Kazuaki IshizakiandSvetlana Karslioglu
5ebcce8195
## Summary Converts docs/source/cpp_index.rst from reStructuredText to MyST Markdown Closes https://github.com/pytorch/pytorch/issues/182458 ## Test plan - [x] lintrunner -m main — no lint issues - [x] Full Sphinx doc build — build succeeded, zero warnings for this file - [x] HTML output verified after `make html` - [x] git log --follow docs/source/cpp_index.md confirms history is preserved Pull Request resolved: https://github.com/pytorch/pytorch/pull/182820 Approved by: https://github.com/svekars Co-authored-by: Svetlana Karslioglu <svekars@meta.com>
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C++
If you are looking for the PyTorch C++ API docs, directly go [here](https://pytorch.org/cppdocs/).
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:
Tensor and Autograd in C++
Most of the tensor and autograd operations in PyTorch Python API are also available in the C++ API. These include:
torch::Tensormethods such asadd/reshape/clone. For the full list of methods available, please see: https://pytorch.org/cppdocs/api/classat_1_1_tensor.html- 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
- The tensor autograd APIs and the
torch::autogradpackage that are crucial for building dynamic neural networks in C++ frontend. For more details, please see: https://pytorch.org/tutorials/advanced/cpp_autograd.html
Authoring Models in C++
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.
- For an overview of the PyTorch C++ model authoring and training API, please see: https://pytorch.org/cppdocs/frontend.html
- For a detailed tutorial on how to use the API, please see: https://pytorch.org/tutorials/advanced/cpp_frontend.html
- Docs for components such as
torch::nn/torch::nn::functional/torch::optimcan be found at: https://pytorch.org/cppdocs/api/library_root.html
Packaging for C++
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.