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1 | 1 | Summary of PyTorch Mobile Recipes
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2 | 2 | =====================================
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| -.. warning:: |
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| - Note: PyTorch Mobile is no longer actively supported. Please check out `ExecuTorch <https://pytorch.org/executorch-overview>`_, PyTorch’s all-new on-device inference library. You can also review these `ExecuTorch examples <https://github.com/pytorch/executorch/tree/main/examples#readme>`_. |
| 4 | +This tutorial has been deprecated. There is a new tutorial on this topic. |
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| -This summary provides a top level overview of recipes for PyTorch Mobile to help developers choose which recipes to follow for their PyTorch-powered mobile app development. |
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| -Introduction |
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| ----------------- |
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| -When a PyTorch model is trained or retrained, or when a pre-trained model is available, for mobile deployment, follow the the recipes outlined in this summary so mobile apps can successfully use the model. |
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| - |
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| -Pre-requisites |
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| ----------------- |
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| - |
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| -PyTorch 1.6.0 or 1.7.0 |
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| - |
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| -(Optional) torchvision 0.6.0 or 0.7.0 |
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| - |
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| -For iOS development: Xcode 11 or 12 |
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| - |
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| -For Android development: Android Studio 3.5.1 or above (with NDK installed); or Android SDK, NDK, Gradle, JDK. |
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| - |
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| -New Recipes for PyTorch Mobile |
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| --------------------------------- |
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| - |
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| -* (Recommended) To fuse a list of PyTorch modules into a single module to reduce the model size before quantization, read the `Fuse Modules recipe <fuse.html>`_. |
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| - |
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| -* (Recommended) To reduce the model size and make it run faster without losing much on accuracy, read the `Quantization Recipe <quantization.html>`_. |
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| - |
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| -* (Must) To convert the model to TorchScipt and (optional) optimize it for mobile apps, read the `Script and Optimize for Mobile Recipe <script_optimized.html>`_. |
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| - |
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| -* (Must for iOS development) To add the model in an iOS project and use PyTorch pod for iOS, read the `Model preparation for iOS Recipe <model_preparation_ios.html>`_. |
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| - |
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| -* (Must for Android development) To add the model in an Android project and use the PyTorch library for Android, read the `Model preparation for Android Recipe <model_preparation_android.html>`_. |
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| - |
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| - |
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| -Learn More |
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| ------------------ |
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| - |
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| -1. `PyTorch Mobile site <https://pytorch.org/mobile>`_ |
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| -2. `PyTorch Mobile Performance Recipes <https://pytorch.org/tutorials/recipes/mobile_perf.html>`_ |
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