Nvidia

It is a very common pitfall of Thrust users to attempt to use a __device__ lambda with Thrust algorithms or iterators that fails in silent or obscure ways.

This is frequently due to the limitation that you cannot reliably query the return type of an extended lambda in host code. Specifically

Many estimators provide a random_state parameter to let users provide seeds for random number generators. Scikit-learn estimators can accept either an integer or a numpy.random.RandomState for random_state, and some PyData ecosystem tools (e.g. Boruta) pass RandomStates to estimators, so it would be nice if we could accept these as well.

import cuml
from sklearn.datasets i

Describe the solution you'd like

Improve this error to provide both the found hardware and the expected hardware

 No compatible device was found for instantiating TensorRT **engine**

I would like to be able to determine my device-wide primitive's temp_storage_bytes before I have all of the primitive's arguments ready. The interface for obtaining it ostensibly requires everything to be ready for the actual run - and the documentation does not make it clear what arguments it actually needs and what it's going to do with them (e.g. will it look at the input at all, in any way? Pr

Describe the bug
A clear and concise description of what the bug is.

To Reproduce
Steps to reproduce the behavior:

1. Download install.sh
2. Make it executable
3. Run install.sh

Expected behavior
Honoring XDG Base Directory specifications and not installing binaries and all of the data files into $HOME/.config which is default $XDG_CONFIG_HOME and shouldn't be hardco