NumPy

EDIT: The failure is due to update in Python 3.10 behaviour.

The following OpInfo tests fail locally (with Python 3.10) but pass on CI for gradient op

https://github.com/pytorch/pytorch/blob/97f29bda59deab8c063cf01f0a8ff4321b93c55e/torch/testing/_internal/common_methods_invocations.py#L8277-L8282

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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

Motivated by huggingface/transformers#12789 in Transformers, one welcoming change would be replacing assertions with proper exceptions. The only type of assertions we should keep are those used as sanity checks.

Currently, there is a total of 87 files with the assert statements (located under datasets and src/datasets), so when working on this, to manage the PR s

I'd like us to add some extra information to the dask docs around the block_info keyword argument for Dask array map_blocks. I think it should be added to this section here

Marvin explained something I found confusing here VolkerH/DaskFusion#1 (comment)

I believe this is undocumented behaviour.

import numba as nb


@nb.njit
def f1():
    for i in nb.prange(1):
        print(type(i))  # >>> int64


@nb.njit(parallel=True)
def f2():
    for i in nb.prange(1):
        print(type(i))  # >>> uint64


f1()
f2()

This caused a nasty bug in my own code that was hard to debug as the problem did not exist without `parallel=Tr

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

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.

hi,
if possible, please add these indicators as well:
TDI (Traders Dynamic Index)
chandelier exit
pivot points
BOP (balance of power)
CTM (Chande trend meter)
Coppock Curve
Correlation Coefficient
PMO (DecisionPoint Price Momentum Oscillator)
Ulcer Index
most of them except TDI are available on stockcharts.com
thanks

Description

While using tokenizers.create with the model and vocab file for a custom corpus, the code throws an error and is not able to generate the BERT vocab file

Error Message

ValueError: Mismatch vocabulary! All special tokens specified must be control tokens in the sentencepiece vocabulary.

To Reproduce

from gluonnlp.data import tokenizers
tokenizers.create('spm', model_p

Noting down a comment by @DanJonesOcean on Twitter: https://twitter.com/DanJonesOcean/status/1441392596362874882

In general, having more examples on each xarray page (like the one below) would be good. Then they would come up quickly in function searches:

http://xarray.pydata.org/en/stable/generated/xarray.Dataset.merge.html#xarray.Dataset.merge

Our API docs are generated by the func