Installation

Metatomic is available for multiple programming languages, and how to install and use it will depend on the programming language you are using.

The TorchScript bindings to metatomic are accessible in Python in the metatomic-torch package. You can install them with

# Make sure you are using the latest version of pip
pip install --upgrade pip

pip install metatomic-torch

We provide pre-compiled wheels on PyPI that are compatible with all the supported torch versions at the time of metatomic-torch release. Currently PyTorch version 1.12 and above is supported.

If you want to use the code with an unsupported PyTorch version, or a new release of PyTorch which did not exist yet when we released metatomic-torch; you’ll need to compile the code on your local machine with

pip install metatomic-torch --no-binary=metatomic-torch

This local compilation will require a couple of additional dependencies:

  • a modern C++ compiler, able to handle C++17, such as:

    • gcc version 7 or above;

    • clang version 5 or above;

    • Microsoft Visual C++ (MSVC) compiler, version 19 (2015) or above.

  • if you want to use the CUDA version of PyTorch, you’ll also need the CUDA toolkit, including the NVIDIA compiler.

By default, PyTorch is installed with CUDA support on Linux, even if you do not have a CUDA-compatible GPU, and will search for a CUDA toolkit when building extension (such as metatomic-torch). If you don’t want to install the CUDA toolkit in this case, you can use the CPU-only version of PyTorch with

pip install --extra-index-url https://download.pytorch.org/whl/cpu metatomic-torch --no-binary=metatomic-torch

A similar index URL can be used to install the ROCm (AMD GPU) version of PyTorch, please refer to the corresponding documentation.

See also

Some potential build failures and corresponding workarounds are listed at the end of the TorchScript C++ installation instructions.

The TorchScript bindings to metatomic are also available as a C++ library, which can be integrated in non-Python software (such as simulation engines) to use custom metatomic models directly in the software without relying on a Python interpreter. The code is installed as a shared library which register itself with torch when loaded, the corresponding header files and a CMake integration allowing you to use metatomic-torch in your code code with find_package(metatomic_torch).

To build and install the code, you’ll need to find the latest release of metatomic-torch on GitHub releases, and download the corresponding metatomic-torch-cxx file in the release assets. Then, you can run the following commands:

cmake -E tar xf metatomic-torch-cxx-*.tar.gz
cd metatomic-torch-cxx-*
mkdir build && cd build

# configure cmake here if needed
cmake ..

# build and install the code
cmake --build . --target install

You will have to to manually install some of the dependencies of metatomic-torch yourself to compile this code, and any of the dependencies is not in a standard location, specify the installation directory when configuring cmake with CMAKE_PREFIX_PATH. The following dependencies might have to be installed beforehand:

  • the C++ interface of metatensor-torch.

  • the C++ part of PyTorch, which you can install on it’s own. We are compatible with libtorch version 1.12 or above. You can also use the same library as the Python version of torch by adding the output of the command below to CMAKE_PREFIX_PATH:

    python -c "import torch; print(torch.utils.cmake_prefix_path)"

Option

Description

Default

CMAKE_BUILD_TYPE

Type of build: debug or release

release

CMAKE_INSTALL_PREFIX

Prefix in which the library will be installed

/usr/local

CMAKE_PREFIX_PATH

;-separated list of path where CMake will search for dependencies. This list should include the path to metatensor and torch

Workaround for some build errors

The CMake configuration used by libtorch sometimes fails to setup the build environment. You’ll find here a list of some known build failures and how to workaround them.

  • Unknown CUDA Architecture Name 9.0a in CUDA_SELECT_NVCC_ARCH_FLAGS

    This can happen when building with a CUDA-enabled version of torch and a recent version of cmake. This issue is tracked at https://github.com/pytorch/pytorch/issues/113948. To work around it, you can export TORCH_CUDA_ARCH_LIST="8.0 8.6 8.9 9.0" in your environment before building the code.

  • Imported target "torch" includes non-existent path
  [...]/MKL_INCLUDE_DIR-NOTFOUND"
in its INTERFACE_INCLUDE_DIRECTORIES.

    This can happen when building for x86_64 Linux when MKL is not available on the current machine. Since MKL is a completely optional dependency, you can silence the error by running cmake with the -DMKL_INCLUDE_DIR=/usr/include option.

Installing a development version

Metatomic is developed on GitHub. If you want to install a development version of the code, you will need git to fetch the latest version of the code.

You can install a development version of the TorchScript bindings to metatomic with:

# Make sure you are using the latest version of pip
pip install --upgrade pip

git clone https://github.com/metatensor/metatomic
cd metatomic
pip install ./python/metatomic_torch

# alternatively, the same thing in a single command
pip install "metatomic-torch @ git+https://github.com/metatensor/metatomic#subdirectory=python/metatomic_torch"

You can install the development version of metatomic with the following (the same cmake configuration options are available):

git clone https://github.com/metatensor/metatomic
cd metatomic/metatomic-torch
mkdir build && cd build

# configure cmake here if needed
cmake ..

# build and install the code
cmake --build . --target install