CUDA

Reporting a bug

• [x]

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.

Problem statement

The algorithms cudf::reduce and cudf::segmented_reduce do not accept an initial value.

Similar algorithms like [std::reduce](https://e

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

It is a very common pitfall of Thrust users to attempt to use a __device__ lambda with Thrust algorithms or iterators that fails in silent or obscure ways.

This is frequently due to the limitation that you cannot reliably query the return type of an extended lambda in host code. Specifically

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。

Many estimators provide a random_state parameter to let users provide seeds for random number generators. Scikit-learn estimators can accept either an integer or a numpy.random.RandomState for random_state, and some PyData ecosystem tools (e.g. Boruta) pass RandomStates to estimators, so it would be nice if we could accept these as well.

import cuml
from sklearn.datasets i

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:

I have the following program here. When running the following command I get an internal compiler error

echo "[[[1.0, 2.0], [3.0, 4.0]], [[5.0, 6.0], [7.0, 8.0]]]
[2.0, 3.0]
[[[10.0, 2.0],[-1.0, -2.0]],[[20.0, 4.0], [-3.0, -4.0]]]
" | futhark run layers/convolutio