May 2025
Intermediate to advanced
310 pages
3h 16m
Chinese
Content preview from PyTorch 口袋参考手册
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第 8 章 PyTorch 生态系统和其他资源
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在前面的章节中，你已经了解了使用 PyTorch 设计和部署深度学习模型所需的一切。您已经学会了如何跨不同平台构建、训练、测试和加速模型，以及如何将这些模型部署到 Cloud 和边缘设备。正如您所看到的，PyTorch 在开发和部署环境中都具有强大的功能，并且具有高度的可扩展性，允许您根据自己的需求进行定制。
在本参考指南的最后，我们将探讨 PyTorch 生态系统、其他支持库和其他资源。PyTorch 生态系统是 PyTorch 最强大的优势之一。它提供了一系列丰富的项目、工具、模型、库和平台，用于探索人工智能并加速人工智能开发。
PyTorch 生态系统包括由研究人员、第三方供应商和 PyTorch 社区创建的项目和库。这些项目得到了很好的维护，并经过了 PyTorch 团队的审核，以确保其质量和实用性。
此外，PyTorch 项目还包括支持特定领域的其他库，包括用于计算机视觉的 Torchvision 和用于 NLP 的 Torchtext。PyTorch 还支持其他软件包，如用于可视化的 TensorBoard，而且还有丰富的学习资源供进一步学习，如 Papers with Code 和 PyTorch Academy。
在本章中，我们将首先概述 PyTorch 生态系统及其支持的项目和工具。然后，我们将深入探讨一些最强大、最流行的资源，并提供有关其用法和 API 的参考资料。最后，我会告诉你如何通过各种教程、书籍、课程和其他培训资源学习更多知识。
让我们先来看看生态系统所能提供的一切。
PyTorch 生态系统
截至 2021 年初，PyTorch 生态系统已拥有 50 多个库和项目，而且这个列表还在不断扩大。其中一些是特定领域的项目，如专门用于计算机视觉或 NLP 解决方案的项目。其他项目，如 PyTorch Lightning 和 fastai，提供了编写简洁代码的框架，而 PySyft 和 Crypten 等项目则支持安全和隐私。还有一些项目支持强化学习、游戏模型、模型可解释性和加速。在本节中，我们将探讨 PyTorch 生态系统中包含的项目。
表 8-1 列出了支持计算机视觉应用的生态系统项目。
| 项目 | 说明 |
|---|---|
火炬视觉 |
PyTorch 的计算机视觉库，提供支持计算机视觉应用的通用变换、模型和实用程序(https://pytorch.tips/torchvision) |
Detectron2 |
Facebook 的异议检测和细分平台(https://pytorch.tips/detectron2) |
图书 |
|
PyTorch3D |
三维计算机视觉可重用组件集(https://pytorch.tips/pytorch3d) |
科尼亚 |
计算机视觉可微分模块库(https://pytorch.tips/kornia) |
莫奈 |
医疗成像中的 Deep Learning 框架(https://pytorch.tips/monai) |
TorchIO |
三维医学图像工具包(https://pytorch.tips/torchio) |
Torchvision 是计算机视觉应用最强大的库之一，包含在 PyTorch 项目中。它也由 PyTorch 开发团队维护。 ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.