From 79828ebb599bd026ab9d5d51db10652fbcbb489d Mon Sep 17 00:00:00 2001 From: distant1219 Date: Tue, 16 Oct 2018 11:07:09 +0800 Subject: [PATCH] Delete 20181004 PyTorch 1.0 Preview Release- Facebook-s newest Open Source AI.md finished translate and delete source --- ...lease- Facebook-s newest Open Source AI.md | 182 ------------------ 1 file changed, 182 deletions(-) delete mode 100644 sources/tech/20181004 PyTorch 1.0 Preview Release- Facebook-s newest Open Source AI.md diff --git a/sources/tech/20181004 PyTorch 1.0 Preview Release- Facebook-s newest Open Source AI.md b/sources/tech/20181004 PyTorch 1.0 Preview Release- Facebook-s newest Open Source AI.md deleted file mode 100644 index 08551028b2..0000000000 --- a/sources/tech/20181004 PyTorch 1.0 Preview Release- Facebook-s newest Open Source AI.md +++ /dev/null @@ -1,182 +0,0 @@ -distant1219 is translating -PyTorch 1.0 Preview Release: Facebook’s newest Open Source AI -====== -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. - -For those who are not familiar, [PyTorch][1] is a Python-based library for Scientific Computing. - -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. - -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. - -### Highlights of PyTorch 1.0 Release Candidate - -![PyTorhc is Python based open source AI framework from Facebook][8] - -Some of the main new features in the release candidate are: - -#### 1\. JIT - -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. - -#### 2\. New torch.distributed library: “C10D” - -“C10D” enables asynchronous operation on different backends with performance improvements on slower networks and more. - -#### 3\. C++ frontend (experimental) - -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. - -To know more, you can take a look at the complete [update notes][9] on GitHub. - -The first stable version PyTorch 1.0 will be released in summer. - -### Installing PyTorch on Linux - -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. - -#### Prerequisites - - * Linux - * Pip - * Python - * [CUDA][12] (For Nvidia GPU owners) - - - -As we recently showed you [how to install and use Pip][13], let’s get to know how we can install PyTorch with it. - -Note that PyTorch has GPU and CPU-only variants. You should install the one that suits your hardware. - -#### Installing old and stable version of PyTorch - -If you want the stable release (version 0.4) for your GPU, use: - -``` -pip install torch torchvision - -``` - -Use these two commands in succession for a CPU-only stable release: - -``` -pip install http://download.pytorch.org/whl/cpu/torch-0.4.1-cp27-cp27mu-linux_x86_64.whl -pip install torchvision - -``` - -#### Installing PyTorch 1.0 Release Candidate - -You install PyTorch 1.0 RC GPU version with this command: - -``` -pip install torch_nightly -f https://download.pytorch.org/whl/nightly/cu92/torch_nightly.html - -``` - -If you do not have a GPU and would prefer a CPU-only version, use: - -``` -pip install torch_nightly -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html - -``` - -#### Verifying your PyTorch installation - -Startup the python console on a terminal with the following simple command: - -``` -python - -``` - -Now enter the following sample code line by line to verify your installation: - -``` -from __future__ import print_function -import torch -x = torch.rand(5, 3) -print(x) - -``` - -You should get an output like: - -``` -tensor([[0.3380, 0.3845, 0.3217], - [0.8337, 0.9050, 0.2650], - [0.2979, 0.7141, 0.9069], - [0.1449, 0.1132, 0.1375], - [0.4675, 0.3947, 0.1426]]) - -``` - -To check whether you can use PyTorch’s GPU capabilities, use the following sample code: - -``` -import torch -torch.cuda.is_available() - -``` - -The resulting output should be: - -``` -True - -``` - -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. - -Lets now look into some research projects that extensively use PyTorch: - -### Ongoing Research Projects based on PyTorch - - * [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. - * [Unsupervised Sentiment Discovery][18]: Such methods are extensively used with social media algorithms. - * [vid2vid][19]: Photorealistic video-to-video translation - * [DeepRecommender][20] (We covered how such systems work on our past [Netflix AI article][21]) - - - -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. - -### How should we react to such PyTorch capabilities? - -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! - --------------------------------------------------------------------------------- - -via: https://itsfoss.com/pytorch-open-source-ai-framework/ - -作者：[Avimanyu Bandyopadhyay][a] -选题：[lujun9972](https://github.com/lujun9972) -译者：[译者ID](https://github.com/译者ID) -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/ -[2]: https://en.wikipedia.org/wiki/General-purpose_computing_on_graphics_processing_units -[3]: https://en.wikipedia.org/wiki/Tensor -[4]: https://www.techopedia.com/definition/32902/deep-neural-network -[5]: https://code.fb.com/ai-research/facebook-accelerates-ai-development-with-new-partners-and-production-capabilities-for-pytorch-1-0 -[6]: https://pytorch.fbreg.com/ -[7]: https://www.themidwaysf.com/ -[8]: https://4bds6hergc-flywheel.netdna-ssl.com/wp-content/uploads/2018/10/pytorch.jpeg -[9]: https://github.com/pytorch/pytorch/releases/tag/v1.0rc0 -[10]: https://conda.io/ -[11]: https://pytorch.org/get-started/locally/ -[12]: https://www.pugetsystems.com/labs/hpc/How-to-install-CUDA-9-2-on-Ubuntu-18-04-1184/ -[13]: https://itsfoss.com/install-pip-ubuntu/ -[14]: https://github.com/pytorch/pytorch/issues/10657#issuecomment-415067478 -[15]: https://rocm.github.io/install.html#installing-from-amd-rocm-repositories -[16]: https://github.com/facebookresearch/Detectron -[17]: https://caffe2.ai/blog/2018/05/02/Caffe2_PyTorch_1_0.html -[18]: https://github.com/NVIDIA/sentiment-discovery -[19]: https://github.com/NVIDIA/vid2vid -[20]: https://github.com/NVIDIA/DeepRecommender/ -[21]: https://itsfoss.com/netflix-open-source-ai/ -[22]: https://news.developer.nvidia.com/pytorch-1-0-accelerated-on-nvidia-gpus/ -[23]: https://en.wikipedia.org/wiki/Skynet_(Terminator)