Philip Hyunsu Cho

@jameslamb We dropped CUDA 10.x support in February 2022: https://github.com/dmlc/xgboost/issues/7366

Thanks for pinging me. Good to know that C++17 is being adopted widely. XGBoost project should move to C++17 standard as well. (*) (*) XGBoost is actually using C++17 when...

I was able to reproduce the error using the latest Docker image (`rapidsai/rapidsai-core-nightly:23.08-cuda11.8-runtime-ubuntu22.04-py3.10`). Error: ``` [W] [18:38:04.294878] Treelite currently does not support float64 model parameters. Accuracy may degrade slightly relative...

Indeed, using `ForestInference.load_from_sklearn` with experimental FIL works. On the other hand, `ForestInference.load_from_sklearn` from the current FIL fails with this error: ``` [W] [18:50:58.669347] Treelite currently does not support float64 model...

We also need to throw an informative error when the user attempts to call `load_from_sklearn` with an existing object.

Update: I ran more experiments and here's what I found: | FIL | Install method | `ForestInference().load_from_sklearn(...)` | `ForestInference.load_from_sklearn(...)` | --- | --- | --- | --- | | Current...

@wphicks I think there is something wrong with this import: https://github.com/rapidsai/cuml/blob/91d30fc305f399362c248f182a79fcc93c21a051/python/cuml/fil/fil.pyx#L20 NumPy is needed for the following lines, so we may want to import NumPy unconditionally: https://github.com/rapidsai/cuml/blob/91d30fc305f399362c248f182a79fcc93c21a051/python/cuml/fil/fil.pyx#L286-L288

It's concerning that the import fails only when using Docker or Conda install. I could not reproduce it when building cuML from the source.

@wphicks Given the lack of bandwidth on our part, can we switch back to traditional import to unblock users to use `load_from_sklearn`?

> It's not clear to me that that will actually solve this issue The experimental FIL uses traditional imports and its import function is working. > Does the host_xpy solution...