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.

Is your feature request related to a problem? Please describe.
Our Python docstrings have various style violations when compared against standards like pep257. Not only does this impact readability (which may be subjective), it also reduces the effectiveness of tools like Sphinx or numpydoc that rely on specific formatting in order to parse docstrings.

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

I can't tell where the problem is. Why does the version of Thrust that I get with stdpar=gpu work, whereas the version from GitHub doesn't?

Thanks

MCVE

#include <thrust/universal_vector.h>

thrust::universal_vector<float> m_x ;

void AllocateNodePersistent(int numNode)
{
  m_x.resize(numNode);
}

It works with stdpar=gpu

$ nvc++ -std=c++17 -stdpar=gpu -c bug.

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。

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.