NumPy

🐛 Describe the bug

Usage of RRefContext::handleException in torch/csrc/distributed/rpc/rref_context.cpp is wrong when the future has an error.

RRefContext::handleException uses TORCH_CHECK which throws.

Callers of RRefContext::handleException don't expect that and run code after it without any guarding.

Versions

master

cc @pietern @mrshenli @pritamdamania87

Started in #13104, turned into a tracking issue here.

Turning on -n in sphinx-build results in over 1000 warnings about bad references in docs. Trying to break these down with various grep statements, it seems about 200 are from numpy.ma, about 150 from polynomial and about 150 from c:type. That is under half of the WARNINGS.

~My workflow is to cd to the doc directory, modify the `ALLSPHIN

Is your feature request related to a problem? Please describe.

I am uploading our dataset and models for the "Constructing interval measures" method we've developed, which uses item response theory to convert multiple discrete labels into a continuous spectrum for hate speech. Once we have this outcome our NLP models conduct regression rather than classification, so binary metrics are not r

We're trying to introduce Parquet into our team, and the largest blocker that we've seen is the dreaded "schemas are inconsistent" error message:

RuntimeError: Schemas are inconsistent, try using to_parquet(..., schema="infer"), or pass an explicit pyarrow schema. Such as to_parquet(..., schema={"column1": pa.string()})

This error message is super unhelpful: surely Dask knows what th

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

Is there a way to train a bidirectional RNN (like LSTM or GRU) on trax nowadays?

Rel. #6198.

NumPy 1.22 added missing parameters for some nan<x> functions.

A number of the nan functions previously lacked parameters that were present in their -based counterpart, e.g. the where parameter was present in numpy.mean but absent from numpy.nanmean.

https://numpy.org/devdocs/release/1.22.0-notes.html#missing-parameters-have-been-added-to-the-nan-x-functions

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环境

1.系统环境：

• Ubuntu 20.04.2 LTS x86_64
• GLIBCXX: 3.4.28 (libstdc++.so.6.0.28)
• GPU:
  

2.MegEngine版本：1.6.0rc1
3.python版本：Python 3.8.10

The program stuck at net.load when I was trying to use the MegFlow. I wait for more than 10min and there is no sign of finishing it.

Bloomberg BQuant platform only allows installation of the wheel version of a python library. Please, can you create a wheel version of TA library?

pydata/xarray#5865 (reply in thread)

I wonder if it's possible to implement a built-in function like:
da.str.format("%.2f") or xr.string_format(da, "%.2f)

To wrap:

import xarray as xr

da = xr.DataArray([5., 6., 7.])
das = xr.DataArray("%.2f")
das.str % da

<xarray.DataArray (dim_0: 3)>
array(['5.00', '6.00', '7.00'], dtype='<U4')
Dim