🐛 Bug

I stumbled upon excessive CPU usage for my training code running on GPU. After some investigations I found the culprit.
It basically was

x = torch.eye(256).to('cuda') 

To Reproduce

This is quick and loads single CPU core.

%%timeit
    torch.eye(181)
6.43 µs ± 218 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

This is 3 times slowe

nditer.close (documentation) takes no arguments, the foreign function implements it also takes only the self parameter:

https://github.com/numpy/numpy/blob/8ada030bd5b37c764ae83449b46c93d743455b40/numpy/core/src/multiarray/nditer_pywrap.c#L2317-L2318

However, this foreign function is declared with the ml_flag `METH

it would be handy to have a passthrough parameter to dd.concat - currently the union_categoricals call is parameter-free:

    dtype = "category"
            if left is not None and right is not None:
                dtype = union_categoricals(
                    [left.astype("category").values, right.astype("category").values]
                ).dtype

This can be made more fle

This happens after a map operation when num_proc is set to >1. I tested this by cleaning up the json before running the map op on the dataset so it's unlikely it's coming from an earlier concatenation.

Example result:

"citation": "@ONLINE {wikidump,\n    author = {Wikimedia Foundation},\n    title  = {Wikimedia Downloads},\n    url    = {https://dumps.wikimedia.org}\n}\n\n@ONLINE 

(Noticed whilst reviewing #6695)

From the docs for numba.cuda.atomic.compare_and_swap:

class compare_and_swap(ary, old, val)

Conditionally assign val to the first element of an 1D array ary if the current value matches old.
Returns the current value as if it is loaded atomically.

It seem

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

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

Implements {DataFrame,Series}.empty

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