Installation

Using pip

Requirements:

  • Linux operating system on x86-64 architecture (for other architectures build from source)

  • Python >= 3.7

Additional requirements for GPU functionality:

  • NVIDIA GPU: In theory GPU devices with Compute Capability >= 2.x should be supported, but the code is not tested using devices with Compute Capability < 6.0.

  • NVIDIA Driver >= 450.80.02

Install via:

pip install cubnm

From source

This might be needed in case prebuilt wheels are not available for your machine (e.g. on arm64 machines).

Requirements:

  • Linux operating system

  • Python >= 3.7

  • GCC: Pre-built wheels were compiled using version 10.2.1 but alternative versions can be used.

  • GSL 2.7: If GSL is not found (in "/usr/lib", "/lib", "/usr/local/lib", "~/miniconda/lib", $LIBRARY_PATH, $LD_LIBRARY_PATH) it will be installed and built by the package in ~/.cubnm/gsl, but this takes a rather long time (5+ minutes). If you have GSL 2.7 installed find the location of its libraries libgsl.a and libgslcblas.a and add the directory to $LIBRARY_PATH. In this case note that GSL must have been built with --enable-shared option.

Additional requirements for GPU functionality:

  • NVIDIA GPU: In theory GPU devices with Compute Capability >= 2.x should be supported, but the code is not tested using devices with Compute Capability < 6.0.

  • CUDA Toolkit: Pre-built wheels were compiled using version 11.8 but alternative versions can be used.

The package can be installed from source using:

pip install git+https://github.com/amnsbr/cubnm.git -vvv