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C++

.. Note:: 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::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
• 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::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

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::optim can 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.