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

What happened:

linspace gives a division by zero error, when NumPy returns an empty array on the same input.

What you expected to happen:

Behaviour the same as NumPy.

Minimal Complete Verifiable Example:

>>> import numpy as np
>>> np.linspace(0, 0, 0, endpoint=False)
array([], dtype=float64)
>>> import dask.array as da
>>> da.linspace(0, 0, 0, endpoint=F

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