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182 lines
7.0 KiB
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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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