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Merge remote-tracking branch 'origin/brucelin-edits'
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‎_ecosystem/elf.md

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summary: ELF is a platform for game research that allows developers to train and test their algorithms in various game environments.
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external: true
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link: https://github.com/pytorch/elf
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logo-class: elf
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logo-class: reasoning
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summary-home: ELF is a platform for game research that allows developers to train and test their algorithms in various game environments.
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‎_ecosystem/glow.md

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layout: ecosystem_detail
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title: Glow
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summary: Glow is a machine learning compiler that accelerates the performance of deep learning frameworks on different hardware platforms.
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summary: Glow is a ML compiler that accelerates the performance of deep learning frameworks on different hardware platforms.
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external: true
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link: https://github.com/pytorch/glow
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order: 3
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summary-home: Glow is a machine learning compiler that accelerates the performance of deep learning frameworks on different hardware platforms.
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summary-home: Glow is a ML compiler that accelerates the performance of deep learning frameworks on different hardware platforms.
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featured-home: true
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logo-class: tool
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‎_ecosystem/gpytorch.md

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title: GPyTorch
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summary: GPyTorch is a Gaussian process library implemented using PyTorch, designed for creating scalable, flexible Gaussian process models.
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external: true
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link: http://pyro.ai/
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link: https://cornellius-gp.github.io/
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‎_ecosystem/tensorly.md

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title: TensorLy
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summary: TensorLy is a Python library that makes tensor learning simple and accessible.
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summary: TensorLy is a high level API for tensor methods and deep tensorized neural networks in Python that aims to make tensor learning simple.
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external: true
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link: http://tensorly.org/stable/home.html
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‎_ecosystem/translate.md

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link: https://github.com/pytorch/translate
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logo-class: language
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## Get Started

‎_ecosystem/wsl-embeddings.md

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external: true
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link: https://facebook.ai/developers/tools/wslembeddings
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logo-class: vision
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‎_features/cloud-partners.md

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print()
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```
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summary-home: PyTorch is well supported on major cloud platforms, providing frictionless development and easy scaling.
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Get up and running quickly with PyTorch through cloud platforms for training and inference.
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PyTorch is well supported on major cloud platforms, providing frictionless development and easy scaling through prebuilt images, large scale training on GPUs, ability to run models in a production scale environment, and more.

‎_features/distributed-training.md

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```
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summary-home: Take advantage of native support for asynchronous execution of collective operations and peer-to-peer communication that is accessible from both Python and C++.
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summary-home: Scalable distributed training and performance optimization in research and production is enabled by the torch.distributed backend.
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Take advantage of native support for asynchronous execution of collective operations and peer-to-peer communication that is accessible from both Python and C++.
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Optimize performance in both research and production by taking advantage of native support for asynchronous execution of collective operations and peer-to-peer communication that is accessible from Python and C++.

‎_features/hybrid-front-end.md

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```
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summary-home: A new hybrid front-end provides ease-of-use and flexibility in eager mode, while seamlessly transitioning to graph mode for speed, optimization, and functionality in C++ runtime environments.
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summary-home: A new hybrid front-end seamlessly transitions between eager mode and graph mode to provide both flexibility and speed.
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A new hybrid front-end provides ease-of-use and flexibility in eager mode, while seamlessly transitioning to graph mode for speed, optimization, and functionality in C++ runtime environments.

‎_features/python-first.md

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```
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summary-home: Deep integration into Python allows popular libraries and packages to be used, while a new pure C++ interface (beta) enables performance-critical research.
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summary-home: Deep integration into Python allows popular libraries and packages to be used for easily writing neural network layers in Python.
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Deep integration into Python allows popular libraries and packages to be used, while a new pure C++ interface (beta) enables performance-critical research.
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PyTorch is not a Python binding into a monolithic C++ framework. It's built to be deeply integrated into Python so it can be used with popular libraries and packages such as Cython and Numba.

‎_features/tools-libraries.md

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```
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summary-home: Access a rich ecosystem of tools and libraries to extend PyTorch and support development in areas from computer vision to reinforcement learning.
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summary-home: A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP and more.
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Access a rich ecosystem of tools and libraries to extend PyTorch and support development in areas from computer vision to reinforcement learning.
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An active community of researchers and developers have built a rich ecosystem of tools and libraries for extending PyTorch and supporting development in areas from computer vision to reinforcement learning.

‎_includes/quick_start_cloud_options.html

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</div>
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<ul>
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<li><a href="https://cloud.google.com/deep-learning-vm-image/">Cloud Deep Learning VM Image</a></li>
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<li><a href="https://cloud.google.com/deep-learning-vm/docs/pytorch_start_instance">Cloud Deep Learning VM Image</a></li>
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</ul>
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</div>
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‎_resources/docs.md

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title: Docs
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summary: Access comprehensive developer documentation.
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summary-home: Access comprehensive developer documentation.
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class: pytorch-resource
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link: https://shiftlab.github.io/pytorch_docs
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link: https://pytorch.org/docs/
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‎_resources/pytorch-discuss.md

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title: PyTorch Discuss
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summary-home: Browse and join discussions on deep learning with PyTorch.
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summary: Browse and join discussions on deep learning with PyTorch.
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link: https://discuss.pytorch.org
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‎_resources/slack.md

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title: Slack
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summary: Get your questions answered and collaborate. Request access
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summary-home: Get questions answered. Email slack@pytorch.org for access.
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summary: Get questions answered. Email slack@pytorch.org for access.
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class: slack
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link: https://pytorch.slack.com
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‎_resources/tutorials.md

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title: Tutorials
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summary: Get in-depth tutorials for beginners and advanced developers.
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summary-home: Get in-depth tutorials for beginners and advanced developers.
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link: https://pytorch.org/tutorials
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‎_sass/base_styles.scss

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‎resources.html

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<div class="container">
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<h1>Resources</h1>
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<p class="lead">Explore educational courses, get your questions answered, and join the discussion with other PyTorch developers.</p>
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