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- Migrate pytorch docs, cpp docs and functorch docs to the pytorch_sphinx_theme2 - Migrate index.rst to markdown and restructure to use high-level horizontal bar sections Python API, Developer Notes - Added python-api.md which becomes the main container for the API docs. This file will be used to add all api references in the toctree. It would be great to have lint for this file: https://github.com/pytorch/pytorch/issues/150718 - Enabled mermaid sphinx extension and opengraph sphinx extension Pull Request resolved: https://github.com/pytorch/pytorch/pull/149331 Approved by: https://github.com/malfet, https://github.com/atalman, https://github.com/albanD
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C++
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===================================
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.. Note::
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If you are looking for the PyTorch C++ API docs, directly go `here <https://pytorch.org/cppdocs/>`__.
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PyTorch provides several features for working with C++, and it’s best to choose from them based on your needs. At a high level, the following support is available:
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TorchScript C++ API
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--------------------
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`TorchScript <https://pytorch.org/docs/stable/jit.html>`__ allows PyTorch models defined in Python to be serialized and then loaded and run in C++ capturing the model code via compilation or tracing its execution. You can learn more in the `Loading a TorchScript Model in C++ tutorial <https://pytorch.org/tutorials/advanced/cpp_export.html>`__. This means you can define your models in Python as much as possible, but subsequently export them via TorchScript for doing no-Python execution in production or embedded environments. The TorchScript C++ API is used to interact with these models and the TorchScript execution engine, including:
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* Loading serialized TorchScript models saved from Python
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* Doing simple model modifications if needed (e.g. pulling out submodules)
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* Constructing the input and doing preprocessing using C++ Tensor API
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Extending PyTorch and TorchScript with C++ Extensions
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------------------------------------------------------
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TorchScript can be augmented with user-supplied code through custom operators and custom classes.
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Once registered with TorchScript, these operators and classes can be invoked in TorchScript code run from
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Python or from C++ as part of a serialized TorchScript model. The `Extending TorchScript with Custom C++ Operators <https://pytorch.org/tutorials/advanced/torch_script_custom_ops.html>`__ tutorial walks through interfacing TorchScript with OpenCV. In addition to wrapping a function call with a custom operator, C++ classes and structs can be bound into TorchScript through a pybind11-like interface which is explained in the `Extending TorchScript with Custom C++ Classes <https://pytorch.org/tutorials/advanced/torch_script_custom_classes.html>`__ tutorial.
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Tensor and Autograd in C++
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---------------------------
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Most of the tensor and autograd operations in PyTorch Python API are also available in the C++ API. These include:
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* ``torch::Tensor`` methods such as ``add`` / ``reshape`` / ``clone``. For the full list of methods available, please see: https://pytorch.org/cppdocs/api/classat_1_1_tensor.html
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* C++ tensor indexing API that looks and behaves the same as the Python API. For details on its usage, please see: https://pytorch.org/cppdocs/notes/tensor_indexing.html
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* The tensor autograd APIs and the ``torch::autograd`` package that are crucial for building dynamic neural networks in C++ frontend. For more details, please see: https://pytorch.org/tutorials/advanced/cpp_autograd.html
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Authoring Models in C++
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------------------------
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The "author in TorchScript, infer in C++" workflow requires model authoring to be done in TorchScript.
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However, there might be cases where the model has to be authored in C++ (e.g. in workflows where a Python
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component is undesirable). To serve such use cases, we provide the full capability of authoring and training a neural net model purely in C++, with familiar components such as ``torch::nn`` / ``torch::nn::functional`` / ``torch::optim`` that closely resemble the Python API.
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* For an overview of the PyTorch C++ model authoring and training API, please see: https://pytorch.org/cppdocs/frontend.html
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* For a detailed tutorial on how to use the API, please see: https://pytorch.org/tutorials/advanced/cpp_frontend.html
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* Docs for components such as ``torch::nn`` / ``torch::nn::functional`` / ``torch::optim`` can be found at: https://pytorch.org/cppdocs/api/library_root.html
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Packaging for C++
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------------------
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For guidance on how to install and link with libtorch (the library that contains all of the above C++ APIs), please see: https://pytorch.org/cppdocs/installing.html. Note that on Linux there are two types of libtorch binaries provided: one compiled with GCC pre-cxx11 ABI and the other with GCC cxx11 ABI, and you should make the selection based on the GCC ABI your system is using.
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