  Knowledge graph embedding research has mainly focused on learning continuous
representations of knowledge graphs towards the link prediction problem.
Recently developed frameworks can be effectively applied in research related
applications. Yet, these frameworks do not fulfill many requirements of
real-world applications. As the size of the knowledge graph grows, moving
computation from a commodity computer to a cluster of computers in these
frameworks becomes more challenging. Finding suitable hyperparameter settings
w.r.t. time and computational budgets are left to practitioners. In addition,
the continual learning aspect in knowledge graph embedding frameworks is often
ignored, although continual learning plays an important role in many real-world
(deep) learning-driven applications. Arguably, these limitations explain the
lack of publicly available knowledge graph embedding models for large knowledge
graphs. We developed a framework based on the frameworks DASK, Pytorch
Lightning and Hugging Face to compute embeddings for large-scale knowledge
graphs in a hardware-agnostic manner, which is able to address real-world
challenges pertaining to the scale of real application. We provide an
open-source version of our framework along with a hub of pre-trained models
having more than 11.4 B parameters.
