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20181004 PyTorch 1.0 Preview Release- Facebook-s newest Open Source AI.md
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PyTorch 1.0 Preview Release: Facebook’s newest Open Source AI
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======
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Facebook already uses its own Open Source AI, PyTorch quite extensively in its own artificial intelligence projects. Recently, they have gone a league ahead by releasing a pre-release preview version 1.0.
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For those who are not familiar, [PyTorch][1] is a Python-based library for Scientific Computing.
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PyTorch harnesses the [superior computational power of Graphical Processing Units (GPUs)][2] for carrying out complex [Tensor][3] computations and implementing [deep neural networks][4]. So, it is used widely across the world by numerous researchers and developers.
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This new ready-to-use [Preview Release][5] was announced at the [PyTorch Developer Conference][6] at [The Midway][7], San Francisco, CA on Tuesday, October 2, 2018.
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### Highlights of PyTorch 1.0 Release Candidate
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![PyTorhc is Python based open source AI framework from Facebook][8]
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Some of the main new features in the release candidate are:
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#### 1\. JIT
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JIT is a set of compiler tools to bring research close to production. It includes a Python-based language called Torch Script and also ways to make existing code compatible with itself.
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#### 2\. New torch.distributed library: “C10D”
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“C10D” enables asynchronous operation on different backends with performance improvements on slower networks and more.
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#### 3\. C++ frontend (experimental)
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Though it has been specifically mentioned as an unstable API (expected in a pre-release), this is a pure C++ interface to the PyTorch backend that follows the API and architecture of the established Python frontend to enable research in high performance, low latency and C++ applications installed directly on hardware.
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To know more, you can take a look at the complete [update notes][9] on GitHub.
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The first stable version PyTorch 1.0 will be released in summer.
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### Installing PyTorch on Linux
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To install PyTorch v1.0rc0, the developers recommend using [conda][10] while there also other ways to do that as shown on their [local installation page][11] where they have documented everything necessary in detail.
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#### Prerequisites
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* Linux
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* Pip
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* Python
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* [CUDA][12] (For Nvidia GPU owners)
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As we recently showed you [how to install and use Pip][13], let’s get to know how we can install PyTorch with it.
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Note that PyTorch has GPU and CPU-only variants. You should install the one that suits your hardware.
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#### Installing old and stable version of PyTorch
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If you want the stable release (version 0.4) for your GPU, use:
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```
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pip install torch torchvision
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```
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Use these two commands in succession for a CPU-only stable release:
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```
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pip install http://download.pytorch.org/whl/cpu/torch-0.4.1-cp27-cp27mu-linux_x86_64.whl
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pip install torchvision
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```
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#### Installing PyTorch 1.0 Release Candidate
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You install PyTorch 1.0 RC GPU version with this command:
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```
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pip install torch_nightly -f https://download.pytorch.org/whl/nightly/cu92/torch_nightly.html
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```
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If you do not have a GPU and would prefer a CPU-only version, use:
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```
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pip install torch_nightly -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html
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```
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#### Verifying your PyTorch installation
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Startup the python console on a terminal with the following simple command:
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```
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python
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```
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Now enter the following sample code line by line to verify your installation:
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```
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from __future__ import print_function
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import torch
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x = torch.rand(5, 3)
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print(x)
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```
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You should get an output like:
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```
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tensor([[0.3380, 0.3845, 0.3217],
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[0.8337, 0.9050, 0.2650],
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[0.2979, 0.7141, 0.9069],
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[0.1449, 0.1132, 0.1375],
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[0.4675, 0.3947, 0.1426]])
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```
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To check whether you can use PyTorch’s GPU capabilities, use the following sample code:
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```
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import torch
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torch.cuda.is_available()
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```
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The resulting output should be:
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```
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True
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```
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Support for AMD GPUs for PyTorch is still under development, so complete test coverage is not yet provided as reported [here][14], suggesting this [resource][15] in case you have an AMD GPU.
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Lets now look into some research projects that extensively use PyTorch:
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### Ongoing Research Projects based on PyTorch
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* [Detectron][16]: Facebook AI Research’s software system to intelligently detect and classify objects. It is based on Caffe2. Earlier this year, Caffe2 and PyTorch [joined forces][17] to create a Research + Production enabled PyTorch 1.0 we talk about.
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* [Unsupervised Sentiment Discovery][18]: Such methods are extensively used with social media algorithms.
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* [vid2vid][19]: Photorealistic video-to-video translation
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* [DeepRecommender][20] (We covered how such systems work on our past [Netflix AI article][21])
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Nvidia, leading GPU manufacturer covered more on this with their own [update][22] on this recent development where you can also read about ongoing collaborative research endeavours.
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### How should we react to such PyTorch capabilities?
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To think Facebook applies such amazingly innovative projects and more in its social media algorithms, should we appreciate all this or get alarmed? This is almost [Skynet][23]! This newly improved production-ready pre-release of PyTorch will certainly push things further ahead! Feel free to share your thoughts with us in the comments below!
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--------------------------------------------------------------------------------
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via: https://itsfoss.com/pytorch-open-source-ai-framework/
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作者：[Avimanyu Bandyopadhyay][a]
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选题：[lujun9972](https://github.com/lujun9972)
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译者：[译者ID](https://github.com/译者ID)
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校对：[校对者ID](https://github.com/校对者ID)
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本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
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[a]: https://itsfoss.com/author/avimanyu/
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[1]: https://pytorch.org/
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[2]: https://en.wikipedia.org/wiki/General-purpose_computing_on_graphics_processing_units
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[3]: https://en.wikipedia.org/wiki/Tensor
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[4]: https://www.techopedia.com/definition/32902/deep-neural-network
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[5]: https://code.fb.com/ai-research/facebook-accelerates-ai-development-with-new-partners-and-production-capabilities-for-pytorch-1-0
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[6]: https://pytorch.fbreg.com/
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[7]: https://www.themidwaysf.com/
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[8]: https://4bds6hergc-flywheel.netdna-ssl.com/wp-content/uploads/2018/10/pytorch.jpeg
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[9]: https://github.com/pytorch/pytorch/releases/tag/v1.0rc0
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[10]: https://conda.io/
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[11]: https://pytorch.org/get-started/locally/
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[12]: https://www.pugetsystems.com/labs/hpc/How-to-install-CUDA-9-2-on-Ubuntu-18-04-1184/
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[13]: https://itsfoss.com/install-pip-ubuntu/
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[14]: https://github.com/pytorch/pytorch/issues/10657#issuecomment-415067478
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[15]: https://rocm.github.io/install.html#installing-from-amd-rocm-repositories
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[16]: https://github.com/facebookresearch/Detectron
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[17]: https://caffe2.ai/blog/2018/05/02/Caffe2_PyTorch_1_0.html
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[18]: https://github.com/NVIDIA/sentiment-discovery
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[19]: https://github.com/NVIDIA/vid2vid
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[20]: https://github.com/NVIDIA/DeepRecommender/
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[21]: https://itsfoss.com/netflix-open-source-ai/
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[22]: https://news.developer.nvidia.com/pytorch-1-0-accelerated-on-nvidia-gpus/
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[23]: https://en.wikipedia.org/wiki/Skynet_(Terminator)
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@ -0,0 +1,174 @@
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PyTorch 1.0 预览版发布: Facebook 最新 AI 开源框架
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======
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Facebook 在人工智能项目中广泛使用自己的开源 AI 框架 PyTorch，最近，他们已经发布了 PyTorch 1.0 的预览版本。
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对于那些不熟悉的人， [PyTorch][1] 是一个基于 Python 的科学计算库。
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PyTorch 利用 [GPUs 超强的运算能力 ][2] 来实现复杂的 [张量][3] 计算 和 [深度神经网络][4]。 因此， 它被世界各地的研究人员和开发人员广泛使用。
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这一新的能够使用的 [预览版][5] 已在2018年10月2日周二旧金山举办的 [PyTorch 开发人员大会][6] 的[中途][7]宣布。
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### PyTorch 1.0 候选版本的亮点
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![PyTorhc is Python based open source AI framework from Facebook][8]
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候选版本中的一些主要新功能包括:
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#### 1\. JIT
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JIT 是一个编译工具集，使研究和生产更加接近。 它包含一个基于 Python 语言的叫做 Torch Script 的脚本语言，也有能使现有代码与它自己兼容的方法。
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#### 2\. 全新的 torch.distributed 库: “C10D”
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“C10D” 能够在不同的后端上启用异步操作， 并在较慢的网络上提高性能。
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#### 3\. C++ 前端 (实验性功能)
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虽然它被特别提到是一个不稳定的 API (预计在预发行版中)， 这是一个 PyTorch 后端的纯 c++ 接口， 遵循 API 和建立的 Python 前端的体系结构，以实现高性能、 低延迟的研究和开发直接安装在硬件上的 c++ 应用程序。
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想要了解更多，可以在 GitHub 上查看完整的 [更新说明][9]。
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第一个PyTorch 1.0 的稳定版本将在夏季发布。
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### 在 Linux 上安装 PyTorch
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为了安装 PyTorch v1.0rc0， 开发人员建议使用 [conda][10]， 同时也可以按照[本地安装][11]所示，使用其他方法可以安装，所有必要的细节详见文档。
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#### 前提
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* Linux
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* Pip
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* Python
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* [CUDA][12] (对于使用 Nvidia GPU 的用户)
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我们已经知道[如何安装和使用 Pip][13]，那就让我们来了解如何使用 Pip 安装 PyTorch。
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请注意，PyTorch 具有 GPU 和仅限 CPU 的不同安装包。你应该安装一个适合你硬件的安装包。
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#### 安装 PyTorch 的旧版本和稳定版
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如果你想在 GPU 机器上安装稳定版（0.4 版本），使用：
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```
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pip install torch torchvision
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```
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使用以下两个命令，来安装仅用于 CPU 的稳定版：
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```
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pip install http://download.pytorch.org/whl/cpu/torch-0.4.1-cp27-cp27mu-linux_x86_64.whl
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pip install torchvision
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```
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#### 安装 PyTorch 1.0 候选版本
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使用如下命令安装 PyTorch 1.0 RC GPU 版本：
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```
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pip install torch_nightly -f https://download.pytorch.org/whl/nightly/cu92/torch_nightly.html
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```
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如果没有GPU，并且更喜欢使用 仅限CPU 版本，使用如下命令：
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```
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pip install torch_nightly -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html
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```
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#### 验证 PyTorch 安装
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使用如下简单的命令，启动终端上的 python 控制台：
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```
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python
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```
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现在，按行输入下面的示例代码以验证您的安装：
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```
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from __future__ import print_function
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import torch
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x = torch.rand(5, 3)
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print(x)
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```
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你应该得到如下输出：
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```
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tensor([[0.3380, 0.3845, 0.3217],
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[0.8337, 0.9050, 0.2650],
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[0.2979, 0.7141, 0.9069],
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[0.1449, 0.1132, 0.1375],
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[0.4675, 0.3947, 0.1426]])
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```
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若要检查是否可以使用 PyTorch 的 GPU 功能， 可以使用以下示例代码:
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```
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import torch
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torch.cuda.is_available()
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```
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输出结果应该是：
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```
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True
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```
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支持 PyTorch 的 AMD GPU 仍在开发中， 因此, 尚未按[报告][14]提供完整的测试覆盖，如果您有 AMD GPU ，请在[这里][15]提出建议。
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现在让我们来看看一些广泛使用 PyTorch 的研究项目：
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### 基于 PyTorch 的持续研究项目
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* [Detectron][16]: Facebook AI 研究院的软件系统, 可以智能地进行对象检测和分类。它之前是基于 Caffe2 的。今年早些时候，Caffe2 和 PyTorch [合力][17]创建了一个研究 + 生产的 PyTorch 1.0
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* [Unsupervised Sentiment Discovery][18]: 广泛应用于社交媒体的一些算法
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* [vid2vid][19]: 逼真的视频到视频的转换
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* [DeepRecommender][20] 我们在过去的[网飞的 AI 文章][21]中介绍了这些系统是如何工作的
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领先的 GPU 制造商英伟达在[更新][22]这方面最近的发展，你也可以阅读正在进行的合作的研究。
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### 我们应该如何应对这种 PyTorch 的能力？
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想到 Facebook 在社交媒体算法中应用如此令人惊叹的创新项目， 我们是否应该感激这一切或是感到惊恐？这几乎是[天网][23]! 这一新改进的发布的 PyTorch 肯定会推动事情进一步向前! 在下方评论，随时与我们分享您的想法!
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--------------------------------------------------------------------------------
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||||
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||||
via: https://itsfoss.com/pytorch-open-source-ai-framework/
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|
||||
作者：[Avimanyu Bandyopadhyay][a]
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选题：[lujun9972](https://github.com/lujun9972)
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译者：[distant1219](https://github.com/distant1219)
|
||||
校对：[校对者ID](https://github.com/校对者ID)
|
||||
|
||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
|
||||
|
||||
[a]: https://itsfoss.com/author/avimanyu/
|
||||
[1]: https://pytorch.org/
|
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[2]: https://en.wikipedia.org/wiki/General-purpose_computing_on_graphics_processing_units
|
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[3]: https://en.wikipedia.org/wiki/Tensor
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[4]: https://www.techopedia.com/definition/32902/deep-neural-network
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[5]: https://code.fb.com/ai-research/facebook-accelerates-ai-development-with-new-partners-and-production-capabilities-for-pytorch-1-0
|
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[6]: https://pytorch.fbreg.com/
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[7]: https://www.themidwaysf.com/
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[8]: https://4bds6hergc-flywheel.netdna-ssl.com/wp-content/uploads/2018/10/pytorch.jpeg
|
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[9]: https://github.com/pytorch/pytorch/releases/tag/v1.0rc0
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[10]: https://conda.io/
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[11]: https://pytorch.org/get-started/locally/
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[12]: https://www.pugetsystems.com/labs/hpc/How-to-install-CUDA-9-2-on-Ubuntu-18-04-1184/
|
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[13]: https://itsfoss.com/install-pip-ubuntu/
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[14]: https://github.com/pytorch/pytorch/issues/10657#issuecomment-415067478
|
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[15]: https://rocm.github.io/install.html#installing-from-amd-rocm-repositories
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[16]: https://github.com/facebookresearch/Detectron
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[17]: https://caffe2.ai/blog/2018/05/02/Caffe2_PyTorch_1_0.html
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[18]: https://github.com/NVIDIA/sentiment-discovery
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[19]: https://github.com/NVIDIA/vid2vid
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[20]: https://github.com/NVIDIA/DeepRecommender/
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[21]: https://itsfoss.com/netflix-open-source-ai/
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[22]: https://news.developer.nvidia.com/pytorch-1-0-accelerated-on-nvidia-gpus/
|
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[23]: https://en.wikipedia.org/wiki/Skynet_(Terminator)
|
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