If you are interested in just predictions you can try Hummingbird. It is part of the PyTorch ecosystem. We get already trained scikit-learn models and translate them into PyTorch models. From them you can run your model on any hardware supported by PyTorch, export it into TVM, ONNX, etc. Performance on hardware acceleration is quite good (orders of magnitude better than scikit-learn is some cases).

If you are interested in trying to do the same thing for training, let's open an issue and try to work through it for few simple models. We have some work on fine tuning pre-trained tree-ensemble models using PyTorch, but this is a bit different..

Paper: https://www.usenix.org/system/files/osdi20-nakandala.pdf

Fine tuning paper: http://www.vldb.org/pvldb/vol15/p11-yu.pdf

Paper on doing the same for other type of computations (e.g., graph algorithms): http://www.vldb.org/pvldb/vol14/p1797-koutsoukos.pdf

Paper on doing the same for SQL queries: https://www.vldb.org/pvldb/vol15/p2811-he.pdf