Building the ML SDK VGF Library

The build system must have:

CMake 3.22 or later.
C/C++ 17 compiler: GCC, or optionally Clang on Linux and MSVC on Windows®.
Python 3.10 or later. Required python libraries for building are listed in tooling-requirements.txt.
Ninja 1.10 or later.

The following dependencies are also needed:

For the preferred dependency versions see the manifest file.

Building with the script

To make the build configuration options easily discoverable, we provide a python build script. When you run the script from a git-repo manifest checkout, the script uses default paths and does not require any additional arguments. If you do not use the script, you must specify paths to the dependencies.

To build on the current platform, for example on Linux or Windows®, run the following command:

python3 $SDK_PATH/sw/vgf-lib/scripts/build.py -j $(nproc)

To cross compile for AArch64 architecture, add the following option:

python3 $SDK_PATH/sw/vgf-lib/scripts/build.py -j $(nproc) --target-platform aarch64

To enable and run tests, use the --test option. To lint the tests, use the --lint option. To enable tests and documentation building python dependencies must be installed:

pip install -r requirements.txt
pip install -r tooling_requirements.txt

To build the documentation, use the --doc option. To build the documentation, you must have sphinx and doxygen installed on your machine.

You can install the build artifacts for this project into a specified location. To install the build artifacts, pass the --install option with the required path.

To create an archive with the build artifacts option, you must add the --package option. The archive is stored in the provided location.

For more command line options, see the help output:

python3 $SDK_PATH/sw/vgf-lib/scripts/build.py --help

PyPI

The ML SDK VGF Library is available on PyPI as the ai-ml-sdk-vgf-library package.

Install:

pip install ai-ml-sdk-vgf-library