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..
Top_Ad6168 t1_j2e84wc wrote
Reply to [D] GPU-enabled scikit-learn by Realistic-Bed2658
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