NumPy

🐛 Describe the bug

Currently many tests run in forks but they are expected to fail due to lack of credentials in forks.

This is a bit annoying to contributors:

A failed job example: https://github.com/ppwwyyxx/pytorch/runs/5137367784?check_suite_focus=true

I thin

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

Array.reshape "only allows for reshapings that collapse or merge dimensions" (xref dask/dask#2561). However, when you try to do one of these unsupported reshapings, the error message Shapes not compatible does not make it at all clear that what you're asking for just isn't supported by dask. Instead, it sounds as though your inputs are invalid.

A more descriptive e

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?

Description

Calling vectorize with a non-None value for the signature parameter outputs this error message about the excluded parameter.

NotImplementedError: cupy.vectorize does not support `excluded` option currently.

Inspecting the code, it is obvious there is a copy-paste error and the 2nd error message should be change excluded to signature.

https://github.com/cupy/c

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.

Wondering if this already exists? If not happy to create if valuable.

I'm looking for a mapping from the column names outputted, to the actual technical indicator it represents.

examples:
momentum_ao == "Momentum, Awesome Oscilator"
momentum_kama == "Momentum, Kaufman’s Adaptive Moving Average (KAMA)"

Can help quickly grasp what the features represent without having to refer back to do

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