.join seems to not work properly with unicode arrays. Here is a reproducer:

In [16]: str_arr = np.array(["hi", "there"])

In [17]: ",".join(str_arr)
Out[17]: 'hi,there'
In [18]: numba.njit(lambda str_arr: ",".join(str_arr))(str_arr)
TypingError: Failed in nopython mode pipeline (step: nopython frontend)
- Resolution failure for literal arguments:
No implementation of function 

Problem:
catboost version: 0.23.2
Operating System: all
Tutorial: https://github.com/catboost/tutorials/blob/master/custom_loss/custom_metric_tutorial.md

Impossible to use custom metric (С++).

Code example

from catboost import CatBoost
train_data = [[1, 4, 5, 6],
 

Improve readability of thread id based branches by giving them more descriptive names.

e.g.

if (!t) // is actually a t == 0

and

https://github.com/rapidsai/cudf/blob/57ef76927373d7260b6a0eda781e59a4c563d36e/cpp/src/io/statistics/column_stats.cu#L285
Is actually a lane_id == 0
As demonstrated in rapidsai/cudf#6241 (comment), pr

Current implementation of join can be improved by performing the operation in a single call to the backend kernel instead of multiple calls.

This is a fairly easy kernel and may be a good issue for someone getting to know CUDA/ArrayFire internals. Ping me if you want additional info.

It would be wonderful if I could inspect the contents of thrust containers: host_vector and device_vector in GDB (and more importantly, in VSCode). GDB allows customizing this.

It would save a lot of time if I could inspect device vectors without having to bring them to the host (e.g. the pretty printer script would do that behind the

The functions under metrics and score in src_prims can be semantically categorized under a common name (whether that can be named 'metrics' or 'scores' is open for discussion).

It is exposed in cuml under a common rubric as [metrics](https://github.

I often use -v just to see that something is going on, but a progress bar (enabled by default) would serve the same purpose and be more concise.

We can just factor out the code from futhark bench for this.

Thank you for this fantastic work!

Could it be possible the fit_transform() method returns the KL divergence of the run?

Thx!