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CUDA

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CUDA® is a parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs). With CUDA, developers are able to dramatically speed up computing applications by harnessing the power of GPUs.

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numba
DrTodd13
DrTodd13 commented Apr 7, 2021

In numba/stencils/stencil.py, there are various places (like line 552, "if isinstance(kernel_size[i][0], int):") where we check for "int" in relation to neighborhoods. I ran across a case where I was creating a neighborhood tuple by extracting values from a Numpy array. This causes a problem because those Numpy values will not match in these isinstance int checks. I worked around it by conver

pseudotensor
pseudotensor commented Jan 12, 2021

Problem: the approximate method can still be slow for many trees
catboost version: master
Operating System: ubuntu 18.04
CPU: i9
GPU: RTX2080

Would be good to be able to specify how many trees to use for shapley. The model.predict and prediction_type versions allow this. lgbm/xgb allow this.

esnvidia
esnvidia commented Jun 15, 2021

Describe the bug
Clipping a DataFrame or Series using ints causes a cudf Failure because it won't handle the different dtypes (int and float)

Steps/Code to reproduce bug

data = cudf.Series([-0.43, 0.1234, 1.5, -1.31])
data.clip(0, 1)

...
  File "cudf/_lib/replace.pyx", line 216, in cudf._lib.replace.clip
  File "cudf/_lib/replace.pyx", line 198, in cudf._lib.replace.clamp
wphicks
wphicks commented Feb 8, 2021

Report needed documentation

Report needed documentation
While the estimator guide offers a great breakdown of how to use many of the tools in api_context_managers.py, it would be helpful to have information right in the docstring during development to more easily understand what is actually going on in each of the provided functions/classes/methods. This is particularly important for

futhark

Created by Nvidia

Released June 23, 2007

Website
developer.nvidia.com/cuda-zone
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