docs/cpp/source/installing.rst
We provide binary distributions of all headers, libraries and CMake
configuration files required to depend on PyTorch. We call this distribution
LibTorch, and you can download ZIP archives containing the latest LibTorch
distribution on our website <https://pytorch.org/get-started/locally/>_. Below
is a small example of writing a minimal application that depends on LibTorch
and uses the torch::Tensor class which comes with the PyTorch C++ API.
The first step is to download the LibTorch ZIP archive via the link above. For example:
.. code-block:: sh
wget https://download.pytorch.org/libtorch/nightly/cpu/libtorch-shared-with-deps-latest.zip unzip libtorch-shared-with-deps-latest.zip
Note that the above link has CPU-only libtorch. If you would like to download a GPU-enabled libtorch, find the right link in the link selector on https://pytorch.org
If you're a Windows developer and wouldn't like to use CMake, you could jump to the Visual Studio Extension section.
Next, we can write a minimal CMake build configuration to develop a small
application that depends on LibTorch. CMake is not a hard requirement for using
LibTorch, but it is the recommended and blessed build system and will be well
supported into the future. A most basic CMakeLists.txt file could look like
this:
.. code-block:: cmake
cmake_minimum_required(VERSION 3.18 FATAL_ERROR) project(example-app)
find_package(Torch REQUIRED) set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${TORCH_CXX_FLAGS}")
add_executable(example-app example-app.cpp) target_link_libraries(example-app "${TORCH_LIBRARIES}") set_property(TARGET example-app PROPERTY CXX_STANDARD 17)
if (MSVC) file(GLOB TORCH_DLLS "${TORCH_INSTALL_PREFIX}/lib/*.dll") add_custom_command(TARGET example-app POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy_if_different ${TORCH_DLLS} $<TARGET_FILE_DIR:example-app>) endif (MSVC)
The implementation of our example will simply create a new torch::Tensor and
print it:
.. code-block:: cpp
#include <torch/torch.h> #include <iostream>
int main() { torch::Tensor tensor = torch::rand({2, 3}); std::cout << tensor << std::endl; }
While there are more fine-grained headers you can include to access only parts
of the PyTorch C++ API, including torch/torch.h is the most sure-proof way of
including most of its functionality.
The last step is to build the application. For this, assume our example directory is laid out like this:
.. code-block:: sh
example-app/ CMakeLists.txt example-app.cpp
We can now run the following commands to build the application from within the
example-app/ folder:
.. code-block:: sh
mkdir build cd build cmake -DCMAKE_PREFIX_PATH=/absolute/path/to/libtorch .. cmake --build . --config Release
where /absolute/path/to/libtorch should be the absolute (!) path to the unzipped LibTorch
distribution. If PyTorch was installed via pip, CMAKE_PREFIX_PATH can be queried
using torch.utils.cmake_prefix_path variable. In that case CMake configuration step would look something like follows:
.. code-block:: sh
cmake -DCMAKE_PREFIX_PATH=python3 -c 'import torch;print(torch.utils.cmake_prefix_path)' ..
If all goes well, it will look something like this:
.. code-block:: sh
root@4b5a67132e81:/example-app# mkdir build root@4b5a67132e81:/example-app# cd build root@4b5a67132e81:/example-app/build# cmake -DCMAKE_PREFIX_PATH=/path/to/libtorch .. -- The C compiler identification is GNU 5.4.0 -- The CXX compiler identification is GNU 5.4.0 -- Check for working C compiler: /usr/bin/cc -- Check for working C compiler: /usr/bin/cc -- works -- Detecting C compiler ABI info -- Detecting C compiler ABI info - done -- Detecting C compile features -- Detecting C compile features - done -- Check for working CXX compiler: /usr/bin/c++ -- Check for working CXX compiler: /usr/bin/c++ -- works -- Detecting CXX compiler ABI info -- Detecting CXX compiler ABI info - done -- Detecting CXX compile features -- Detecting CXX compile features - done -- Looking for pthread.h -- Looking for pthread.h - found -- Looking for pthread_create -- Looking for pthread_create - not found -- Looking for pthread_create in pthreads -- Looking for pthread_create in pthreads - not found -- Looking for pthread_create in pthread -- Looking for pthread_create in pthread - found -- Found Threads: TRUE -- Configuring done -- Generating done -- Build files have been written to: /example-app/build root@4b5a67132e81:/example-app/build# cmake --build . --config Release Scanning dependencies of target example-app [ 50%] Building CXX object CMakeFiles/example-app.dir/example-app.cpp.o [100%] Linking CXX executable example-app [100%] Built target example-app
Executing the resulting example-app binary found in the build folder
should now merrily print the tensor (exact output subject to randomness):
.. code-block:: sh
root@4b5a67132e81:/example-app/build# ./example-app 0.2063 0.6593 0.0866 0.0796 0.5841 0.1569 [ Variable[CPUFloatType]{2,3} ]
.. tip::
On Windows, debug and release builds are not ABI-compatible. If you plan to
build your project in debug mode, please try the debug version of LibTorch.
Also, make sure you specify the correct configuration in the cmake --build .
line above.
To ensure smooth installation and usage of LibTorch, please ensure your system meets the following requirements:
LibTorch Project Template <https://marketplace.visualstudio.com/items?itemName=YiZhang.LibTorch001>_ can help Windows developers
set all libtorch project settings and link options for debug and release.
It's easy to use and you could check out the demo video <https://ossci-windows.s3.us-east-1.amazonaws.com/vsextension/demo.mp4>_.
The only prerequisite is to download the libtorch on https://pytorch.org
If you run into any troubles with this installation and minimal usage guide,
please use our forum <https://discuss.pytorch.org/>_ or GitHub issues <https://github.com/pytorch/pytorch/issues>_ to get in touch.