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PyTorch content edits from Bruce (#55)
* Bruce adding markdown and html content * Remove leading slashes in case studies logo * Add full Tensor Comprehensions blog post
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‎_case_studies/nvidia.md

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---
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layout: blog_detail
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title: Salesforce
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logo: assets/images/salesforce.png
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---
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Pushing the state of the art in NLP and Multi-task learning.

‎_case_studies/stanford-university.md

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layout: blog_detail
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title: Stanford University
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logo: assets/images/stanford-university.png
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Using PyTorch's flexibility to efficiently research new algorithmic approaches.

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---
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layout: blog_detail
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title: Udacity
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---
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Educating the next wave of AI Innovators using PyTorch.

‎_ecosystem/allennlp.md

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---
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layout: ecosystem_detail
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title: AllenNLP
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summary: AllenNLP is an open-source research library built on PyTorch for designing and evaluating deep learning models for NLP.
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external: true
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link: https://allennlp.org/
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---
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## PYRO
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layout: ecosystem_detail
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title: ELF
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summary: Extensive, Lightweight, and Flexible platform for game research
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call-to-action: Try via CoLab
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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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## ELF

‎_ecosystem/glow.md

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---
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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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external: true
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link: https://github.com/pytorch/glow
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---

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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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---
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## PYRO
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title: Pyro
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summary: Pyro is a universal probabilistic programming language (PPL) written in Python and supported by PyTorch on the backend.
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external: true
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link: http://pyro.ai/
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---
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## PYRO
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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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external: true
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link: http://tensorly.org/stable/home.html
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## PYRO
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‎_ecosystem/wav2letter.md renamed to ‎_ecosystem/translate.md

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layout: ecosystem_detail
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summary: Simple and efficient end-to-end Automatic Speech Recognition (ASR) system from Facebook AI Research
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call-to-action: Try via CoLab
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title: Translate
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summary: Translate is an open source project based on Facebook's machine translation systems.
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external: true
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link: https://github.com/pytorch/translate
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## Get Started

‎_ecosystem/wsl-embeddings.md

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title: WSL Embeddings
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summary: WSL Embeddings allow developers to experiment with image recognition capabilities based on models trained on large datasets [Coming Soon]
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external: true
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link: https://facebook.ai/developers/tools/wslembeddings
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---

‎_features/cloud-partners.md

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```
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Get up and running quickly with PyTorch through cloud platforms for training and inference.

‎_features/distributed-training.md

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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++.

‎_features/hybrid-front-end.md

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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.

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‎_features/native-onnx-support.md

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Export models in the standard ONNX (Open Neural Network Exchange) format for direct access to ONNX-compatible platforms, runtimes, visualizers, and more.

‎_features/python-first.md

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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.

‎_features/tools-libraries.md

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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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‎_get_started/get-started-locally.md

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‎_get_started/get-started-via-cloud-partners.md

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<p>Cloud platforms provide powerful hardware and infrastructure for training and deploying deep learning models. Select a cloud platform below to get started with PyTorch.</p>
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</div>
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<!--
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‎_includes/footer.html

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<div class="row">
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<div class="col-md-4 text-center">
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<h2>Docs</h2>
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<p>Access comprehensive developer documentation for PyTorch</p>
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<a class="with-right-arrow" href="{{ site.external_urls.docs }}">View Docs</a>
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</div>
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<p>Get in-depth tutorials for beginners and advanced developers</p>
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<a class="with-right-arrow" href="{{ site.baseurl }}/tutorials">View Tutorials</a>
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<li><a href="{{ site.baseurl }}/features">Features</a></li>
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<li><a href="{{ site.external_urls.contributing }}" target="_blank">Contributing</a></li>
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<li class="list-title"><a href="{{ site.baseurl }}/resources">Resources</a></li>
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<li><a href="{{ site.external_urls.tutorials }}" target="_blank">Tutorials</a></li>
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<li><a href="{{ site.external_urls.github_issues }}" target="_blank">Github Issues</a></li>
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<li><a href="{{ site.external_urls.github_issues }}" target="_blank">GitHub Issues</a></li>
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<li class="list-title">Stay Connected</li>
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‎_includes/quick_start_cloud_options.html

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<div class="quick-start-guides">
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<li><a href="https://aws.amazon.com/sagemaker/">Amazon SageMaker</a></li>
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<li><a href="https://aws.amazon.com/machine-learning/amis/">AWS Deep Learning AMIs</a></li>
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<li><a href="https://aws.amazon.com/machine-learning/">Learn More</a></li>
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<div class="cloud-option-body google-cloud">
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Google Cloud Platform
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Google Cloud Platform
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<li><a href="https://azure.microsoft.com/en-us/services/machine-learning-studio/">Azure Machine Learning Studio</a></li>
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<li><a href="https://azure.microsoft.com/en-us/services/virtual-machines/data-science-virtual-machines/">Data Science Virtual Machines</a></li>
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<li><a href="https://notebooks.azure.com/pytorch">Azure Notebooks</a></li>
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<li><a href="https://azure.microsoft.com/en-us/overview/ai-platform/">Learn More</a></li>
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</ul>
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<div class="col-md-12 cloud-option with-right-arrow animated-border" data-toggle="dropdown" data-flip="false">
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‎_includes/quick_start_local.html

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<p>Select your preferences and run the PyTorch install command. Please ensure that you are on the latest pip and numpy
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