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4ac91b2d64
Summary: as title. These were already added to the tutorials, but I didn't add them to the cpp docs. Pull Request resolved: https://github.com/pytorch/pytorch/pull/17452 Differential Revision: D14206501 Pulled By: suo fbshipit-source-id: 89b5c8aaac22d05381bc4a7ab60d0bb35e43f6f5
139 lines
4.8 KiB
ReStructuredText
139 lines
4.8 KiB
ReStructuredText
Installing C++ Distributions of PyTorch
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=======================================
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We provide binary distributions of all headers, libraries and CMake
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configuration files required to depend on PyTorch. We call this distribution
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*LibTorch*, and you can download ZIP archives containing the latest LibTorch
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distribution on `our website <https://pytorch.org/get-started/locally/>`_. Below
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is a small example of writing a minimal application that depends on LibTorch
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and uses the ``torch::Tensor`` class which comes with the PyTorch C++ API.
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Minimal Example
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---------------
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The first step is to download the LibTorch ZIP archive via the link above. For
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example:
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.. code-block:: sh
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wget https://download.pytorch.org/libtorch/nightly/cpu/libtorch-shared-with-deps-latest.zip
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unzip libtorch-shared-with-deps-latest.zip
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Note that the above link has CPU-only libtorch. If you would like to download a GPU-enabled
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libtorch, find the right link in the link selector on https://pytorch.org
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Next, we can write a minimal CMake build configuration to develop a small
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application that depends on LibTorch. CMake is not a hard requirement for using
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LibTorch, but it is the recommended and blessed build system and will be well
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supported into the future. A most basic `CMakeLists.txt` file could look like
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this:
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.. code-block:: cmake
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cmake_minimum_required(VERSION 3.0 FATAL_ERROR)
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project(example-app)
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find_package(Torch REQUIRED)
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add_executable(example-app example-app.cpp)
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target_link_libraries(example-app "${TORCH_LIBRARIES}")
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set_property(TARGET example-app PROPERTY CXX_STANDARD 11)
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The implementation of our example will simply create a new `torch::Tensor` and
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print it:
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.. code-block:: cpp
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#include <torch/torch.h>
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#include <iostream>
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int main() {
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torch::Tensor tensor = torch::rand({2, 3});
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std::cout << tensor << std::endl;
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}
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While there are more fine-grained headers you can include to access only parts
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of the PyTorch C++ API, including `torch/torch.h` is the most sure-proof way of
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including most of its functionality.
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The last step is to build the application. For this, assume our example
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directory is laid out like this:
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.. code-block:: sh
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example-app/
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CMakeLists.txt
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example-app.cpp
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We can now run the following commands to build the application from within the
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``example-app/`` folder:
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.. code-block:: sh
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mkdir build
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cd build
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cmake -DCMAKE_PREFIX_PATH=/absolute/path/to/libtorch ..
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make
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where ``/absolute/path/to/libtorch`` should be the absolute (!) path to the unzipped LibTorch
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distribution. If all goes well, it will look something like this:
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.. code-block:: sh
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root@4b5a67132e81:/example-app# mkdir build
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root@4b5a67132e81:/example-app# cd build
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root@4b5a67132e81:/example-app/build# cmake -DCMAKE_PREFIX_PATH=/path/to/libtorch ..
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-- The C compiler identification is GNU 5.4.0
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-- The CXX compiler identification is GNU 5.4.0
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-- Check for working C compiler: /usr/bin/cc
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-- Check for working C compiler: /usr/bin/cc -- works
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-- Detecting C compiler ABI info
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-- Detecting C compiler ABI info - done
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-- Detecting C compile features
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-- Detecting C compile features - done
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-- Check for working CXX compiler: /usr/bin/c++
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-- Check for working CXX compiler: /usr/bin/c++ -- works
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-- Detecting CXX compiler ABI info
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-- Detecting CXX compiler ABI info - done
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-- Detecting CXX compile features
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-- Detecting CXX compile features - done
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-- Looking for pthread.h
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-- Looking for pthread.h - found
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-- Looking for pthread_create
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-- Looking for pthread_create - not found
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-- Looking for pthread_create in pthreads
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-- Looking for pthread_create in pthreads - not found
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-- Looking for pthread_create in pthread
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-- Looking for pthread_create in pthread - found
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-- Found Threads: TRUE
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-- Configuring done
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-- Generating done
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-- Build files have been written to: /example-app/build
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root@4b5a67132e81:/example-app/build# make
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Scanning dependencies of target example-app
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[ 50%] Building CXX object CMakeFiles/example-app.dir/example-app.cpp.o
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[100%] Linking CXX executable example-app
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[100%] Built target example-app
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Executing the resulting ``example-app`` binary found in the ``build`` folder
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should now merrily print the tensor (exact output subject to randomness):
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.. code-block:: sh
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root@4b5a67132e81:/example-app/build# ./example-app
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0.2063 0.6593 0.0866
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0.0796 0.5841 0.1569
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[ Variable[CPUFloatType]{2,3} ]
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.. tip::
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On Windows, debug and release builds are not ABI-compatible. If you plan to
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build your project in debug mode, we recommend
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`building PyTorch from source <https://github.com/pytorch/pytorch#from-source>`_.
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Support
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-------
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If you run into any troubles with this installation and minimal usage guide,
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please use our `forum <https://discuss.pytorch.org/>`_ or `GitHub issues
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<https://github.com/pytorch/pytorch/issues>`_ to get in touch.
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