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.

Describe the bug
cudf.pivot doesn't understand "values" when it's column name instead of list and the column name includes numeric

Steps/Code to reproduce bug

import cudf
df = cudf.DataFrame([
    ['A', 'a', 0, 0, 0],
    ['A', 'b', 1, 1, 1],
    ['A', 'c', 2, 2, 2],
    ['B', 'a', 0, 0, 0],
    ['B', 'b', 1, 1, 1],
    ['B', 'c', 2, 2, 2],
    ['C', 'a', 0, 0, 0],
    [

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.

We should add an example that demonstrates how to use a CUDA stream with thrust algorithms, e.g:

thrust::reduce(thrust::device.on(stream), ...);

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
If min_samples_split is a small float, then it may be equivalent to splitting on < 2 samples. This causes cuml to blow up:

RuntimeError: exception occured! file=../src/decisiontree/decisiontree.cu line=41: Invalid value for min_samples_split: 1. Should be >= 2.
Obtained 64 stack frames
#0 in /home/mboling/miniconda3/lib/python3.8/site-packages/cuml/common/../../..

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.