CUDA

• I have tried using the latest released version of Numba (most recent is
  visible in the change log (https://github.com/numba/numba/blob/main/CHANGE_LOG).
• I have included a self contained code sample to reproduce the problem.
  i.e. it's possible to run as 'python bug.py'.

I think I have discovered a very minor bug - or rather inconsistency with numpy - in Numba's implementation

Problem:

_catboost.pyx in _catboost._set_features_order_data_pd_data_frame()

_catboost.pyx in _catboost.get_cat_factor_bytes_representation()

CatBoostError: Invalid type for cat_feature[non-default value idx=1,feature_idx=336]=2.0 : cat_features must be integer or string, real number values and NaN values should be converted to string.

Could you also print a feature name, not o

Rel. #6198.

NumPy 1.22 added missing parameters for some nan<x> functions.

A number of the nan functions previously lacked parameters that were present in their -based counterpart, e.g. the where parameter was present in numpy.mean but absent from numpy.nanmean.

https://numpy.org/devdocs/release/1.22.0-notes.html#missing-parameters-have-been-added-to-the-nan-x-functions

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Is your feature request related to a problem? Please describe.
#8643 introduced a new rST directive pandas-compat to our documentation that allows us to collect all documentation relating to differences between cuDF methods and pandas methods. That directive enables us to much more effectively indicate to users when our behavior differs from pandas. However, we have not since made much use

I'm working with clang++ 13.0 and CUDA Toolkit 11.6. It seems to me that there's probably some problem with the __noinline__ macro. In thrust, it is used as __attribute__((__noinline__)) which expects __noinline__ expand to noinline. However, with clang++, __noinline__ expands to __attribute__((noinline)), which makes __attribute__((__attribute__((noinline)))) and cause a compile

请问可以直接training tmfile出来吗? 因为tengine-convert-tool covert 会有error

tengine-lite library version: 1.4-dev
Get input tensor failed

或是有例子能training出下面tmfile 呢?
![Screenshot from 2021-05-27 07-01-46](https://user-images.githubusercontent.com/40915044/11

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.

你好，请问怎么装载 ONNX 模型，目前只看到 Oneflow->ONNX 工具，没有找到 ONNX->Oneflow 工具。

Is your feature request related to a problem? Please describe.
We are prefixing cmake's logging with different words to be able to diagnose CI easier, for example CUML_CPP and CUML_PY. CMAKE_MESSAGE_CONTEXT can cleanup the functionality as opposed to having the keyword repeated in every statement.

Hey everyone!

mapd-core-cpu is already available on conda-forge (https://anaconda.org/conda-forge/omniscidb-cpu)

now we should add some instructions on the documentation.

at this moment it is available for linux and osx.

some additional information about the configuration:

1. for now, always install omniscidb-cpu inside a conda environment (also it is a good practice), eg:

They're getting slow. This might require some wrangling of things we create in tests to make them not collide.

There is currently code generation for C and Python and there are a few inofficial bridges using the former to call futhark code from Haskell, Python, rust and Standard ML. However, there is no such convenient way to call futhark from a JVM language. Please add such support. I'd love to be able to call futhark code from, e.g., a Scala program. Thanks!