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Summary: * Add cpp_index.rst for landing page to match 1.5 (https://github.com/pytorch/pytorch/blob/release/1.5/docs/source/cpp_index.rst) * Link to new cpp landing page was added to the docs table of contents in this PR: https://github.com/pytorch/pytorch/pull/38350 Pull Request resolved: https://github.com/pytorch/pytorch/pull/38450 Differential Revision: D21580939 Pulled By: jlin27 fbshipit-source-id: 021c43f207a100d554266e4e16cb6752ca9c56a0
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44 lines
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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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