What is the translation of " PYTORCH " in Chinese?

• pytorch

Examples of using Pytorch in English and their translations into Chinese

PyTorch Language Library.
PyTorch语言库.

New projects extend PyTorch.
新项目拓展PyTorch.

Pytorch is evolving fast.
PyTorch也在迅速发展。

Translate ideas into practical code(in frameworks such as PyTorch, Caffe 2).
把想法转化为代码（使用PyTorch、Caffe2等框架）.

PyTorch is completely based on Python.
Locust是完全基于Python.

Using high-level frameworks like Keras, TensorFlow or PyTorch allows us to build very complex models quickly.
比如使用Keras，TensorFlow或PyTorch这样的高级框架，我们可以快速构建非常复杂的模型。

As with PyTorch, requirements depend on your operating system.
与PyTorch一样，要求取决于你的操作系统。

If you're an academic or an engineer who wants an easy-to-learn package to perform these two things, PyTorch is for you.
如果你是学者或工程师，想要简单易学的软件包来完成这两件事，PyTorch是适合你的选择。

PyTorch is based on Torch and was distributed by Facebook as their machine learning framework.
PyTorch基于Torch，由Facebook作为机器学习的框架而分发。

To help accelerate and optimize this process, we're introducing PyTorch 1.0, the next version of our open source AI framework.
为了帮助加速和优化这个过程，我们引入了PyTorch1.0，这是我们开源AI框架的下一个版本。

PyTorch is an open-source machine learning library for Python, based on Torch, used for applications such as natural language processing.
PyTorch是一个基于Torch的Python开源机器学习库，用于自然语言处理等应用程序。

Enabling GPU acceleration is handled implicitly in Keras, while PyTorch requires us to specify when to transfer data between the CPU and GPU.
启用GPU加速在Keras中隐式处理，而PyTorch要求我们指定何时在CPU和GPU之间传输数据。

PyTorch is now the second-fastest-growing open source project on GitHub, with a 2.8x increase in contributors over the past 12 months.
PyTorch现在是GitHub上增长速度第二快的开源项目，在过去的12个月里，贡献者增加了2.8倍。

Enabling GPU acceleration is handled implicitly in Keras, while PyTorch requires us to specify when to transfer data between the CPU and GPU.
GPU加速在Keras中可以进行隐式地处理，而PyTorch需要我们指定何时在CPU和GPU间迁移数据。

Get familiar with DL frameworks/libraries(in my time, it was Theano and Torch, now it's probably PyTorch, TensorFlow, and Keras).
熟悉深度学习框架/库；在我开始学的时候，是Theano和Torch，现在可能是PyTorch、TensorFlow和Keras。

We're lucky that there folks like the Pytorch team that are building the tools that creative practitioners need to rapidly iterate and experiment.
我们很幸运，有像Pytorch团队这样的人正在构建创造性实践者需要快速迭代和试验的工具。

Udacity is partnering with Facebook to give developers access to a free Intro to Deep Learning course, which is taught entirely on PyTorch.
Udacity目前正与Facebook合作，为开发人员提供免费的深度学习入门课程，该课程将完全在PyTorch上讲授。

Caffe2, PyTorch(both Facebook's projects), and Cognitive Toolkit(Microsoft's project) will provide support sometime in September.
Caffe2，PyTorch（Facebook的两个项目）和CognitiveToolkit（微软的项目）将在9月份的某个时候提供支持。

With ONNX, developers can share models among different frameworks, for example, by exporting models built in PyTorch and importing them to Caffe2.
有了ONNX，开发人员可以在不同框架之间共享模型，例如，导出在PyTorch中构建的模型，并将它们导入到Caffe2中。

They have now integrated ONNX into PyTorch 1.0 so that the models can be interoperable with other frameworks and developers can“mix-and-match.”.
他们现在已经将ONNX整合到PyTorch1.0中，以便模型可以与其他框架进行互操作，开发人员可以“混搭”。

Google, Microsoft, NVIDIA, Tesla, and many other technology providers discussed their current and planned integration with PyTorch 1.0 at that event, and both fast.
Google、Microsoft、NVIDIA、Tesla和许多其他技术提供商在那次活动中讨论了他们的现状和与PyTorch1.0的集成计划，fast.

The PyTorch developers and user community answer questions at all hours on the discussion forum, though you should probably check the API documentation first.
PyTorch开发人员和用户社区在讨论论坛上的第一时间回答问题，但你应该首先检查API文档。

The company's AI ecosystem includes three major components: the infrastructure, workflow management software running on top, and the core machine learning frameworks such as PyTorch.
该公司的人工智能生态系统包括三个主要组成部分：基础设施、运行在上面的工作流管理软件，以及像PyTorch这样的核心机器学习框架。

Glow is a single component of Pytorch 1.0, a collection of open-source projects that includes merged Caffe2 and Pytorch frameworks.
Glow是Pytorch1.0的一个组成部分，后者是一个开源项目集，包括合并的Caffe2和Pytorch框架。

Pytorch is an open-source, Python-based scientific computing package that is used to implement Deep Learning techniques and Neural Networks on large datasets.
Pytorch是一个基于Python的开源科学计算软件包，可用于在大型的数据集上实施深度学习技术和神经网络。

They have now integrated ONNX into PyTorch 1.0 so that the models can be interoperable with other frameworks and developers can“mix-and-match.”.
现在，Facebook已经将ONNX整合到PyTorch1.0中，使模型能够与其他框架进行互操作，并且开发人员可以“混合搭配”。

PyTorch improves upon Torch's architectural style and does not have any support for containers which makes the entire deep modeling process easier and transparent.
PyTorch改进了Torch的架构风格，并且没有任何对容器的支持-这使整个深度建模过程更容易和透明。

Overall, PyTorch is targeted at researchers, but it can also be used for prototypes and initial production workloads with the most advanced algorithms available.
总体而言，PyTorch针对的是研究人员，但它也可以用于原型和初始生产工作负载，并提供最先进的算法。

Using Keras and PyTorch in Python, the book focuses on how various deep learning models can be applied to semi-supervised and unsupervised anomaly detection tasks.
本书使用Python中的Keras和PyTorch，重点介绍如何将各种深度学习模型应用于半监督和非监督异常检测任务。

For PyTorch resources, we recommend the official tutorials, which offer a slightly more challenging, comprehensive approach to learning the inner-workings of neural networks.
至于PyTorch资源，我们推荐官方教程，提供了稍微更有挑战性的综合方法来学习神经网络的内在工作原理。