To tap the power of accelerators, AI codes must incorporate efficient internode communication schemes (like the well-established MPI) and align with programming models associated with the available accelerators. Examples include CUDA for Nvidia GPUs, ROCm for AMD GPUs, or SYCL/DPC++ for Intel GPUs. The neural network-based AI codes often rely on the availability and development of frameworks such as pyTorch and tensorflow, where the developers of these frameworks take the burden to adapt the framework to accelerators. For instance, pytorch provides versions of its framework with support for CUDA or ROCm backends. However, not all of the AI methods can be seamlessly translated into a neuralnetwork representation and not all applications are well suited for neural networks. Consequently, significant adaptation is required, leading to limited accelerator support. For example, the widely used decision-tree-based AI library, XGBoost, offers a CUDA version, but is still lacking a ROCm equivalent.