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ArrayFire is a high performance library for parallel computing with an easy-to-use API. It enables users to write scientific computing code that is portable across CUDA, OpenCL and CPU devices.

This project is meant to provide thin Python bindings for the ArrayFire C library. It also decouples releases of the main C/C++ library from the Python library by acting as a intermediate library and only wrapping the provided C calls.

This allows the building of large binary wheels only when the underlying ArrayFire version is increased, and the fully-featured Python library can be developed atop independently. The package is not intended to be used directly and merely exposes theC functionality required by downstream implementations. This package can exist in two forms, with a bundled binary distribution, or merely as a loader that will load the ArrayFire library from a system or user level install.

The arrayfire-binary-python-wrapper can be installed from a variety of sources.Pre-built wheels are available for a number of systems and toolkits. These will include a binary distribution of the ArrayFire libraries. Installing from PyPI directly will only include a wrapper-only, source distribution that will not contain binaries. In this case, wrapper-only installations will require a separate installation of the ArrayFire C/C++ libraries.You can get the ArrayFire C/C++ library from the following sources:

• Download and install binaries
• Build and install from source

Install the last stable version of python wrapper:

pip install arrayfire-binary-python-wrapper

Install a pre-built wheel:

pip install arrayfire-binary-python-wrapper -f https://repo.arrayfire.com/python/wheels/3.9.0/

The arrayfire-binary-python-wrapper can build wheels in packaged-binary or in system-wrapper modes.scikit-build-core is used to provide the python build backend.The minimal, wrapper-only mode that relies on a system install will be built by default though the regular python build process. For example:

pipx run build --wheel

Building a full pre-packaged local binary is an involved process that will require referencing the regular ArrayFirebuildprocedures.Besides the regular ArrayFire CMake configuration, building the binaries is an opt-in process that is set by an environment variableAF_BUILD_LOCAL_LIBS=1. Once that environment variable is set, scikit-build-core will take care of cloning ArrayFire, building, and including the necessary binaries.

The community of ArrayFire developers invites you to build with us if you areinterested and able to write top-performing tensor functions. Together we canfulfillThe ArrayFireMissionfor fast scientific computing for all.

Contributions of any kind are welcome! Please refer tothewiki and ourCode ofConduct to learn more about how you can get involved with the ArrayFireCommunity throughSponsorship,DeveloperCommits,orGovernance.

If you redistribute ArrayFire, please follow the terms established inthelicense.

ArrayFire development is funded by AccelerEyes LLC and several third parties,please see the list ofacknowledgements for anexpression of our gratitude.

• Slack Chat
• Google Groups
• ArrayFire Services:Consulting |Support |Training

The literal mark "ArrayFire" and ArrayFire logos are trademarks of AccelerEyesLLC (dba ArrayFire). If you wish to use either of these marks in your ownproject, please consultArrayFire's TrademarkPolicy