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

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.

Now we are going to have set operations (rapidsai/cudf#11043). To be consistent with other libraries/framework (like Presto: https://prestodb.io/docs/current/functions/array.html), we should rename lists::drop_list_duplicates into lists::distinct. The implementation should be moved into set_operations.hpp|cu to be easily located and for consistency, as mentioned above

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:

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!