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Adding Prototype Tutorials to the site (#1385)
* added prototype tutorials to the site * fixed mobile TOC so that it will show up in left nav Co-authored-by: Brian Johnson <[email protected]>
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index.rst

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:caption: PyTorch Recipes
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See All Recipes <recipes/recipes_index>
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See All Prototype Recipes <prototype/prototype_index>
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.. toctree::
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:maxdepth: 2
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:hidden:
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:caption: Mobile
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beginner/deeplabv3_on_ios.html
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beginner/deeplabv3_on_android.html
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beginner/deeplabv3_on_ios
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beginner/deeplabv3_on_android
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PyTorch Prototype Recipes
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---------------------------------------------
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Prototype features are not available as part of binary distributions like PyPI or Conda (except maybe behind run-time flags). To test these features we would, depending on the feature, recommend building from master or using the nightly wheels that are made available on `pytorch.org <https://pytorch.org>`_.
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*Level of commitment*: We are committing to gathering high bandwidth feedback only on these features. Based on this feedback and potential further engagement between community members, we as a community will decide if we want to upgrade the level of commitment or to fail fast.
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.. raw:: html
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</div>
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</div>
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<div id="tutorial-cards-container">
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<nav class="navbar navbar-expand-lg navbar-light tutorials-nav col-12">
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<div class="tutorial-tags-container">
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<div id="dropdown-filter-tags">
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<div class="tutorial-filter-menu">
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<div class="tutorial-filter filter-btn all-tag-selected" data-tag="all">All</div>
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</div>
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</div>
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</div>
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</nav>
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<hr class="tutorials-hr">
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<div class="row">
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<div id="tutorial-cards">
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<div class="list">
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.. Add prototype tutorial cards below this line
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.. Quantization
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.. customcarditem::
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:header: FX Graph Mode Quantization User Guide
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:card_description: Learn about FX Graph Mode Quantization.
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:image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.PNG
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:link: ../prototype/fx_graph_mode_quant_guide.html
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:tags: FX,Quantization
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.. customcarditem::
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:header: FX Graph Mode Post Training Dynamic Quantization
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:card_description: Learn how to do post training dynamic quantization in graph mode based on torch.fx.
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:image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.PNG
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:link: ../prototype/fx_graph_mode_ptq_dynamic.html
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:tags: FX,Quantization
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.. customcarditem::
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:header: FX Graph Mode Post Training Static Quantization
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:card_description: Learn how to do post training static quantization in graph mode based on torch.fx.
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:image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.PNG
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:link: ../prototype/fx_graph_mode_ptq_static.html
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:tags: FX,Quantization
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.. customcarditem::
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:header: Graph Mode Dynamic Quantization on BERT
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:card_description: Learn how to do post training dynamic quantization with graph mode quantization on BERT models.
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:image: ../_static/img/thumbnails/cropped/graph-mode-dynamic-bert.PNG
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:link: ../prototype/graph_mode_dynamic_bert_tutorial.html
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:tags: Text,Quantization
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.. customcarditem::
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:header: PyTorch Numeric Suite Tutorial
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:card_description: Learn how to use the PyTorch Numeric Suite to support quantization debugging efforts.
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:image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.PNG
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:link: ../prototype/numeric_suite_tutorial.html
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:tags: Debugging,Quantization
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.. Mobile
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.. customcarditem::
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:header: Use iOS GPU in PyTorch
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:card_description: Learn how to run your models on iOS GPU.
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:image: ../_static/img/thumbnails/cropped/ios.PNG
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:link: ../prototype/ios_gpu_workflow.html
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:tags: Mobile
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.. customcarditem::
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:header: Convert MobileNetV2 to NNAPI
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:card_description: Learn how to prepare a computer vision model to use Android’s Neural Networks API (NNAPI).
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:image: ../_static/img/thumbnails/cropped/android.PNG
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:link: ../prototype/nnapi_mobilenetv2.html
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:tags: Mobile
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.. customcarditem::
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:header: PyTorch Vulkan Backend User Workflow
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:card_description: Learn how to use the Vulkan backend on mobile GPUs.
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:image: ../_static/img/thumbnails/cropped/android.PNG
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:link: ../prototype/vulkan_workflow.html
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:tags: Mobile
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.. customcarditem::
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:header: Lite Interpreter Workflow in Android and iOS
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:card_description: Learn how to use the lite interpreter on iOS and Andriod devices.
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:image: ../_static/img/thumbnails/cropped/mobile.PNG
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:link: ../prototype/lite_interpreter.html
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:tags: Mobile
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.. TorchScript
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.. customcarditem::
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:header: Model Freezing in TorchScript
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:card_description: Freezing is the process of inlining Pytorch module parameters and attributes values into the TorchScript internal representation.
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:image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.PNG
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:link: ../prototype/torchscript_freezing.html
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:tags: TorchScript
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.. vmap
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.. customcarditem::
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:header: Using torch.vmap
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:card_description: Learn about torch.vmap, an autovectorizer for PyTorch operations.
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:image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.PNG
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:link: ../prototype/vmap_recipe.html
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:tags: vmap
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.. End of tutorial card section
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.. raw:: html
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</div>
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<div class="pagination d-flex justify-content-center"></div>
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</div>
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</div>
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.. -----------------------------------------
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.. Page TOC
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.. -----------------------------------------
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.. toctree::
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:hidden:
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prototype/fx_graph_mode_quant_guide.html
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prototype/fx_graph_mode_ptq_dynamic.html
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prototype/fx_graph_mode_ptq_static.html
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prototype/graph_mode_dynamic_bert_tutorial.html
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prototype/ios_gpu_workflow.html
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prototype/nnapi_mobilenetv2.html
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prototype/numeric_suite_tutorial.html
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prototype/torchscript_freezing.html
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prototype/vmap_recipe.html
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prototype/vulkan_workflow.html
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prototype/lite_interpreter.html

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