter，Salesforce和NVIDIA等多个行业改编。

PyTorch的历史 (History of PyTorch)

PyTorch derives its current form from two sources. The first being Torch, a machine learning library developed in Lua language, dating back to 2002. Torch is no longer active and has been completely taken over by PyTorch as of now. The second source of PyTorch is the Chainer framework, developed in Japan in 2015, that uses NumPy like tensor structures for computations and an eager approach to auto differentiation. Both these features have been actively adopted by the PyTorch framework.

PyTorch的当前形式来自两个来源。 第一个是Torch ，它是使用Lua语言开发的机器学习库，最早可以追溯到2002年。Torch不再活跃，并且到现在为止已被PyTorch完全接管。 PyTorch的第二个来源是Chainer框架，日本在2015年开发的，使用NumPy的像计算张量结构和渴望的方法来自动分化。 这两个功能已被PyTorch框架积极采用。

Another independent framework developed by Facebook known as Caffe2 (Convolutional Architecture for Fast Feature Embedding) has later been merged into PyTorch.

由Facebook开发的另一个独立框架，称为Caffe2 （ 用于快速特征嵌入的卷积架构 ），后来被合并到PyTorch中。

PyTorch的功能 (Features of PyTorch)

• Versatile Collection of Modules: PyTorch comes with several specially developed modules like torchtext, torchvision, and torchaudio to work with different areas of deep learning like NLP, computer vision and speech processing.
  多功能的模块集合： PyTorch带有几个特别开发的模块，例如torchtext ， torrvision和torchaudio ，可与NLP，计算机视觉和语音处理等不同深度学习领域一起使用。
• Numpy friendly: PyTorch works with NumPy like tensor structures for its computations which are all GPU compatible.
  NumPy 友好： PyTorch像张量结构一样与NumPy一起使用，它们的计算都与GPU兼容 。
• Easy to implement backpropagation: PyTorch supports auto-differentiation i.e. it greatly simplifies the way in which complex calculations like backpropagation are handled by recording the operations performed on a variable and runs them backward. This proves to be effective in saving time and also takes the burden off the programmers’ backs.
  易于实现反向传播： PyTorch支持自动微分，即它通过记录对变量执行的操作并向后运行，大大简化了诸如反向传播之类的复杂计算的处理方式。 事实证明，这样做可以节省时间，并且可以减轻程序员的负担。
• More Pythonic: PyTorch is considered more Pythonic by several developers since it supports dynamically making changes to your code.
  更多Pythonic： PyTorch被一些开发人员视为更Pythonic，因为它支持动态地更改您的代码。
• Flexible, pain-less debugging: PyTorch doesn’t require you to define the entire graph a priori. It runs with an imperative paradigm, meaning that each line of code adds a certain component to the graph, and each component can be run, tested and debugged independently of the complete graph structure, which makes it very flexible.
  灵活，轻松的调试： PyTorch不需要先验定义整个图形。 它以命令式范式运行，这意味着每行代码都会向图添加一个特定的组件，并且每个组件都可以独立于完整的图结构运行，测试和调试，这使其非常灵活。

与Tensorflow的比较 (Comparison to Tensorflow)

Though Google’s Tensorflow is already a well-established ML/DL framework with several faithful supporters, PyTorch has found its stronghold due to its dynamic graph approach and flexible debugging strategy. PyTorch has several researchers actively supporting it due to these reasons. In the year 2018-19, it was observed that research papers mentioning PyTorch have doubled in number.

尽管Google的Tensorflow已经是一个建立完善的ML / DL框架，拥有多个忠实的支持者，但PyTorch凭借其动态图方法和灵活的调试策略而找到了据点。 由于这些原因，PyTorch的研究人员积极支持它。 在2018-19年度，据观察，提到PyTorch的研究论文数量增加了一倍。

Tensorflow 2.0 has introduced an eager execution paradigm for dynamic graph definitions in similar lines to PyTorch. However, the resources to help you learn this feature are still sparse. Though Tensorflow is often touted as the industry strength ML/DL library, PyTorch still continues to rise, owing to its gentler learning curves for newcomers.

Tensorflow 2.0在与PyTorch相似的行中引入了针对动态图定义的急切执行范例。 但是，帮助您学习此功能的资源仍然很少。 尽管Tensorflow通常被吹捧为具有行业实力的ML / DL库，但PyTorch仍在继续增长，因为它为新手提供的学习曲线较为温和。

This tutorial series aims to equip you with all the necessary skills you need to start developing and training your own neural networks with PyTorch.

本教程系列旨在为您提供开始使用PyTorch开发和训练自己的神经网络所需的所有必要技能。

So bookmark the PyTorch page keep a watch on all the new topics that will be covered in the future.

因此，在PyTorch页面上添加书签， 可随时关注将来将涉及的所有新主题。

翻译自: https://www.journaldev.com/35641/what-is-pytorch

什么是pytorch