CUDA

I see comments suggesting adding this to understand how loops are being handled by numba, and in the their own FAQ (https://numba.pydata.org/numba-doc/latest/user/faq.html)

from llvmlite import binding as llvm
llvm.set_option('','--debug-only=loop-vectorize')

You would then create your njit function and run it, and I believe the idea is that it prints debug information about whether

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

Description

https://numpy.org/doc/stable/reference/generated/numpy.corrcoef.html

https://docs.cupy.dev/en/stable/reference/generated/cupy.corrcoef.html

Seems args are different

Additional Information

dtype argument added in NumPy version 1.20.

Recently in Morpheus we encountered a bug where get_current_device_resource was undefined in a place we were not explicitly using it. Most public-facing libcudf APIs provide a memory_resource* as a default argument by calling get_current_device_resource, defined in rmm/mr/per_device_resource.hpp, however in some places this header is not included which requires the caller of libcudf APIs t

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.

I changed some compilation parameters of my program and now I see this warning that might be easy to fix.

/usr/local/cuda/bin/../targets/x86_64-linux/include/thrust/detail/complex/catrigf.h: In function 'thrust::complex<float> thrust::detail::complex::casinhf(thrust::complex<float>)':
/usr/local/cuda/bin/../targets/x86_64-linux/include/thrust/detail/complex/catrigf.h:170:36: error: implic

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。

Describe the bug

We should raise better error messages in the scenario when users pass stuff like pandas.Series/list etc to the vectorizer.

Steps/Code to reproduce bug

import cudf
import pandas
from cuml.feature_extraction.text import TfidfVectorizer

vec =  TfidfVectorizer()
text_s = pandas.Series(["apple", "is", "great"])

 vec.fit_transform(text_s)

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:

In order to test manually altered IR, it would be nice to have a --skip-compilation flag for futhark test, just like we do for futhark bench.